OPINION PAPER/COMMENTARY
Quantitative PET Factors Predictive of the Response to Therapy in Solid Tumors Which Is the Best? Anna Margherita Maffione, MD,* Gaia Grassetto, MD,* Sotirios Chondrogiannis, MD,* Patrick M. Colletti, MD,Þ and Domenico Rubello, MD* Key Words: PET, PERCIST, SUV, SUL, RECIST, MTV, TLG, PREDIST, BOSS (Clin Nucl Med 2014;39: 160Y163)
A
ccurate staging and restaging of oncologic patients are essential components of personalized cancer care. 18F-FDG PET/CT has achieved a significant qualitative and quantitative diagnostic role in managing cancer. Since 1993, the quantification of glycolytic metabolism of a region of interest based on an SUV allowed for a reproducible metric for cancer activity.1 Nevertheless, since its introduction, the application of SUV in the clinical practice has been challenged by several prominent investigators. Although SUV measurements are quite reproducible on a given PET/CT instrument, variability between different systems has elicited the sarcastic moniker ‘‘silly useless value.’’2 Moreover, many variations of SUV have been tested in literature such as SUVmax, SUVmean, SUVpeak, SUL (SUV normalized for lean body mass), SUVbsa (SUV normalized for body surface area), and SUVref3 Thus, quantitative PET studies on the same topic may not be immediately and simply comparable. On the other hand, SUV (SUVmax in most cases) remains a pivotal tool of daily use for nuclear medicine physicians and referring clinicians who request and expect to find quantitation in the PET report rather than qualitative terms like ‘‘mild,’’ ‘‘moderate,’’ or ‘‘severe’’ uptake. Quantitation is even more important when the same lesion has to be evaluated after a treatment by experienced nuclear medicine readers.4 Moreover, the effective use of quantitative parameters for the evaluation of therapy response may allow comparison of clinical data with results known from scientific literature. In 1993, investigators reported the use of PET for monitoring breast cancer response to therapy.5 Soon after, in 1996, SUV and the ratio between FDG uptake and the normal liver were investigated as possible predictors of response to fluorouracil in colorectal cancer with liver metastases.6 Later, the ratio between the target lesion and the liver was no longer favored because the liver uptake has been shown to be quite variable.7,8 In 1996, the comparison of pre-SUV and post-SUV for monitoring the effects of therapy was demonstrated to be more sensitive for primary tumor and more specific for nodal metastasis in comparison with ultrasonography in breast cancer.9 Since 1999, the percentage of SUV decrease [$SUVmax, also called response index (RI)] has been recommended by the European Received for publication October 25, 2013; revision accepted October 30, 2013. From the *Department of Nuclear Medicine, PET Unit, Santa Maria della Misericordia Hospital, Rovigo, Italy; and †Department of Radiology, University of Southern California, Los Angeles, CA. Conflicts of interest and sources of funding: none declared. Reprints: Anna Margherita Maffione, MD, Department of Nuclear Medicine, PET Unit, Santa Maria della Misericordia Hospital, Viale Tre Martiri, 140, 45100 Rovigo, Italy. E-mail: [email protected]. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0363-9762/14/3902Y0160
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Organization for Research and Treatment of Cancer position paper as a method to assess metabolic response of tumors with FDG PET.10 The most valid parameter for response prediction and the determination of neoplastic tissue vitality after treatment is beyond doubt the post-SUVmax. Many studies have investigated the postSUVmax value as a predictor of response with the intent to identify a cutoff that could significantly discriminate the responder from the nonresponder patients. In rectal and breast cancers, most cutoffs are between 3.7 and 5.4 with a range of sensitivity (67%Y86%) and specificity (75%Y100%) reported (Table 1). Unfortunately, because of posttherapy inflammation, a relatively wide range of post-SUVmax could represent both neoplastic tissue and treatment-related phenomena.11Y13 In an attempt to reduce false-positive findings related to inflammation, an interesting new method of imaging analysis, named BOSS (biological target