Evaluation and management of pulmonary nodules: state-of-the-art and future perspectives.

Review

Evaluation and management of pulmonary nodules: state-of-theart and future perspectives 1.

Introduction

2.

Etiology

3.

Detection of pulmonary

Mohamed Sayyouh, Dharshan R Vummidi & Ella A Kazerooni† University of Michigan Health System, Division of Cardiothoracic Radiology, Department of Radiology, Ann Arbor, MI, USA

nodules

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4.

Characterization of pulmonary nodules by computed tomography

5.

Magnetic resonance imaging

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Positron emission tomography

7.

Lung cancer screening

8.

Management of pulmonary nodules

9.

Conclusion

10.

Expert opinion

Introduction: The imaging evaluation of pulmonary nodules, often incidentally detected on imaging examinations performed for other clinical reasons, is a frequently encountered clinical circumstance. With advances in imaging modalities, both the detection and characterization of pulmonary nodules continue to evolve and improve. Areas covered: This article will review the imaging modalities used to detect and diagnose benign and malignant pulmonary nodules, with a focus on computed tomography (CT), which continues to be the mainstay for evaluation. The authors discuss recent advances in the lung nodule management, and an algorithm for the management of indeterminate pulmonary nodules. Expert opinion: There are set of criteria that define a benign nodule, the most important of which are the lack of temporal change for 2 years or more, and certain benign imaging criteria, including specific patterns of calcification or the presence of fat. Although some indeterminate pulmonary nodules are immediately actionable, generally those approaching 1 cm or larger in diameter, at which size the diagnostic accuracy of tools such as positron emission tomography (PET)/CT, single photon emission CT (SPECT) and biopsy techniques are sufficient to warrant their use. The majority of indeterminate pulmonary nodules are under 1 cm, for which serial CT examinations through at least 2 years for solid nodules and 3 years for ground-glass nodules, are used to demonstrate either benign biologic behavior or otherwise. The management of incidental pulmonary nodules involves a multidisciplinary approach in which radiology plays a pivotal role. Newer imaging and postprocessing techniques have made this a more accurate technique eliminating ambiguity and unnecessary follow-up. Keywords: characterization, detection, diagnosis, nodule, pulmonary, screening Expert Opin. Med. Diagn. (2013) 7(6):629-644

1.

Introduction

Evaluation of pulmonary nodules is a longstanding clinical problem. As we begin discussion of this topic, it is important to understand what we are discussing, and that begins with the definition of a pulmonary nodule. The most widely used definition of a pulmonary nodule comes from the multidisciplinary Fleischner Society glossary of terms, which defines a nodule as “a moderately well marginated rounded or oval opacity not > 30-mm in diameter that is completely surrounded by pulmonary parenchyma and is not associated with lymphadenopathy, atelectasis, pneumonia or pleural effusion” [1-3]. Lesions >30-mm in diameter are termed lung “masses” and are likely malignant [4]. Small pulmonary nodules, < 1-cm in diameter, became an increasing diagnostic problem with the advent of volumetric computed tomography (CT) imaging and lung cancer screening trials [5]. Small pulmonary nodules are found in 5 -- 60% of patients in published lung cancer CT screening 10.1517/17530059.2013.858117 © 2013 Informa UK, Ltd. ISSN 1753-0059, e-ISSN 1753-0067 All rights reserved: reproduction in whole or in part not permitted

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Article highlights. .

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Pulmonary nodules can be caused by a variety of benign and malignant diseases. The goal of the radiologic evaluation of pulmonary nodules is to detect and differentiate benign from malignant nodules as accurately as possible. Many imaging modalities and techniques have been developed for evaluation of pulmonary nodules, including radiography, CT MRI, PET/CT and SPECT, including advanced applications such as dual energy and computer-aided diagnosis techniques. The radiological criteria used to differentiate benign and malignant nodules are primarily attenuation and lack of temporal change, other features such as size, number, margin, location, content, cavitation and enhancement characteristics can be factored in to determine the probability of malignancy when serial comparisons are not available. Low-dose CT for lung screening reduces lung cancer and all-cause mortality in high risk current and former smokers. Biomarkers and mRNA are promising non-invasive methods that need further validation, but may add specificity to which indeterminate nodules are more likely to be malignant than others.

This box summarizes key points contained in the article.

studies [6]. This wide variation may be explained by different imaging methods, varying techniques, diverse geographic locations, interobserver variation and the evolution of CT technology over these studies [4]. Although the great majority of these indeterminate pulmonary nodules are benign, 1 in 13 men and 1 in 16 women will be diagnosed with lung cancer [7]. 2.

Etiology

Pulmonary nodules may be of many etiologies, including a variety of benign lesions such as granulomas, hamartomas and arteriovenous malformations, and malignant lesions such as bronchogenic carcinoma, metastases and lymphoma (see Table 1). This diversity of etiologies may also add to the confusion in the approach for evaluation of nodules once detected. 3.

Detection of pulmonary nodules

Chest radiography Detection of pulmonary nodules on chest radiographs is limited by low tissue contrast and overlapping structures [8]. Despite the lower sensitivity for nodule detection compared to CT, radiography can often reveal even very small pulmonary nodules particularly when calcified [9,10]. A systematic approach to interpreting chest radiographs coupled with the use of dual-energy and temporal subtraction radiography 3.1

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techniques, enhances the detection of subtle lung abnormalities on radiography [11-13]. Dual-energy radiography reduces anatomic noise from overlying bones, thereby improving the detection of pulmonary nodules [14,15]. Temporal subtraction technology enhances the ability to detect change in size over a certain period of time [13,16,17]. The quality of this technique depends upon the success of two-dimensional (2D) registration [18]. Computer-aided detection (CAD) technology can be used as a complementary tool in clinical practice as a second opinion [19]. Tomosynthesis Digital tomosynthesis is a new imaging modality aiming at improving detection of subtle pulmonary lesions in chest radiographs [20]. In this technique, the patient is positioned in front of a stationary detector, and the X-ray tube moves in a vertical path [20]. Images are then acquired rapidly during the course of tube movement, and a workstation is used to reconstruct the images [20]. In a study by Vikgren et al., incorporating tomosynthesis in chest examinations increased the sensitivity for detecting pulmonary nodules, especially when < 9-mm in diameter [21]. Compared to CT, tomosynthesis has limited depth resolution due to limited angle used and low radiation dose [21]. However, with lower cost and radiation dose, tomosynthesis may be a good alternative [21]. The role of this technology, in practice, is still a subject of research. 3.2

Computed tomography CT greatly enhances nodule detection over radiography [1]. The early Lung Cancer Action Project (ELCAP) showed that the detection of noncalcified pulmonary nodules was three times greater than with chest radiography [22]. In the early CT era of conventional slice-by-slice axial acquisition, lack of contiguity between slices increased the possibility of missing pulmonary nodules [23]. Subsequent spiral volumetric CT from single slice to multidetector, provides continuous data thin-slice volumetric acquisitions of the entire lungs with improved spatial and temporal resolution, resulting in more consistent exam quality and largely alleviating the earlier problems [23,24]. In clinical practice, chest CT parameters are set to maximize temporal and spatial resolution [4,25]. Thin sections images to the order of 1 -- 1.25-mm are useful, particularly when evaluating ground-glass or part solid nodules [26]. Factors that contribute to difficulty with nodule detection on CT include small nodule size, central or lower-lobe location, complex lung background and ground-glass attenuation [27]. Postprocessing techniques can improve pulmonary nodule detection [28-31]. Maximum intensity projections (MIPs) demonstrate only the voxels with maximum intensity, improving the delineation of nodules from vessels, and are increasingly used in clinical practice today [32]. Park et al., demonstrated that the pulmonary nodules detection sensitivity of 4 readers increased from 86 -- 91% to 91 -- 94% when using 5-mm MIPs reconstructed at 1-mm intervals [33]. The slice thickness and interval used for MIPs vary from 5 -- 8 3.3

