International Journal of
Radiation Oncology biology
physics
www.redjournal.org
Physics: The Use of Magnetic Resonance Imaging for Radiation Therapy is Accelerating in Utility and Novelty By Eric E. Klein, PhD, Senior Editor, Issam El Naqa, PhD, Katja Langen, PhD, and Nesrin Dogan, PhD, Associate Editors
The use of magnetic resonance in radiation therapy has been growing in the arena of delivery with real-time image guidance (1) and facilitation of high-frequency ultrasound (2). Although in our previous Oncology Scans (3-7) we examined the field of medical physics and its far-reaching subject matter, in this issue, we turn our attention to the accelerated use of magnetic resonance imaging (MRI) in various regions of the body to improve predictability and assessment. We have chosen 3 articles published outside of the Red Journal that are key examples of the expanded utility of MRI for particular clinical sites. The report by Brynolfsson et al (8) describes the increased use of MRI to determine the stage and specific responses on a personalized basis, in particular, the apparent diffusion coefficient (ADC) texture as an imaging biomarker for high-grade glioma. A similar report by Steenbergen et al (9) describes prostate tumor delineation using multiparametric MRI as a method for pathology validation, with an optimum goal of administering a boost to the dominant prostate lesion. Finally, a comprehensive report by Va´zquez Osorio et al (10) studied improved mapping of deformed anatomy, in particular for external beam radiation therapy and brachytherapy dosage accumulation for cervical cancer.
Brynolfsson et al. ADC texturedAn imaging biomarker for high-grade glioma? Med Phys 2014. (8) Summary: The past 2 decades have witnessed the increased use of imaging as a tool for staging and/or predicting the treatment response. More recently, this area has evolved into extracting a large number of features (biomarkers) and correlating them with biological and clinical endpoints as a part of the new field of radiomics (11). The general problem of radiomics could be posed as a pattern recognition exercise, which requires an understanding of the observed clinical endpoint and the underlying characteristics of the imaging Int J Radiation Oncol Biol Phys, Vol. 93, No. 5, pp. 953e956, 2015 0360-3016/$ - see front matter http://dx.doi.org/10.1016/j.ijrobp.2015.07.2276
modality considered and its derived dynamic and static features (12). Radiomics features are postulated to provide better spatial characterization of uptake heterogeneity beyond the simple descriptors currently used, such as the maximum standardized uptake values in positron emission tomography or the mean ADC in diffusion-weighted (DW) MRI. Texture features have garnered the most interest in the radiomics field, because they integrate intensity with spatial information, resulting in higher order histograms compared with conventional first-order metrics. The study by Brynolfsson et al (8) investigated the role of texture analysis of ADCs from DW-MRI in conjunction with principal components analysis (PCA) to derive pretreatment biomarkers in high-grade gliomas. A cohort of 23 patients with high-grade glioma who had received radiation therapy (RT) plus temozolomide was analyzed. The patients underwent DW-MRI scanning with different gradient strengths (b-values) before treatment. The ADC maps were calculated using nonlinear regression of the diffusion signal from the b-values and resampled to a transversal grid. Textural information was extracted from cuboids encapsulating 88 identified volumes of interest from the patient population using 2-dimensional, 6-bit, gray-level co-occurrence matrices. A total of 20 texture features were extracted in anatomic directions (transversal, sagittal, and coronal) yielding 3 20 texture descriptors. The resulting matrix was decomposed using PCA for the clinical endpoint of survival. The most prominent PCs identified were PC1, PC3, and PC5, which were found to provide the best separation between 2 risk groups with a median survival of 1099 days and 345 days. The corresponding model parameters (pseudoscores) were estimated by projection into the truncated 3-PC space. The model was tested for robustness using sevenfold cross-validation using an orthogonal partial least-squares approach. The model was shown to be independent of age, stage, and surgical procedure. Comment: The report by Brynolfsson et al (8) is an example of the emerging field of radiomics, in which extracted