Expert Opin. Med. Diagn. (2013) 7(6)

Evaluation and management of pulmonary nodules: state of the art and future perspectives

Table 1. Differential diagnosis of pulmonary nodules. Neoplastic Benign


Neoplastic Malignant

Infection

Inflammatory & Other

Vascular

Developmental

Hamartoma Chondroma Lipoma Fibroma Leiomyoma Carcinoid (benign) Primary: Bronchogenic carcinoma Non-small-cell carcinoma Small cell carcinoma Lymphoma Sarcoma Carcinoid (malignant) Secondary: Metastasis Lymphoma Granuloma Tuberculosis Atypical mycobacteria Fungal Bacterial Abscess Round pneumonia Septic emboli Sarcoidosis Amyloidosis Wegener’s granulomatosis and variants Rheumatoid arthritis Intrapulmonary lymph nodes Lipoid pneumonia Hematoma/contusion Hemangioma Arteriovenous malformations Pulmonary infarct Bronchogenic cyst Pulmonary sequestration Bronchial atresia

and 1 -- 5-mm respectively. Volume rendering (VR) requires every sample value to be mapped to an opacity or a color. In one study, VR performed significantly better than MIP for the detection of nodules < 11-mm in diameter and equivalent to MIP for larger nodules [30]. Computer-Aided detection A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules, segmentation of the detected nodules, and characterization of the nodules as benign or malignant. The segmentation of lungs is challenging due to inhomogeneities in the lung, pulmonary structures of similar densities such as arteries, veins, bronchi and bronchioles, different scanners and scanning protocol. There are several studies that have looked at different approaches to segmentation that include iterative approach, spatial edge detector, wavelet edge detector, thresholding and deformable models. Nodules show up as relatively low-contrast white circular objects within the lung fields rendering them relatively 3.4

indistinguishable from overlapping shadows, vessels and ribs. Nodule detection is a complicated task for the above mentioned reasons. Nodule detection on chest CT consists of two broad steps: i) identification of candidate nodules and ii) preservation of true positive and elimination of falsepositive nodules. The approaches for detection include comparison of geometric structures, rule-based scheme and linear discriminant analysis. Lung nodule segmentation refers to a task of delineating the spatial extent of focal nodular lesions appearing in chest CT scans. The advent of multidetector CT with advances in scanner technology has resulted in a change of nodule segmentation research from thresholding-based 2D methods to more sophisticated 3D/volumetric methods. Diagnosis of lung nodules is based on growth rate, shape and appearance and PET-CT features. Research has focused on the use of neural networks and linear discriminant analysis to differentiate benign from malignant nodules based on the above-mentioned features [34]. CAD may be used on both chest radiography and CT as a second reader with the goal of identifying lesions that might be missed by the reader [24,35,6]. This is particularly helpful in lung cancer screening or looking for metastases [37]. For chest radiography, CAD methods use primarily the posterior-anterior (PA) image, and more recently the lateral view. In a study by Shiraishi et al., the sensitivity for pulmonary nodule detection was 70.5% when applied to PA views only and increased to 86.9% when also applying CAD to the lateral view [38]. Many CT-based CAD systems have been developed with sensitivities ranging from 38 -- 100% [39-44]. Rubin et al., showed that CAD improved reader’s sensitivity for detection of pulmonary nodules from 50 to 76% [45]. CAD can also be used to characterize pulmonary nodules [46,47], such as nodule shape, size and volume, attenuation and enhancement characteristics [24,48]. CAD may also assist characterization of pulmonary lesions integrating CT and PET [46,47]. Using a semiautomatic computer-aided method with both CT and positron emission tomography (PET), Nie et al., found CAD to be more effective for characterization of pulmonary lesions than either of CT or PET alone [49]. A major challenge to the use of CAD for CT in clinical practice is the high rate of false-positive detections [24] which may result from artifact, branching points of vessels or central vessels [44]. Other challenges include a range of sensitivity and specificity for nodule detection that varies among CAD systems due to the diversity of algorithms, CT input and varying populations of nodules in which CAD has been studied [24]. Another challenge for existing CAD systems is the detection of ground-glass nodules [32]. For example, Beigelman-Aubry et al., demonstrated a sensitivity of CAD of 53% for ground-glass nodules compared to 73% for part solid and solid nodules [50]. Lastly, in a clinical operation, IT systems that allow all chest CT examinations in a practice to run through CAD for user review at their PACS workstation, are only beginning to appear in practice.


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MRI MRI has a limited role in detection of pulmonary nodules because of high susceptibility differences between air and pulmonary tissue, limited spatial resolution and presence of respiratory and cardiac motion which produce significant artifacts [24]. 3.5

4. Characterization of pulmonary nodules by computed tomography

Size The risk of malignancy is strongly related to nodule size [24]. Up to 80% of nodules > 20-mm are malignant, while over 90% of nodules < 20-mm are benign in the absence of an existing malignancy [51]. Of incidentally detected pulmonary nodules 5-mm or less in diameter in patients without a history of cancer, < 1% of them demonstrate malignant behavior over the subsequent 2 years [52]. Guidelines for the management of small pulmonary nodules detected on CT scans have been developed and are discussed later in this article (Figure 1). The most common method of measuring nodule size is the use of manually placed electronic calipers to measure the maximum cross-sectional diameter on the axial image on which the nodule appears the largest [32]. However, this 2-D CT measurement to detect size change is not reliable and is subject to inter- and intraobserver variation [32,35]. This is particularly challenging when evaluating smaller nodules, nodules that grow asymmetrically and due to variation in breath hold between examinations [53].


4.1

Volumetry Three-dimensional volumetric measurements are more accurate in assessing changes in size of pulmonary nodules than unidimensional measurements used since the 2000 Response Evaluation Criteria in Solid Tumors (RECIST) criteria which presumes that 1D measurements are a close enough approximation to 2D and even 3D measurement techniques [54-57]. Because nodule growth is a 3D process, volume evaluations are more sensitive than 1- or 2D measurements for identifying changes in nodule size, particularly for asymmetrically shaped or growth of nodules [58,59]. The concept is based on the principle that small increases in diameter produce a greater increase in volume according to the equation: 4/3 p (diameter/2)3 (volume of a sphere = 4/3 p (diameter/2)3 [35]. However, even 3D techniques may be inaccurate, particularly with used on nodules with a ground-glass component and nodules near or abutting bifurcating vessels [60,61]. Recently, 4D-CT using ventilator-gating under tidal volume ventilation has been applied [35]. This is particularly useful in patients who cannot cooperate with breath hold instructions [61]. However, until the widespread acceptance of a reliable and seamless volumetry software, 2D measurements are still the method of choice and are the fundamental underpinning of growth criteria for cancer, such as the RECIST criteria. 4.2.

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Growth rate The growth rate of pulmonary nodules is of vital importance in determining the likelihood of malignancy [58]. The growth rate of a lesion is determined by calculating the “doubling time”, which is the time that a lesion takes to double in volume [35]. Generally, benign lesions have a longer doubling time than malignant lesions [62]. Malignant nodules have doubling time between 30 and 400 days [63]. Nodules with doubling time < 30 days are likely to be infectious, although lymphoma and metastases may also show very rapid growth. Nodules with doubling time > 400 days are typically associated with benign etiologies [63]. The absence of growth in a lesion over a 2-year period has been accepted as a reliable sign of benignity [64]. However, some malignant lesions, especially spectrum of adenocarcinoma., formerly known as bronchoalveolar cell carcinoma, can grow very slowly [35]. El baz et al., proposed an alternative method for diagnosing malignant nodules where the 3D surfaces are delineated by spherical harmonical analysis. Preliminary results on 327 patients showed a sensitivity of 93.6% for differentiating benign from malignant nodules [65]. 4.3

Number Pulmonary nodules may be multiple or solitary. Multiple pulmonary nodules have a variety of causes such as metastases, septic emboli and pulmonary infarcts, sarcoidosis, amyloidosis, rheumatoid arthritis, arteriovenous malformations (AVMs) and vasculitides [32,66,67]. Clustering of multiple pulmonary nodules in one area of the lung may suggest benign etiology; however, the presence of a dominant nodule with smaller satellite lesions suggests lung cancer [52]. 4.4

Attenuation and internal characteristics Pulmonary nodules may be of varying radiographic density and CT attenuation, some of which can be used to confirm that a nodule is benign. When measuring internal characteristic of a nodule on CT, a circular or elliptical region of interest should be used covering the center of the lesion and avoiding its margin so as to avoid volume averaging and streak artifact if any, and not an individual pixel cursor [35]. Of the benign features, fat and specific patterns of calcification can be used to identify nodules as benign; other features do not have sufficient specificity. 4.5

Ground-glass versus solid nodules Noncalcified, nonfat attenuation pulmonary nodules may be classified as solid, ground-glass or mixed in attenuation. Solid nodules completely obscure the normal lung parenchyma within it [68]. Nonsolid or ground-glass nodules have a focal area of increased lung attenuation through which the lung parenchymal architecture is visible and undisturbed [68-70]. Part solid nodules are nodules containing both ground-glass and solid components [24,68]. The term “subsolid nodules” 4.6



Pulmonary nodule

Solid

Benign calcification

YES

Subsolid Fleischner Society guidelines (Table 4)

No further testing


NO Size of nodule

≥ 8 mm

4 – 8 mm

≤ 4 mm High risk

≤ 4 mm Low risk

Fleischner Society guidelines (Table 3) Cancer probability

High

Low

SUV < 2.5 or -ve biopsy Observe

PET/CT or biopsy SUV ≥ 2.5 or +ve biopsy

Surgery

Figure 1. Suggested algorithm for management of a pulmonary nodule.

refers to any nodule with a ground-glass component, whether exclusively or part solid nodules. Thin section CT technique is particularly important when characterizing and looking for interval change of subsolid nodules [71]. With the advances in MDCT and the use of low-dose CT for lung cancer screening programs the number of subsolid nodules detected have increased [69,72]. Subsolid nodules are now known to fall into the spectrum of peripheral lung adenocarcinomas, including premalignant atypical adenomatous hyperplasia (AAH), slow-growing adenocarcinomas, formerly known as bronchoalveolar carcinoma (BAC) and mixed subtype adenocarcinoma [68,73]. However, benign conditions such as focal inflammation, focal fibrosis and organizing pneumonia may also present as subsolid nodules [74-76]. In a study of 233 patients with pulmonary nodules by Henschke et al., the

prevalence of malignancy was 63% among part-solid nodules, 18% among ground-glass nodules and 7% among solid nodules [69]. The malignant part-solid nodules were typically slow growing adenocarcinomas or adenocarcinomas with bronchoalveolar features, whereas the solid nodules were typically other subtypes of adenocarcinoma [69]. While the overall number of part solid nodules detected with CT is considerably smaller than the number of solid nodules, the prevalence of malignancy within the subsolid nodules is higher. In slow-growing adenocarcinomas manifesting as ground-glass nodules, accelerated growth or development of new solid component is indicative of transition into invasive adenocarcinoma (Figure 2) [1]. The value of CT to differentiate between benign and malignant subsolid nodules is still controversial [69,75-77].


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B.


A.

Figure 2. A. A 41 30 mm left upper lobe mass with a spiculated outline (arrow heads). B. Intense FDG uptake on PET CT, resected specimen revealed moderately differentiated adenocarcinoma.

Lobulation of a ground-glass pulmonary nodule and round shape of a subsolid pulmonary nodule are associated with a greater frequency of malignancy [78]. In addition, spherical areas of air attenuation or “pseudocavitation” within a ground-glass nodule are highly suggestive of slow-growing adenocarcinoma (formerly BAC) although lymphoma and benign lesions such as organizing pneumonia can have similar air bronchograms [79]. Calcification There are four patterns of calcification that are considered benign with a high degree of specificity. These are diffuse, central, laminated and popcorn calcifications [80]. Other patterns of calcification may be seen with bronchogenic carcinoma, atypical and malignant carcinoid tumors and metastatic adenocarcinoma, particularly from the colon including stippled, eccentric and amorphous patterns of calcification [81,82]. These occur due to the production of calcium or osteoid by the tumor, infarction due to tumor growth or engulfment of a pre-existing granuloma [83]. Solidly calcified nodules in the setting of osteosarcoma may represent metastases containing osteoid matrix and should not be assumed to represent benign calcified granulomas. 4.7

Fat The most common fat-containing pulmonary nodule is a hamartoma, about half of which have detectable fat on CT (Figure 3) [80]. In one definition of fat in hamartomas, 8 voxels of --40 to --120 HU are needed to report fat with sufficient sensitivity and specificity [84]. Lipoma is rare benign cause of a fat-containing pulmonary nodule [69]. Malignant fatcontaining nodules are extremely rare in the lungs, and may occur with liposarcoma metastases to the lung [85]. In essence, 4.8

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if fat is identified in a lung nodule it should be considered to be benign. Cavitation Air lucency or cavitation can occur in both benign and malignant pulmonary nodules (Figure 4) [35]. Although an irregular thick wall of >15-mm favors malignancy [86], cavity wall thickness cannot be reliably used to differentiate benign from malignant nodules [87]. Examples of benign lesions that may cavitate include an abscess, Wegener granulomatosis (Figure 5) and rheumatoid nodules, while malignant cavitary nodules include primary and metastatic squamous cell carcinoma, lymphoma and sarcoma [88]. Slow-growing adenocarcinomas sometimes show internal bubbly or frothy lucencies due to growth of the tumor around patent bronchi (Figure 2) [89]. 4.9

Enhancement Nodules 7 -- 30-mm in diameter are amenable to CT enhancement studies, in which sequential images or image stacks are taken at baseline, during and after contrast administration, Malignant pulmonary nodules having greater enhancement and slow washout relative to benign nodules, a similar principle as SPECT and PET scans [4,51,90-92]. Using an enhancement threshold of > 15 HU has 98% sensitivity, 58% specificity and 77% accuracy for malignancy [86]. However, the negative predictive value of this threshold is 98%, which means that absence of enhancement > 15 HU is strongly predictive of benignity [93,94]. This technique cannot be used with calcified nodules or lesions measuring more than 30-mm due to the higher chances of necrosis, or with nodules under 7-mm due to the difficulty maintaining a consistent and large enough region of interest without partial volume artifact [90]. 4.10




A.