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imaging-based features are used to decode tumor phenotypes and model the response to treatment (13). The extracted texture features in this case are used to capture tumor heterogeneity, that is, the integrity of tumor cellularity, which is then related to patient response and survival. For analytics, it would have been useful to compare the extracted texture features with the clinical outcomes using univariate analysis in contrast to common first-order statistics such as the mean ADCs. This could also provide an intuitive understanding of the underlying biology. Moreover, the features were extracted using 2-dimensional analysis in the main plane views instead of 3-dimensional analysis, which might have provided a better representation of the volumetric textural information. The investigators applied PCA, an unsupervised learning technique, in contrast to most publications in the field, which have used logistic regression analysis or more advanced supervised machine-learning techniques. The advantage with the former is that the separation between the classes was identified without a priori grouping of the data and might have provided a better opportunity to generalize to unseen data. However, this was not validated on an independent data set in the study. In contrast, with supervised learning, the patterns are learned through a training phase and require thorough statistical internal testing to avoid overfitting of pitfalls before further consideration. However, a drawback of PCA is that the underlying interpretation of the pattern captured can be masked by a nonintuitive linear combination of the features. In addition to the 2-dimensional, 6-bit, gray-level co-occurrence matrix texture features examined in their study, other variants have been applied in published studies, including the neighborhood gray tone difference matrix, run-length matrix, and gray-level sizezone matrix, with each having its own merits and shortfalls for understanding tumor heterogeneity. Another aspect that the investigators successfully considered is the crosscorrelation with other prognostic factors such as age, stage, and resection, among others. However, an important confounding effect that was not discussed is the tumor volume (14). This is in addition to the uncertainties associated with contouring, acquisition parameters, image resolution, and signal-to-noise ratio, among others. Future directions in this field should aim to address these issues, in addition to including multiparametric MRI (MP-MRI) studies or multimodality imaging such as positron emission tomographye computed tomography or positron emission tomography/ MRI, which could provide a necessary wealth of information to improve the prediction power of current outcome models.
Steenbergen et al. Prostate tumor delineation using multi-parametric magnetic resonance imaging: Inter-observer variability and pathology validation. Radiother Oncol 2015. (9) Summary: The study by Steenbergen et al (9) addresses the delineation accuracy of prostate lesions using MP-MRI (dynamic contrast-enhanced MRI, T2-weighted, diffusion-
International Journal of Radiation Oncology Biology Physics
weighted imaging performed on a 1.5 T MRI scanner). Pathology data were available and used as the reference. The study population consisted of 20 prostate cancer patients who had undergone MP-MRI before prostatectomy. The contours from 6 teams, each consisting of a radiation oncologist and radiologist, were compared to assess the interobserver agreement and accuracy in contrast to the histological specimens. A total of 89 lesions were identified histologically; that is, in addition to the 20 dominant lesions, 69 satellite lesions were detected. Of the 89 lesions, 22, plus all dominant lesions, were larger than 0.4 cm3. A total of 40 lesions were detected by at least 1 team on the MP-MRI scans, and 22 of those lesions were true positive lesions. Of the 40 sites, 19 were detected by consensus, with all groups detecting them. Of the remaining 21 lesions, 4 were true and 17 were false positive. The false-positive lesions revealed a certain amount of ambiguity in the MP-MRI information if analyzed in the single-observer setting, which represents the typical clinical situation. Of the 19 lesions detected by all observers on MP-MRI, 18 were true positive and dominant and 1 was a lesion for which no pathology data were available. Of the 20 dominant lesions, 18 were detected by each observer group on MP-MRI. Of the 2 dominant lesions that were not detected by all the groups, 1 was missed by all the groups. This lesion was a 1.9-cm3 lesion located in the central gland. The second dominant lesion that was missed was the smallest of the dominant lesions with a volume of 0.45 cm3. The median volume of the 18 dominant lesions that were detected on MP-MRI was 2.4 cm3. Of the 69 satellite lesions, 66 were missed by all the groups on MP-MRI. Most of the satellite lesions were smaller than 0.4 cm3. For the 18 dominant lesions, the MP-MRI contour variability among the 6 user groups was 0.23 cm. The observer-delineated lesions were in better agreement with each other than with the pathology volumes. Agreement was measured using the kappa index, which ranges from 0 (no agreement) to unity (perfect agreement). For the agreement between the users and the pathology data, the kappa index was 0.45 0.16 (range 0.08 to 0.74). Comment: The Focal Lesion Ablative Microboost (FLAME) trial was designed to test the benefit of administering a boost to the dominant prostate lesion concurrently to a dose of 95 Gy (2.7 Gy 35 fractions) with a whole gland dose of 77 Gy (2.2 Gy 35 fractions) compared with a whole gland dose only. The primary endpoint was the 5-year freedom from biochemical failure rate (15). The protocol allowed for the use of different MRI studies to delineate the boost volume: dynamic contrast-enhanced MRI, T2weighted, diffusion-weighted MRI, and MR spectroscopy imaging. The benefit of a boost to the dominant lesion is clearly linked to the delineation accuracy for these lesions. The interuser variability of MP-MRI contours for prostate lesions has been the focus of several recent studies (16-19). The study by Steenbergen et al (9) has shown that for the particular MP-MRI studies, the dominant lesion was
Volume 93 Number 5 2015
detected with a sensitivity of 90% (18 of 20). The 2 misses were attributed to lesion size (0.45 cm3) and location (central gland). The inability to detect smaller satellites by consensus on MP-MRI studies highlights the need for a dose bath to the entire gland if dosimetric coverage of these lesions is intended. The interuser delineation variability was in the 2- to 3-mm range, and agreement with the pathology data was less than that among the users. Additional margins might be needed to ensure complete coverage of the dominant lesion. In conclusion, their study has demonstrated the value and limitations of MP-MRI studies for the detection and contouring of tumor lesions in prostate cancer patients. This understanding is fundamental to clinicians contemplating the use of subvolume boosting in prostate cancer patients.
Va´squez Osorio et al. Improving anatomical mapping of complexly deformed anatomy for external beam radiotherapy and brachytherapy dose accumulation in cervical cancer. Med Phys 2015. (10) Summary: The dose accumulation for combined external beam (EB) and brachytherapy (BT) dose distributions for the treatment of cervical cancer is challenging because of the large anatomic deformations caused by shrinkage of tumor; deformation and movement of the bladder, cervix and uterus, and rectum and sigmoid, changes in bladder and rectum filling; organ sliding; and brachytherapy applicator insertion (20, 21). Such large and complex deformations can be problematic for most of the intensity-based deformable image registration algorithms, especially those resulting from sliding between tissues (22). The work by Va´squez Osorio et al (10) proposed a novel structure-wise deformable image registration technique that uses a vector field integration method for registration of cervical cancer patients. The work included 12 cervical cancer patients who underwent MRI and were treated with intensity modulated RT (64 Gy in 23 fractions), followed by an intracavitary BT boost (3 7 Gy) and an EB boost to the lymph nodes for selected patients. T2-weighted MRI scans for each patient were acquired before both EB and BT treatments. First, the bony anatomy on the EB and BT MRI scans was rigidly aligned using a semiautomatic registration algorithm. Next, the inner anatomic structures such as the uterus midline, target volume, and cervix, among others, and the corresponding points in the surrounding regions on both MRI scans were delineated to be used as the inner validation structures and outer anatomic validation landmarks. Features in the region surrounding the manually delineated structures were automatically segmented, and each structure and feature pair were then registered independently using the in-house structure-wise nonrigid registration with vector-field integration method (23). In addition, a background transformation was performed to account for the regions far from all structures and features. Finally, the structured-based, feature-based, and
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background transformations were integrated into a single vector field for EB and BT dose summation. The results showed that the proposed structure-wise registration with vector integration method produced the best results in terms of inner and outer anatomic correctness (average 3.5 mm and 3.4 mm, respectively) compared with both rigid and nonrigid registration based on all structures. Although nonrigid registration based on all structures aligned the inner validation structures better than did rigid registration (6.3 mm vs 22.4 cm), it failed to align to the outer anatomic validation landmarks better than did rigid registration (16.9 mm vs 4.3 mm). Comment: The novel structure-wise nonrigid registration, which integrates the weighted sum of different transformations into a single vector, presented in their study allowed more accurate registration of large and complex deformations and the sliding among tissues observed between EBRT and BT for cervical cancer patients. Such a method might make managing the accumulation of the 3dimensional dose distribution between EBRT and BT practical. One major difference between the current and other published methods is the ability to control the size of each transformation scope during vector field integration. The limitations of the proposed method include a lack of consistency and one-to-one correspondence of the between input images, because there is not a specific constraint for different transformations to be coherent among each other. Furthermore, uncertainties resulting from inter- and intraobserver variations exist, because the method uses manual delineation of the structures to be registered. The new method presented might allow management of the accumulation of the 3-dimensional dose distribution between EBRT and BT practical. However, future studies on the dose accumulation between EBRT and BT are needed to quantify the dose mapping errors and assess the effect of geometric uncertainties on dose accumulation.