B.

Figure 3. A. A non calcified 5 mm pulmonary nodule in the lingula (arrow). B. Volume rendered image of the same nodule with a volume of 67 mm3 and benign morphology, stable > 2 years, and therefore likely benign.

Contour and margin, including the halo sign and reverse halo sign 4.11

In general, nodule shape and margin characteristics are neither sensitive nor specific enough when evaluating individual nodules for malignancy. A polygonal shape is highly specific for benignity [95] and most likely represents an intrapulmonary lymph node, for subpleural nodules [78]. Irregular or spiculated margins, due to localized extension of malignant cells, are highly suspicious for bronchogenic carcinoma, but can also be seen with infection and focal fibrosis (Figure 6) [89,96,97]. Lobulated margins, which may be due to differential growth within a nodule, can occur in primary or secondary lung neoplasms as well as with some benign lesions such as hamartomas or granulomas [89]. A smooth margin is of little diagnostic value as it could occur with many benign and malignant lesions [89]. The CT Halo sign is defined as a nodule with “ill-defined rim of ground-glass attenuation” and correlates pathologically with perinodular hemorrhage, tumor infiltration or inflammation. The most common cause is infection, especially invasive aspergillosis [24]. It may also be seen with slowgrowing adenocarcinomas (formerly known as BAC) [70]. The reverse halo sign is when the nodule center is ground glass in attenuation and periphery is a solid soft-tissue component, as described with organizing pneumonia, and less commonly other infectious and inflammatory etiologies [32]. Location Malignant nodules of bronchogenic carcinoma are twice as common in the upper lobes than the lower lobes, while benign nodules are more randomly distributed, and hematogenously disseminated metastases and infections, such as military tuberculosis, are lower lobe predominant [85,99,100]. 4.12

In patients with idiopathic pulmonary fibrosis, or other forms of fibrotic lung disease, bronchogenic carcinoma usually develops within areas of fibrosis along the lung periphery [101]. Perifissural nodules (PFNs) are usually intrapulmonary lymph nodes, several imaging characteristics should suggest these and thereby reduce unnecessary follow-up CT scans. De Hoop et al., defined the typical perifissural nodule as a homogenous, solid nodule attached to a fissure with smooth margins and an oval, lentiform or triangular shape [102]. They found that nodules meeting this definition of PFN represented almost 20% of all detected nodules on CT examinations in 2994 individuals, that none of these nodules turned out to be malignant in 5.5 years of follow-up, and felt that these nodules are intrapulmonary lymph nodes. Additional evidence in support of PFNs from the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) screening trial showed that smoothly marginated nodules attached to fissures or pleura do not turn malignant, with many intrapulmonary lymph nodes meeting these criteria [103]. Dual energy CT Adjusting the kVp to maximize contrast has often been used in diagnostic radiography and CT. In an early experiment, Bhalla et al. demonstrated that at 80 kVp the average attenuation measurements of all soft-tissue densities and water decrease compared to 140 kVp, while those of calcium solution and bones increase [104]. Currently, this emerging technique uses dual source and more recently kVp-switching single source CT technology [24,105]. Simultaneous 80 kV and 140 kV CT images are obtained and the varying behavior of different tissues when exposed to the two different X-ray spectra enables identification of fat, bone, air and soft tissue [105-107]. Also, material-specific images; including “iodine-enhanced image” can be created [108]. For pulmonary 4.13


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A.

B.

C.

D.

30.75 HU, 17.3sd 0.0621 cm^2

E.

Figure 4. A -- D. Hamartoma. A. Well-defined 9 mm nodule in the left upper lobe. B. The nodule shows heterogenous attenuation with foci of low density secondary to macroscopic fat. C. Allowing for volume averaging, region of interest measurement demonstrating the presence of intralesional fat likely representing a hamartoma. D. 18 15 mm lingular nodule with coarse (popcorn) calcifications associated with hamartomas. E. Calcified granuloma; a 17 12 mm left lower-lobe nodule with homogenous near complete calcification.

nodules, these techniques provide information about the degree of enhancement and presence of nonvisible calcifications without increasing the radiation dose [109,110]. Chae et al., in a series of 49 nodules ranging from 5- to 70-mm demonstrated that CT number of nodules on 3-min delayed iodine-enhanced images is more sensitive and specific than the degree of enhancement with a sensitivity of 92% and a specificity of 70% for malignancy [109]. 4.14 CT-guided percutaneous aspiration and/or biopsy

Tissue sampling can be performed for nodules suspicious for malignancy or infection, generally 8 -- 10-mm or larger [24]. In general, core biopsy sampling is preferred to fine needle 636

aspiration operationally and for accuracy. Nodules that are ideal for percutaneous biopsy can be reached without crossing a major vessel or pleural fissure [111,112]. Li et al., in a series of CT-guided needle biopsies of 169 patients with pulmonary nodules < 2-cm demonstrated a diagnostic accuracy of 93.5%, sensitivity for malignant lesions of 90.4% and specificity for benign lesions of 100%. Positive and negative predictive values were 100 and 83.3%, respectively [113]. The most common complications associated with this procedure were pneumothorax and hemorrhage [113], Biopsy should only be performed if the results will alter clinical decision making, For example, in a 65-year-old male with a 50 pack year history of smoking and a solitary spiculated 2.5-cm upper-lobe lung nodules with no evidence of metastases on



A.

B.

A: 16.8 mm


A: 9.9 mm

Figure 5. Cavitary pulmonary masses. A. A 37 30 mm right upper-lobe cavitary mass with a thick (> 16 mm) spiculated wall. Transbronchial biopsy revealed moderately differentiated squamous cell carcinoma. B. A 42 23 mm right lower lobe cavitary (wall thickness < 16 mm) lung mass. Core needle biopsy revealed necrotizing noninfectious granulomatosis consistent with Wegener granulomatosis.

A.

C.

B.

Figure 6. A. Part solid 24 22 mm pulmonary nodule with ground-glass (arrow heads) and solid components (arrow) demonstrating central cystic lucencies (pseudocavitation). B. More conspicuous solid component in 18 months with a parenchymal band extending to the pleura is concerning for malignancy. C. At 24 months from the initial scan the solid component has further increased in size suspicious for a primary bronchogenic carcinoma. Histopathology of the resected specimen revealed well differentiated adenocarcinoma with bronchoalveolar features.

PET scan who is a surgical candidate, the probability of malignancy approaches 100%. A positive CT biopsy result would not alter the decision of curative resection, and a negative CT biopsy result would be treated as a false negative and not alter that treatment decision. 5.

Magnetic resonance imaging

Despite a limited role, fast acquisition sequences with high temporal resolution provide an opportunity for magnetic

resonance imaging (MRI) to be used for nodule characterization [24]. Most neoplastic lesions show high signal intensity relative to surrounding lung tissue on T2-weighted images [24]. Studies have suggested that a heavily T2-weighted sequence such as half Fourier acquisition single-shot echo (HASTE) is the sequence of choice for imaging of the lungs, with sensitivity of 94.9 -- 95.7% for nodules between 5 and 10-mm in diameter [114,115]. Others have suggested using a breath-hold turbo spin echo (TSE) sequence for detecting pulmonary metastases [116]. Because the significance of very small nodules


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Table 2. Likelihood ratios of malignancy [83]. Feature

Size (mm)

Lobar site Contour


Calcification

Cavity wall thickness

CT enhancement PET SUV Age

Smoking

Finding 0 -- 10 11 -- 20 21 -- 30 > 30 Upper/middle Smooth Spiculated Benign calcifications Indeterminate calcifications £ 4 mm 4 -- 16 mm > 16 mm £ 15 HU > 15 HU £ 2.5 > 2.5 20 -- 29 30 -- 39 50 -- 70 > 70 Never smoked Current smoker

History of malignancy

7. Likelihood Ratio 0.52 0.74 3.67 5.23 1.22 0.30 5.54 0.01 2.20 0.07 0.72 37.97 0.04 2.32 0.04 4.30 0.05 0.24 1.90 4.16 0.19 2.27 4.95

detected by CT in low-risk patients is doubtful, MR could be useful in younger patients without risk factors, to follow-up a known lesion measuring > 5-mm without the need for ionizing radiation [24]. Dynamic contrast enhanced MRI can be also used in evaluation of pulmonary nodules [35]. Enhancement curves of malignant pulmonary nodules show higher maximum peak, faster slope and significant contrast wash-out when compared to benign lesions, similar to CT enhancement studies [117].

6.

Low-dose CT Lung cancer is the leading cause of cancer-related death in the United States [121]. Although many studies have been tested to screen for lung cancer, including chest radiography, sputum analysis, and low-dose CT, only the latter has been shown to reduce lung cancer specific mortality in high-risk smokers. In three studies supported by the National Cancer Institute, annual chest radiography, with and without sputum cytology, failed to prove benefit as screening tools for lung cancer [122-126]. According to the landmark National Lung Screening Trial (NLST), screening with low dose CT results in 20% reduction in mortality from lung cancer in high risk smokers aged 55 -- 74 years [121,127]. However, the high prevalence of false-positive scan results, downstream non invasive and invasive testing, exposure to radiation, and cost remain concerns [122]. 7.1

Biomarkers and micro-RNA Extensive research into sputum, blood and urine biomarkers that may add specificity to low dose CT results or impact the subsequent management of small screen or incidentally detected nodules is promising, but remains in the preclinical domain [128]. Of the biomarkers studied, carcinoembryonic antigen, cytokeratin and neuron-specific enolase, have shown no significant value due to their low sensitivity [129]. Recently, matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has been used to investigate protein expression profiles in health and disease [129]. In a recent study by Pecot et al., serum proteomic biomarkers proved to have added diagnostic value to clinical and radiological data in the noninvasive evaluation of indeterminate pulmonary nodules [128]. Micro--RNA (mRNA) provides promising biomarkers for diagnosis of lung cancer. Further validation in clinical trials is needed before being used in laboratory settings [130]. 7.2

Positron emission tomography

The combination of PET/CT has added significantly to the characterization and management of pulmonary nodules, with sensitivity and specificity of 96 and 88%, respectively, for malignancy [118,119]. A standardized uptake value (SUV) of 2.5 is commonly used as a threshold for malignancy [35]. The sensitivity of PET for the evaluation of small nodules generally 7-mm and less in diameter is limited by spatial resolution [120]. Some lung cancers, specifically the adenocarcinomas formerly classified as BACs, may have a false negative CT even when several centimeters in size, while some infections and inflammatory conditions can result in a false positive CT. Although a positive CT generally pushes management towards tissue sampling, a negative PET/CT is not license to ignore a nodule completely, but to do follow-up low radiation dose chest CT imaging to look for interval growth. 638

Lung cancer screening

8.

Management of pulmonary nodules

8.1

Broad guidelines

[1]

1) Careful observation and follow-up -- low probability of malignancy or higher probability but confounding medical circumstances. 2) Additional diagnostic work up (PET, CT-guided biopsy, bronchoscopy) -- moderate probability of malignancy, or high probability but confounding medical circumstances. 3) Surgery -- high probability. 8.2 Estimating lung cancer probability 8.2.1 “Pretest” probability of malignancy This is the probability of malignancy based on clinical characteristics and radiological features before performing other



Table 3. Fleischner Society guidelines for management of small solid Pulmonary Nodules [131]. Nodule size (mm) £4

No follow-up

> 4 -- 6

CT at 12 months; if unchanged, no further follow-up CT at 6 -- 12 months; if unchanged, CT at 18 -- 24 months Consider immediate workup with contrast-enhanced CT or PET or biopsy or follow-up with CT at 3,9 and 24 months

> 6 -- 8 ‡8


Patient at low risk

Patient at high risk

CT at 12 months; if unchanged, no further follow-up CT at 6 -- 12 months; if unchanged, CT at 18 -- 24 months CT at 3 -- 6 months; if unchanged, CT at 9 -- 12 and 24 months Consider immediate workup with contrast-enhanced CT or PET or biopsy or follow-up with CT at 3,9 and 24 months

Table 4. Fleischner Society Recommendations for the management of subsolid pulmonary nodules detected at CT [130]. Nodule type Solitary pure GGNs £ 5 mm > 5 mm

Solitary part-solid nodule

Multiple pure GGNs £ 5 mm > 5 mm without a dominant lesion(s) Dominant nodule(s) with part-solid or solid component

Management recommendations

Additional remarks

No CT follow-up required Initial follow-up at 3 months to confirm persistence then annual surveillance CT for a minimum of 3 years Initial follow-up CT at 3 months to confirm persistence. If persistent and solid component < 5 mm, then yearly surveillance CT for a minimum of 3 years. If persistent and solid component ‡ 5 mm, then biopsy or surgical resection Obtain follow-up CT at 2 and 4 years Initial follow-up CT at 3 months to confirm persistence and then annual surveillance CT for a minimum of three years Initial follow-up CT at 3 months to confirm persistence. If persistent, biopsy or surgical resection is recommended, especially for lesions with > 5 mm solid component

Obtain contiguous 1 mm thick sections to confirm that nodule is truly a pure GGN FDGPET is of limited value, potentially misleading, and therefore not recommended Consider PET/CT for part-solid nodules > 10 mm

Consider alternate causes for multiple GGNs £ 5 mm FDG PET is of limited value, potentially misleading, and therefore is not recommended Consider lung-sparing surgery for patients with dominant lesion(s) suspicious for lung cancer

GGN: Ground glass nodule

tests [1]. Clinical risk factors include age > 60 years, prior cancer history, cigarette smoking, chronic obstructive lung disease and asbestos exposure. The two main radiological risk factors are size > 20-mm and spiculated outline. Physician’s clinical judgment remains the cornerstone in estimation of pretest probability of malignancy [1]. The website chestxray. com has a calculator for the probability of malignancy in a lung nodule that is based on variables such as age, smoking history and imaging features of the nodule [97]. Likelihood ratio When evaluating the risk that pulmonary nodule is malignant on imaging, it is useful to think of the clinical and imaging 8.2.2

features in terms of likelihood ratios (LRs). It describes the probability of a lesion being malignant based on a specific feature. LRs greater than one favor malignancy and close to zero favor a benign etiology (Table 2) [93,131,132]. Small indeterminate solid pulmonary nodules Guidelines for management of small solid pulmonary nodules have been developed by the Fleischner Society. These guidelines are only applicable incidentally in patients who are 35 years or older with small solid pulmonary nodules. High-risk patients primarily include smokers [52]. Additional risk factors are family history of lung cancer and exposure to asbestos, radon and uranium. Nodules < 4-mm are likely 8.2.3