References 1. Li HH, Rodriguez VL, Green OL, et al. Patient-specific quality assurance for the delivery of (60)Co intensity modulated radiation therapy subject to a 0.35-T lateral magnetic field. Int J Radiat Oncol Biol Phys 2015;91:65-72. 2. Pichardo S, Ko¨hler M, Lee J, et al. In vivo optimization study for multi-baseline MR-based thermometry in the context of hyperthermia using MR-guided high intensity focused ultrasound for head and neck applications. Int J Hyperthermia 2014;30:579-592. 3. Klein EE, Dong L, Paganetti H, et al. The far-reaching subject matter of medical physics in radiation oncology. Int J Radiat Oncol Biol Phys 2015;91:697-700. 4. Klein EE, Brock KK, Fontenot JD, et al. Oncology scandBringing rapid learning and adaptive techniques into clinical physics. Int J Radiat Oncol Biol Phys 2014;90:243-245. 5. Klein EE, Dogan N, Chen Z, et al. Oncology scandThe vision of medical physics. Int J Radiat Oncol Biol Phys 2014;88:251-253. 6. Klein EE, Das IJ, Dong L, et al. Oncology scandImprovements in dose calculation, deformable registration, and MR-guided radiation delivery. Int J Radiat Oncol Biol Phys 2013;86:395-397.
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7. Klein EE, Chen Z, Chetty IJ, et al. Oncology scandPhysics. Int J Radiat Oncol Biol Phys 2012;84:871-873. 8. Brynolfsson P, Nilsson D, Henriksson R, et al. ADC texturedAn imaging biomarker for high-grade glioma? Med Phys 2014;41:101903. 9. Steenbergen P, Haustermans K, Lerut E, et al. Prostate tumor delineation using multiparameter magnetic resonance imaging: Inter-observer variability and pathology validation. Radiother Oncol 2015;115:186-190. 10. Va´squez Osorio EM, Kolkman-Deurloo K, Schuring-Pereira M, et al. Improving anatomical mapping of complexly deformed anatomy for external beam radiotherapy and brachytherapy dose accumulation in cervical cancer. Med Phys 2015;42:206-220. 11. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-446. 12. El Naqa I. The role of quantitative PET in predicting cancer treatment outcomes. Clin Transl Imaging 2014;2:305-320. 13. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006. 14. Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med 2014;55:37-42. 15. Lips IM, van der Heide UA, Haustermans K, et al. Single blind randomized phase III trial to investigate the benefit of a focal lesion ablative microboost in prostate cancer (FLAME-trial): Study protocol for a randomized controlled trial. Trials 2011;12:255. 16. Bratan F, Niaf E, Melodelima C, et al. Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: A prospective study. Eur Radiol 2013;23:2019-2029.