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M. Sayyouh et al.

benign and require a 1-year follow-up scan only in smokers. Nodules measuring 4- to 8-mm need follow-up to 2 years to document interval stability with more follow-up frequency in smokers. Larger nodules may need further work up (Table 3) [52]. Subsolid pulmonary nodules (ground-glass and part-solid nodules)


8.2.4

Management of subsolid pulmonary nodules is controversial [68] and is the subject of the recent Fleischner Society Guideline shown in Table 4 [133]. In general the follow-up duration for nodules with any ground-glass component extends to at least three years to document lack of change and benign biologic behavior, a full year longer than for solid nodules. These guidelines assume meticulous evaluation, optimally with contiguous thin sections (1-mm) reconstructed with narrow and/or mediastinal windows to evaluate the solid component and wide and/or lung windows to evaluate the nonsolid component of nodules, if indicated. When electronic calipers are used, bidimensional measurements of both the solid and ground-glass component of lesions should be obtained as necessary. The use of a consistent low-dose technique is recommended, especially in cases for which prolonged follow-up is recommended, particularly in younger patients. With serial scans, always compare with the original baseline study to detect subtle indolent growth. 9.

Conclusion

The diagnosis and characterization of pulmonary nodules remains a common clinical management issue. An approach incorporating radiological characteristics, temporal change in size and metabolic signature on PET-CT help in arriving at a diagnosis in the appropriate clinical setting. 10.

approach that frequently includes PET-CT, surgery and/or biopsy [51]. There are a dizzying array of choices to image nodules < 2 cm, but the cornerstone remains radiography (+/-- dual energy subtraction) with multidetector CT. Radiological stability over 2 years implying benignity is still widely employed in management with a few exceptions surrounding groundglass nodules [64]. The just published recommendations by Naidich et al., are a welcome addition to address the ambiguity that existed with management of ground-glass nodules or subsolid nodules [133]. Although 1D and 2D measurements still prevail, volumetry and CAD are gaining acceptance as a supplementary tool due to their improved accuracy and reproducibility [58,59]. Widespread use will hinge on their seamless integration onto existing PACS systems. Newer techniques such as proteomic signatures are still experimental and can only complement existing validated techniques [128]. Lung cancer screening is most likely here to stay pending cost effectiveness analysis; there are a large number of exsmokers who are now potentially eligible for screening. The emphasis is to perform CT with even lower doses (ultralow dose CT) and interpret them as per guidelines to obviate unnecessary follow-up. Accurate and reproducible post processing again takes precedence with techniques such as volumetry. The challenge may lie more in the logistics of setting up a multidisciplinary lung cancer screening program and running it efficiently in the current climate of rising costs. The management of incidental pulmonary nodules involves a multidisciplinary approach in which radiology plays a pivotal role. Newer imaging and post processing techniques have made this a more accurate technique eliminating ambiguity and unnecessary follow-up.

Expert opinion Declaration of interest

Incidental pulmonary nodules are a frequently encountered clinical conundrum. Nodules > 2-cm have a higher risk of malignancy necessitating a more aggressive multidisciplinary

640

The authors state no conflict of interest and have received no payment in preparation of this manuscript.



Bibliography Papers of special note have been highlighted as either of interest () or of considerable interest () to readers. 1.

Ost DE, Gould MK. Decision making in patients with pulmonary nodules. Am J Respir Crit Care Med 2012;185:363-72

2.

Ost D, Fein AM, Feinsilver SH. Clinical practice. The solitary pulmonary nodule. N Engl J Med 2003;348:2535-42 This article provides an introduction to the issue with some important definitions.


.

3.

4.

5.

6.

Gould MK, Fletcher J, Iannettoni MD, et al. Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition). Chest 2007;132:108S-30S Hodnett PA, Ko JP. Evaluation and management of indeterminate pulmonary nodules. Radiol Clin North Am 2012;50:895-914 Benjamin MS, Drucker EA, McLoud TC, Shepard JA. Small pulmonary nodules: detection at chest CT and outcome. Radiology 2003;226:489-93 McWilliams A, Mayo J. Computed tomography-detected noncalcified pulmonary nodules: a review of evidence for significance and management. Proc Am Thorac Soc 2008;5:900-4

7.

Truong MT, Sabloff BS, Ko JP. Multidetector CT of solitary pulmonary nodules. Thorac Surg Clin 2010;20:9-23

8.

Shah PK, Austin JH, White CS, et al. Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. Radiology 2003;226:235-41 Emphasises the importance of reviewing areas of the chest radiograph prone to missed nodules.

.

9.

10.

11.

12.

Abe H, Macmahon H, Shiraishi J, et al. Computer-aided diagnosis in chest radiology. Semin Ultrasound CT MR 2004;25:432-7

13.

McAdams HP, Samei E, Dobbins J III, et al. Recent advances in chest radiography. Radiology 2006;241:663-83 Summarises the technological advances and their applications with emphasis on tomosynthesis and dual energy/ temporal subtraction.

.

14.

15.

16.

17.

18.

19.

Ricke J, Fischbach F, Freund T, et al. Clinical results of CsI-detector-based dual-exposure dual energy in chest radiography. Eur Radiol 2003;13:2577-82 Tagashira H, Arakawa K, Yoshimoto M, et al. Detectability of lung nodules using flat panel detector with dual energy subtraction by two shot method: evaluation by ROC method. Eur J Radiol 2007;64:279-84 Johkoh T, Kozuka T, Tomiyama N, et al. Temporal subtraction for detection of solitary pulmonary nodules on chest radiographs: evaluation of a commercially available computer-aided diagnosis system. Radiology 2002;223:806-11 Kakeda S, Kamada K, Hatakeyama Y, et al. Effect of temporal subtraction technique on interpretation time and diagnostic accuracy of chest radiography. AJR Am J Roentgenol 2006;187:1253-9 Ishida T, Ashizawa K, Engelmann R, et al. Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching. J Digit Imaging 1999;12:77-86 MacMahon H, Engelmann R, Behlen FM, et al. Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. Radiology 1999;213:723-6

Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999;354:99-105

20.

Ketai L, Malby M, Jordan K, et al. Small nodules detected on chest radiography: does size predict calcification? Chest 2000;118:610-14

Dobbins JT III, McAdams HP. Chest tomosynthesis: technical principles and clinical update. Eur J Radiol 2009;72:244-51

21.

Vikgren J, Zachrisson S, Svalkvist A, et al. Comparison of chest tomosynthesis and chest radiography for detection of pulmonary nodules: human observer

MacMahon H. Improvement in detection of pulmonary nodules: digital image processing and computer-aided

study of clinical cases. Radiology 2008;249:1034-41

diagnosis. Radiographics 2000;20:1169-77


22.

Henschke CI, McCauley DI, Yankelevitz DF, et al. Early lung cancer action project: a summary of the findings on baseline screening. Oncologist 2001;6:147-52

23.

Friese SA, Rieber A, Fleiter T, et al. Pulmonary nodules in spiral volumetric and single slice computed tomography. Eur J Radiol 1994;18:48-51

24.