International Journal of Radiation Oncology Biology Physics 17. Jung SI, Donati OF, Vargas HA, et al. Transition zone prostate cancer: Incremental value of diffusion-weighted endorectal MR imaging in tumor detection and assessment of aggressiveness. Radiology 2013; 269:493-503. 18. Rischke HC, Nestle U, Fechter T, et al. 3 Tesla multiparametric MRI for GTV definition of dominant intraprostatic lesions in patients with prostate cancerdAn interobserver variability study. Radiat Oncol 2013;8:183. 19. Anwar M, Westphalen AC, Jung AJ, et al. Role of endorectal MR imaging and MR spectroscopic imaging in defining treatable intraprostatic tumor foci in prostate cancer: Quantitative analysis of imaging contour compared to whole-mount histopathology. Radiother Oncol 2014;110:303-308. 20. Kim H, Huq MS, Houser C, et al. Mapping of dose distribution from IMRT onto MRI-guided high dose rate brachytherapy using deformable image registration for cervical cancer treatments: Preliminary study with commercially available software. J Contemp Brachytherapy 2014;6:178-184. 21. van de Bunt L, van der Heide UA, Ketelaars M, et al. Conventional, conformal, and intensity-modulated radiation therapy treatment planning of external beam radiotherapy for cervical cancer: The impact of tumor regression. Int J Radiat Oncol Biol Phys 2006;64:189-196. 22. Christensen GE, Carlson B, Chao KS, et al. Image-based dose planning of intracavitary brachytherapy: Registration of serial-imaging studies using deformable anatomic templates. Int J Radiat Oncol Biol Phys 2001;51:227-243. 23. Vasquez Osorio EM, Hoogeman MS, Bondar L, et al. A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy. Med Phys 2009;36:2848-2859.
Radiation Oncology biology
physics
www.redjournal.org
Physics: The Use of Magnetic Resonance Imaging for Radiation Therapy is Accelerating in Utility and Novelty By Eric E. Klein, PhD, Senior Editor, Issam El Naqa, PhD, Katja Langen, PhD, and Nesrin Dogan, PhD, Associate Editors
The use of magnetic resonance in radiation therapy has been growing in the arena of delivery with real-time image guidance (1) and facilitation of high-frequency ultrasound (2). Although in our previous Oncology Scans (3-7) we examined the field of medical physics and its far-reaching subject matter, in this issue, we turn our attention to the accelerated use of magnetic resonance imaging (MRI) in various regions of the body to improve predictability and assessment. We have chosen 3 articles published outside of the Red Journal that are key examples of the expanded utility of MRI for particular clinical sites. The report by Brynolfsson et al (8) describes the increased use of MRI to determine the stage and specific responses on a personalized basis, in particular, the apparent diffusion coefficient (ADC) texture as an imaging biomarker for high-grade glioma. A similar report by Steenbergen et al (9) describes prostate tumor delineation using multiparametric MRI as a method for pathology validation, with an optimum goal of administering a boost to the dominant prostate lesion. Finally, a comprehensive report by Va´zquez Osorio et al (10) studied improved mapping of deformed anatomy, in particular for external beam radiation therapy and brachytherapy dosage accumulation for cervical cancer.