Girvin F, Ko JP. Pulmonary nodules: detection, assessment, and CAD. AJR Am J Roentgenol 2008;191:1057-69 Current review on the diagnosis of pulmonary nodules.

..

25.

Moses DA, Ko JP. Multidetector CT of the solitary pulmonary nodule. Semin Roentgenol 2005;40:109-25

26.

Ahn MI, Gleeson TG, Chan IH, et al. Perifissural nodules seen at CT screening for lung cancer. Radiology 2010;254:949-56

27.

Gurney JW. Missed lung cancer at CT: imaging findings in nine patients. Radiology 1996;199:117-22

28.

Gruden JF, Ouanounou S, Tigges S, et al. Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT. AJR Am J Roentgenol 2002;179:149-57

29.

Valencia R, Denecke T, Lehmkuhl L, et al. Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices. Eur Radiol 2006;16:325-32

30.

Peloschek P, Sailer J, Weber M, et al. Pulmonary nodules: sensitivity of maximum intensity projection versus that of volume rendering of 3D multidetector CT data. Radiology 2007;243:561-9 Reiterates the importance of postprocessing in the diagnosis of lung nodules.

.

31.

Tillich M, Kammerhuber F, Reittner P, et al. Detection of pulmonary nodules with helical CT: comparison of cine and film-based viewing. AJR Am J Roentgenol 1997;169:1611-14

32.

Brandman S, Ko JP. Pulmonary nodule detection, characterization, and management with multidetector

641

M. Sayyouh et al.

on CT images with morphologic matching algorithm--preliminary results. Radiology 2005;236:286-93

computed tomography. J Thorac Imaging 2011;26:90-105 33.


34.

.

Park EA, Goo JM, Lee JW, et al. Efficacy of computer-aided detection system and thin-slab maximum intensity projection technique in the detection of pulmonary nodules in patients with resected metastases. Invest Radiol 2009;44:105-13 El-Baz A, Beache GM, Gimel’farb G, et al. Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging 2013;2013:942353 Comprehensive review on the various types of CAD.

35.

Jones YM, Irion KL, Holemans JA. A review of the imaging and clinical management of solitary pulmonary nodules. Imaging 2008;20:303-11

36.

de Hoop B, De Boo DW, Gietema HA, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology 2010;257:532-40

37.

38.

39.

40.

41.

42.

43.

44.

642

45.

46.

47.

48.

Koo CW, Anand V, Girvin F, et al. Improved efficiency of CT interpretation using an automated lung nodule matching program. AJR Am J Roentgenol 2012;199:91-5

49.

Shiraishi J, Li F, Doi K. Computer-aided diagnosis for improved detection of lung nodules by use of posterior-anterior and lateral chest radiographs. Acad Radiol 2007;14:28-37

50.

Tao C, Gierada DS, Zhu F, et al. Automated matching of pulmonary nodules: evaluation in serial screening chest CT. AJR Am J Roentgenol 2009;192:624-8 Reeves AP, Kostis WJ. Computer-aided diagnosis for lung cancer. Radiol Clin North Am 2000;38:497-509 Giger ML, Bae KT, MacMahon H. Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol 1994;29:459-65 Armato SG III, Giger ML, Moran CJ, et al. Computerized detection of pulmonary nodules on CT scans. Radiographics 1999;19:1303-11 Brown MS, Goldin JG, Suh RD, et al. Lung micronodules: automated method for detection at thin-section CT--initial experience. Radiology 2003;226:256-62

51.

52.

..

53.

Rubin GD, Lyo JK, Paik DS, et al. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 2005;234:274-83 Awai K, Murao K, Ozawa A, et al. Pulmonary nodules: estimation of malignancy at thin-section helical CT--effect of computer-aided diagnosis on performance of radiologists. Radiology 2006;239:276-84 Li F, Aoyama M, Shiraishi J, et al. Radiologists’ performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. AJR Am J Roentgenol 2004;183:1209-15 McNitt-Gray MF, Hart EM, Wyckoff N, et al. A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys 1999;26:880-8 Nie Y, Li Q, Li F, et al. Integrating PET and CT information to improve diagnostic accuracy for lung nodules: a semiautomatic computer-aided method. J Nucl Med 2006;47:1075-80 Beigelman-Aubry C, Hill C, Boulanger X, et al. Evaluation of a computer aided detection system for lung nodules with ground glass opacity component on multidetector-row CT. J Radiol 2009;90:1843-9 Swensen SJ, Jett JR, Hartman TE, et al. CT screening for lung cancer: five-year prospective experience. Radiology 2005;235:259-65 MacMahon H, Austin JH, Gamsu G, et al. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 2005;237:395-400 Seminal statement, widely used in the management of incidentally detected pulmonary nodules. Bogot NR, Kazerooni EA, Kelly AM, et al. Interobserver and intraobserver variability in the assessment of pulmonary nodule size on CT using film and computer display methods. Acad Radiol 2005;12:948-56

Bae KT, Kim JS, Na YH, et al. Pulmonary nodules: automated detection


54.

Erasmus JJ, Gladish GW, Broemeling L, et al. Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response. J Clin Oncol 2003;21:2574-82

55.

Revel MP, Bissery A, Bienvenu M, et al. Are two-dimensional CT measurements of small noncalcified pulmonary nodules reliable? Radiology 2004;231:453-8

56.

Schwartz LH, Ginsberg MS, DeCorato D, et al. Evaluation of tumor measurements in oncology: use of filmbased and electronic techniques. J Clin Oncol 2000;18:2179-84

57.

Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228-47

58.

Yankelevitz DF, Reeves AP, Kostis WJ, et al. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000;217:251-6

59.

Revel MP, Lefort C, Bissery A, et al. Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 2004;231:459-66

60.

Goodman LR, Gulsun M, Washington L, et al. Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR Am J Roentgenol 2006;186:989-94

61.

Boll DT, Gilkeson RC, Fleiter TR, et al. Volumetric assessment of pulmonary nodules with ECG-gated MDCT. AJR Am J Roentgenol 2004;183:1217-23

62.

Revel MP, Merlin A, Peyrard S, et al. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol 2006;187:135-42

63.

Lillington GA, Caskey CI. Evaluation and management of solitary and multiple pulmonary nodules. Clin Chest Med 1993;14:111-19

64.

Yankelevitz DF, Henschke CI. Does 2year stability imply that pulmonary nodules are benign? AJR Am J Roentgenol 1997;168:325-8

65.

El-Baz A, Gimel’farb G, Falk R, Abo El-Ghar M. Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer. Pattern Recognit 2009;42:1041-51


66.

67.


68.

69.

70.

71.

.

72.

73.

74.

75.

76.

Cordier JF, Valeyre D, Guillevin L, et al. Pulmonary Wegener’s granulomatosis. A clinical and imaging study of 77 cases. Chest 1990;97:906-12

glass opacity at thin-section CT: histopathologic comparisons. Radiology 2007;245:267-75 77.