Brynolfsson et al. ADC texturedAn imaging biomarker for high-grade glioma? Med Phys 2014. (8) Summary: The past 2 decades have witnessed the increased use of imaging as a tool for staging and/or predicting the treatment response. More recently, this area has evolved into extracting a large number of features (biomarkers) and correlating them with biological and clinical endpoints as a part of the new field of radiomics (11). The general problem of radiomics could be posed as a pattern recognition exercise, which requires an understanding of the observed clinical endpoint and the underlying characteristics of the imaging Int J Radiation Oncol Biol Phys, Vol. 93, No. 5, pp. 953e956, 2015 0360-3016/$ - see front matter http://dx.doi.org/10.1016/j.ijrobp.2015.07.2276
modality considered and its derived dynamic and static features (12). Radiomics features are postulated to provide better spatial characterization of uptake heterogeneity beyond the simple descriptors currently used, such as the maximum standardized uptake values in positron emission tomography or the mean ADC in diffusion-weighted (DW) MRI. Texture features have garnered the most interest in the radiomics field, because they integrate intensity with spatial information, resulting in higher order histograms compared with conventional first-order metrics. The study by Brynolfsson et al (8) investigated the role of texture analysis of ADCs from DW-MRI in conjunction with principal components analysis (PCA) to derive pretreatment biomarkers in high-grade gliomas. A cohort of 23 patients with high-grade glioma who had received radiation therapy (RT) plus temozolomide was analyzed. The patients underwent DW-MRI scanning with different gradient strengths (b-values) before treatment. The ADC maps were calculated using nonlinear regression of the diffusion signal from the b-values and resampled to a transversal grid. Textural information was extracted from cuboids encapsulating 88 identified volumes of interest from the patient population using 2-dimensional, 6-bit, gray-level co-occurrence matrices. A total of 20 texture features were extracted in anatomic directions (transversal, sagittal, and coronal) yielding 3 20 texture descriptors. The resulting matrix was decomposed using PCA for the clinical endpoint of survival. The most prominent PCs identified were PC1, PC3, and PC5, which were found to provide the best separation between 2 risk groups with a median survival of 1099 days and 345 days. The corresponding model parameters (pseudoscores) were estimated by projection into the truncated 3-PC space. The model was tested for robustness using sevenfold cross-validation using an orthogonal partial least-squares approach. The model was shown to be independent of age, stage, and surgical procedure. Comment: The report by Brynolfsson et al (8) is an example of the emerging field of radiomics, in which extracted
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imaging-based features are used to decode tumor phenotypes and model the response to treatment (13). The extracted texture features in this case are used to capture tumor heterogeneity, that is, the integrity of tumor cellularity, which is then related to patient response and survival. For analytics, it would have been useful to compare the extracted texture features with the clinical outcomes using univariate analysis in contrast to common first-order statistics such as the mean ADCs. This could also provide an intuitive understanding of the underlying biology. Moreover, the features were extracted using 2-dimensional analysis in the main plane views instead of 3-dimensional analysis, which might have provided a better representation of the volumetric textural information. The investigators applied PCA, an unsupervised learning technique, in contrast to most publications in the field, which have used logistic regression analysis or more advanced supervised machine-learning techniques. The advantage with the former is that the separation between the classes was identified without a priori grouping of the data and might have provided a better opportunity to generalize to unseen data. However, this was not validated on an independent data set in the study. In contrast, with supervised learning, the patterns are learned through a training phase and require thorough statistical internal testing to avoid overfitting of pitfalls before further consideration. However, a drawback of PCA is that the underlying interpretation of the pattern captured can be masked by a nonintuitive linear combination of the features. In addition to the 2-dimensional, 6-bit, gray-level co-occurrence matrix texture features examined in their study, other variants have been applied in published studies, including the neighborhood gray tone difference matrix, run-length matrix, and gray-level sizezone matrix, with each having its own merits and shortfalls for understanding tumor heterogeneity. Another aspect that the investigators successfully considered is the crosscorrelation with other prognostic factors such as age, stage, and resection, among others. However, an important confounding effect that was not discussed is the tumor volume (14). This is in addition to the uncertainties associated with contouring, acquisition parameters, image resolution, and signal-to-noise ratio, among others. Future directions in this field should aim to address these issues, in addition to including multiparametric MRI (MP-MRI) studies or multimodality imaging such as positron emission tomographye computed tomography or positron emission tomography/ MRI, which could provide a necessary wealth of information to improve the prediction power of current outcome models.
Steenbergen et al. Prostate tumor delineation using multi-parametric magnetic resonance imaging: Inter-observer variability and pathology validation. Radiother Oncol 2015. (9) Summary: The study by Steenbergen et al (9) addresses the delineation accuracy of prostate lesions using MP-MRI (dynamic contrast-enhanced MRI, T2-weighted, diffusion-
International Journal of Radiation Oncology Biology Physics
weighted imaging performed on a 1.5 T MRI scanner). Pathology data were available and used as the reference. The study population consisted of 20 prostate cancer patients who had undergone MP-MRI before prostatectomy. The contours from 6 teams, each consisting of a radiation oncologist and radiologist, were compared to assess the interobserver agreement and accuracy in contrast to the histological specimens. A total of 89 lesions were identified histologically; that is, in addition to the 20 dominant lesions, 69 satellite lesions were detected. Of the 89 lesions, 22, plus all dominant lesions, were larger than 0.4 cm3. A total of 40 lesions were detected by at least 1 team on the MP-MRI scans, and 22 of those lesions were true positive lesions. Of the 40 sites, 19 were detected by consensus, with all groups detecting them. Of the remaining 21 lesions, 4 were true and 17 were false positive. The false-positive lesions revealed a certain amount of ambiguity in the MP-MRI information if analyzed in the single-observer setting, which represents the typical clinical situation. Of the 19 lesions detected by all observers on MP-MRI, 18 were true positive and dominant and 1 was a lesion for which no pathology data were available. Of the 20 dominant lesions, 18 were detected by each observer group on MP-MRI. Of the 2 dominant lesions that were not detected by all the groups, 1 was missed by all the groups. This lesion was a 1.9-cm3 lesion located in the central gland. The second dominant lesion that was missed was the smallest of the dominant lesions with a volume of 0.45 cm3. The median volume of the 18 dominant lesions that were detected on MP-MRI was 2.4 cm3. Of the 69 satellite lesions, 66 were missed by all the groups on MP-MRI. Most of the satellite lesions were smaller than 0.4 cm3. For the 18 dominant lesions, the MP-MRI contour variability among the 6 user groups was 0.23 cm. The observer-delineated lesions were in better agreement with each other than with the pathology volumes. Agreement was measured using the kappa index, which ranges from 0 (no agreement) to unity (perfect agreement). For the agreement between the users and the pathology data, the kappa index was 0.45 0.16 (range 0.08 to 0.74). Comment: The Focal Lesion Ablative Microboost (FLAME) trial was designed to test the benefit of administering a boost to the dominant prostate lesion concurrently to a dose of 95 Gy (2.7 Gy 35 fractions) with a whole gland dose of 77 Gy (2.2 Gy 35 fractions) compared with a whole gland dose only. The primary endpoint was the 5-year freedom from biochemical failure rate (15). The protocol allowed for the use of different MRI studies to delineate the boost volume: dynamic contrast-enhanced MRI, T2weighted, diffusion-weighted MRI, and MR spectroscopy imaging. The benefit of a boost to the dominant lesion is clearly linked to the delineation accuracy for these lesions. The interuser variability of MP-MRI contours for prostate lesions has been the focus of several recent studies (16-19). The study by Steenbergen et al (9) has shown that for the particular MP-MRI studies, the dominant lesion was
Volume 93 Number 5 2015
detected with a sensitivity of 90% (18 of 20). The 2 misses were attributed to lesion size (0.45 cm3) and location (central gland). The inability to detect smaller satellites by consensus on MP-MRI studies highlights the need for a dose bath to the entire gland if dosimetric coverage of these lesions is intended. The interuser delineation variability was in the 2- to 3-mm range, and agreement with the pathology data was less than that among the users. Additional margins might be needed to ensure complete coverage of the dominant lesion. In conclusion, their study has demonstrated the value and limitations of MP-MRI studies for the detection and contouring of tumor lesions in prostate cancer patients. This understanding is fundamental to clinicians contemplating the use of subvolume boosting in prostate cancer patients.