Ginsberg MS, Griff SK, Go BD, et al. Pulmonary nodules resected at videoassisted thoracoscopic surgery: etiology in 426 patients. Radiology 1999;213:277-82 Godoy MC, Naidich DP. Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: recommended interim guidelines for assessment and management. Radiology 2009;253:606-22 Henschke CI, Yankelevitz DF, Mirtcheva R, et al. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol 2002;178:1053-7 Lee YR, Choi YW, Lee KJ, et al. CT halo sign: the spectrum of pulmonary diseases. Br J Radiol 2005;78:862-5 Travis WD, Brambilla E, Noguchi M, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society: international multidisciplinary classification of lung adenocarcinoma: executive summary. Proc Am Thorac Soc 2011;8:381-5 Revised classification of lung adenocarcinoma with important changes in the terminology. Shiau MC, Bonavita J, Naidich DP. Adenocarcinoma of the lung: current concepts in radiologic diagnosis and management. Curr Opin Pulm Med 2007;13:261-6 Travis WD, Garg K, Franklin WA, et al. Evolving concepts in the pathology and computed tomography imaging of lung adenocarcinoma and bronchioloalveolar carcinoma. J Clin Oncol 2005;23:3279-87 Gandara DR, Aberle D, Lau D, et al. Radiographic imaging of bronchioloalveolar carcinoma: screening, patterns of presentation and response assessment. J Thorac Oncol 2006;1:S20-6 Lee HJ, Goo JM, Lee CH, et al. Predictive CT findings of malignancy in ground-glass nodules on thin-section chest CT: the effects on radiologist performance. Eur Radiol 2009;19:552-60 Kim HY, Shim YM, Lee KS, et al. Persistent pulmonary nodular ground-

78.

90.

Swensen SJ, Viggiano RW, Midthun DE, et al. Lung nodule enhancement at CT: multicenter study. Radiology 2000;214:73-80

91.

Swensen SJ, Morin RL, Schueler BA, et al. Solitary pulmonary nodule: CT evaluation of enhancement with iodinated contrast material--a preliminary report. Radiology 1992;182:343-7

92.

Swensen SJ, Jett JR, Hartman TE, et al. Lung cancer screening with CT: mayo Clinic experience. Radiology 2003;226:756-61

93.

Black WC, Armstrong P. Communicating the significance of radiologic test results: the likelihood ratio. AJR Am J Roentgenol 1986;147:1313-18

94.

Tateishi U, Tsukagoshi S, Inokawa H, et al. Fluctuation in measurements of pulmonary nodule under tidal volume ventilation on four-dimensional computed tomography: preliminary results. Eur Radiol 2008;18:2132-9

95.

Maile CW, Rodan BA, Godwin JD, et al. Calcification in pulmonary metastases. Br J Radiol 1982;55:108-13

Edey AJ, Hansell DM. Incidentally detected small pulmonary nodules on CT. Clin Radiol 2009;64:872-84

96.

Pugatch RD. Radiologic evaluation in chest malignancies. A review of imaging modalities. Chest 1995;107:294S-7S

Siegelman SS, Khouri NF, Leo FP, et al. Solitary pulmonary nodules: CT assessment. Radiology 1986;160:307-12

97.

Swensen SJ, Silverstein MD, Ilstrup DM, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med 1997;157:849-55

Takashima S, Sone S, Li F, et al. Indeterminate solitary pulmonary nodules revealed at population-based CT screening of the lung: using first followup diagnostic CT to differentiate benign and malignant lesions. AJR Am J Roentgenol 2003;180:1255-63 Li F, Sone S, Abe H, et al. Malignant versus benign nodules at CT screening for lung cancer: comparison of thinsection CT findings. Radiology 2004;233:793-8

79.

Oda S, Awai K, Liu D, et al. Ground-glass opacities on thin-section helical CT: differentiation between bronchioloalveolar carcinoma and atypical adenomatous hyperplasia. AJR Am J Roentgenol 2008;190:1363-8

80.

Siegelman SS, Khouri NF, Scott WW Jr, et al. Pulmonary hamartoma: CT findings. Radiology 1986;160:313-17

81.

Magid D, Siegelman SS, Eggleston JC, et al. Pulmonary carcinoid tumors: CT assessment. J Comput Assist Tomogr 1989;13:244-7

82.

83.

pathologic correlation. Radiology 1991;179:469-76

84.

Moore AD, Godwin JD, Muller NL, et al. Pulmonary harartoma: CT findings. Radiology 1987;162:313-17

85.

Winer-Muram HT. The solitary pulmonary nodule. Radiology 2006;239:34-49

98.

Honda O, Tsubamoto M, Inoue A, et al. Pulmonary cavitary nodules on computed tomography: differentiation of malignancy and benignancy. J Comput Assist Tomogr 2007;31:943-9

El-Bazl A, Nitzken M, Khalifa F, et al. 3D shape analysis for early diagnosis of malignant lung nodules. Inf Process Med Imaging 2011;22:772-83

99.

Wormanns D, Diederich S. Characterization of small pulmonary nodules by CT. Eur Radiol 2004;14:1380-91

86.

87.

Woodring JH, Fried AM. Significance of wall thickness in solitary cavities of the lung: a follow-up study. AJR Am J Roentgenol 1983;140:473-4

88.

Kuhlman JE, Reyes BL, Hruban RH, et al. Abnormal air-filled spaces in the lung. Radiographics 1993;13:47-75

89.

Zwirewich CV, Vedal S, Miller RR, Muller NL. Solitary pulmonary nodule: high-resolution CT and radiologic-


100. Boitsios G, Bankier AA, Eisenberg RL. Diffuse pulmonary nodules. AJR Am J Roentgenol 2010;194:W354-66 101. Park J, Kim DS, Shim TS, et al. Lung cancer in patients with idiopathic pulmonary fibrosis. Eur Respir J 2001;17:1216-19 102. de Hoop B, van Ginneken B, Gietema H, Prokop M. Pulmonary perifissural nodules on CT scans: rapid

643

M. Sayyouh et al.

growth is not a predictor of malignancy. Radiology 2012;265:611-16 103. Xu DM, van der Zaag-Loonen HJ, Oudkerk M, et al. Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up. Radiology 2009;250:264-72


104. Bhalla M, Shepard JA, Nakamura K, Kazerooni EA. Dual kV CT to detect calcification in solitary pulmonary nodule. J Comput Assist Tomogr 1995;19:44-7 105. Johnson TR, Krauss B, Sedlmair M, et al. Material differentiation by dual energy CT: initial experience. Eur Radiol 2007;17:1510-17 106. Mendler MH, Bouillet P, Le Sidaner A, et al. Dual-energy CT in the diagnosis and quantification of fatty liver: limited clinical value in comparison to ultrasound scan and single-energy CT, with special reference to iron overload. J Hepatol 1998;28:785-94 107. Wang B, Gao Z, Zou Q, Li L. Quantitative diagnosis of fatty liver with dual-energy CT. An experimental study in rabbits. Acta Radiol 2003;44:92-7 108. Petkovska I, Shah SK, McNitt-Gray MF, et al. Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps. Eur J Radiol 2006;59:244-52 109. Chae EJ, Song JW, Seo JB, et al. Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience. Radiology 2008;249:671-81 110. Kang MJ, Park CM, Lee CH, et al. Dual-energy CT: clinical applications in various pulmonary diseases. Radiographics 2010;30:685-98 111. Moore EH. Technical aspects of needle aspiration lung biopsy: a personal perspective. Radiology 1998;208:303-18 112. Yankelevitz DF, Vazquez M, Henschke CI. Special techniques in transthoracic needle biopsy of pulmonary nodules. Radiol Clin North Am 2000;38:267-79 113. Li Y, Du Y, Yang HF, et al. CT-guided percutaneous core needle biopsy for small (

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