Va´squez Osorio et al. Improving anatomical mapping of complexly deformed anatomy for external beam radiotherapy and brachytherapy dose accumulation in cervical cancer. Med Phys 2015. (10) Summary: The dose accumulation for combined external beam (EB) and brachytherapy (BT) dose distributions for the treatment of cervical cancer is challenging because of the large anatomic deformations caused by shrinkage of tumor; deformation and movement of the bladder, cervix and uterus, and rectum and sigmoid, changes in bladder and rectum filling; organ sliding; and brachytherapy applicator insertion (20, 21). Such large and complex deformations can be problematic for most of the intensity-based deformable image registration algorithms, especially those resulting from sliding between tissues (22). The work by Va´squez Osorio et al (10) proposed a novel structure-wise deformable image registration technique that uses a vector field integration method for registration of cervical cancer patients. The work included 12 cervical cancer patients who underwent MRI and were treated with intensity modulated RT (64 Gy in 23 fractions), followed by an intracavitary BT boost (3 7 Gy) and an EB boost to the lymph nodes for selected patients. T2-weighted MRI scans for each patient were acquired before both EB and BT treatments. First, the bony anatomy on the EB and BT MRI scans was rigidly aligned using a semiautomatic registration algorithm. Next, the inner anatomic structures such as the uterus midline, target volume, and cervix, among others, and the corresponding points in the surrounding regions on both MRI scans were delineated to be used as the inner validation structures and outer anatomic validation landmarks. Features in the region surrounding the manually delineated structures were automatically segmented, and each structure and feature pair were then registered independently using the in-house structure-wise nonrigid registration with vector-field integration method (23). In addition, a background transformation was performed to account for the regions far from all structures and features. Finally, the structured-based, feature-based, and
Oncology Scan
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background transformations were integrated into a single vector field for EB and BT dose summation. The results showed that the proposed structure-wise registration with vector integration method produced the best results in terms of inner and outer anatomic correctness (average 3.5 mm and 3.4 mm, respectively) compared with both rigid and nonrigid registration based on all structures. Although nonrigid registration based on all structures aligned the inner validation structures better than did rigid registration (6.3 mm vs 22.4 cm), it failed to align to the outer anatomic validation landmarks better than did rigid registration (16.9 mm vs 4.3 mm). Comment: The novel structure-wise nonrigid registration, which integrates the weighted sum of different transformations into a single vector, presented in their study allowed more accurate registration of large and complex deformations and the sliding among tissues observed between EBRT and BT for cervical cancer patients. Such a method might make managing the accumulation of the 3dimensional dose distribution between EBRT and BT practical. One major difference between the current and other published methods is the ability to control the size of each transformation scope during vector field integration. The limitations of the proposed method include a lack of consistency and one-to-one correspondence of the between input images, because there is not a specific constraint for different transformations to be coherent among each other. Furthermore, uncertainties resulting from inter- and intraobserver variations exist, because the method uses manual delineation of the structures to be registered. The new method presented might allow management of the accumulation of the 3-dimensional dose distribution between EBRT and BT practical. However, future studies on the dose accumulation between EBRT and BT are needed to quantify the dose mapping errors and assess the effect of geometric uncertainties on dose accumulation.
References 1. Li HH, Rodriguez VL, Green OL, et al. Patient-specific quality assurance for the delivery of (60)Co intensity modulated radiation therapy subject to a 0.35-T lateral magnetic field. Int J Radiat Oncol Biol Phys 2015;91:65-72. 2. Pichardo S, Ko¨hler M, Lee J, et al. In vivo optimization study for multi-baseline MR-based thermometry in the context of hyperthermia using MR-guided high intensity focused ultrasound for head and neck applications. Int J Hyperthermia 2014;30:579-592. 3. Klein EE, Dong L, Paganetti H, et al. The far-reaching subject matter of medical physics in radiation oncology. Int J Radiat Oncol Biol Phys 2015;91:697-700. 4. Klein EE, Brock KK, Fontenot JD, et al. Oncology scandBringing rapid learning and adaptive techniques into clinical physics. Int J Radiat Oncol Biol Phys 2014;90:243-245. 5. Klein EE, Dogan N, Chen Z, et al. Oncology scandThe vision of medical physics. Int J Radiat Oncol Biol Phys 2014;88:251-253. 6. Klein EE, Das IJ, Dong L, et al. Oncology scandImprovements in dose calculation, deformable registration, and MR-guided radiation delivery. Int J Radiat Oncol Biol Phys 2013;86:395-397.
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