volume overlapping segmentation system), has been recently proposed14 using nuclear medicine and radiation oncology concepts. This method is based on the quantification of the amount of superimposition between the pretreatment and posttreatment biological target volume, presumably unmasking false-positive FDG uptake areas. The simple estimate of the difference between the pre-SUVmax and post-SUVmax has been considered by Capirci and colleagues15 in a sizeable cohort of patients with rectal cancer. The absolute SUV difference was significantly predictive of response to therapy with an accuracy of 79% using a stated cutoff of 6.2. The percentage difference between the post-SUVmax and preSUVmax, typically named RI, has been the object of more attention by investigators. Numerous studies have demonstrated the high predictive power of RI, but a wide range of results are reported (sensitivity, 63%Y100%; specificity, 55%Y100%; and cutoff, 36%Y88%) (Table 1). Soon after, other 2 quantitative parameters were proposedV the metabolic tumor volume (MTV), defined as the sum of voxels with SUV greater than or equal to a selected threshold,16 and the total lesion glycolysis (TLG), introduced in 1999 by Larson et al,17 which combines SUV with MTV as a representation of tumor burden. The application of MTV and TLG in daily practice and in scientific analysis did not bloom like SUV because the calculation of MTV (and therefore TLG that derives from the multiplication of MTV and SUVmean) requires an appropriate workstation, in most cases designed for radiotherapy segmentation and consequently not routinely available. Moreover, investigators have named the MTV concept in many different ways (ie, metabolically active tumor volume, metabolic volume, functional tumor volume, PET-derived tumor volume, etc), such as for TLG that is also called MTB (metabolic tumor burden) or with the variant metabolic tumor diameter. Such name confusion could make simple research tricky. Interestingly, 1 of the first studies published on the clinical application of MTV demonstrated low accuracy in differentiating responder from nonresponder patients to chemoradiation in esophageal cancer. The authors hypothesized that the inflammatory effect of radiation may have obscured tumor-specific metabolic changes.18 Clinical Nuclear Medicine
& Volume 39, Number 2, February 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
RI
* 2014 Lippincott Williams & Wilkins
19
142
51
Esophageal cancer Esophageal cancer
Breast cancer
Osteosarcoma
Jacobs et al39
Tateishi et al23 Breast cancer
Breast cancer
Hatt et al37
Hatt et al40
Im et al41
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
www.nuclearmed.com
Predictive
V
V
V
V
V
V
V
V
V
Sens, 67%; V V spec, 96%; cutoff, 83.3 and 88.0 Sens, 67%; Sens, 63%; Sens, 89%; spec, Sens, 93%; spec, 75%; spec, 92%; 42%; cutoff, spec, 88%; cutoff, 3.7 cutoff, 48% 11.3 cm3 cutoff, 42% V Predictive Predictive Predictive
V
Predictive
V
Predictive
V
V
V
Cutoff, 75%; sens, 79%; spec, 86% (prediction of down-staging) V
Nonpredictive
V
V
V
V
V
V V
PERCIST
SUVmax difference cutoff, 6.2; sens, 71%; spec, 88% V
V
V
V V
Other Parameters
V
V
V
V
V
End Point
TRG by Mandard
Pathologic evaluation
pCR = ypT0N0 TRG by Mandard
TRG by Mandard
pCR, 995% tumor destruction; suboptimal response, G95%
TRG by Mandard
Rusch pathologic response score
Microscopic vs macroscopic CR, PR, SD, and PD (staging EUS vs pathologic evaluation) Pathologic evaluation
V
V
Histopathological necrosis fractions
Sataloff tumor response scale
Metabolic tumor 10% of cancer cells is the cutoff diameter (cutoff, 23%; between pCR and pPR sens, 91%; spec, 89%); diameter-SUV index (cutoff, 55%; sens, 91%; spec, 93%) V V V 10% of cancer cells is the cutoff between pCR and pPR V Predictive V 4 grades of response (percentage of viable residual tumor cell by Miyata) V V V 10% of cancer cells is the cutoff between pCR and pPR Nonpredictive Sens, 70%; V Qualitative pCR and spec, 96% non-pCR by pathologist
V
V
Sens, 78%; spec, Sens, 96%; 63%; cutoff, spec, 92%; 3 23.3 g/mL per cm cutoff, 56% Predictive Predictive
V
V
V
Predictive
V
V
Sens, 86%; spec, Sens, 100%; V Sens, 69%; 75%; cutoff, spec, 8% spec, 80%; 3 23.4 g/mL per cm cutoff, 94% Not predictive V V V V V Sens, 100%; PREDIST sens, spec, 10% 82%; spec, 55% V V V V
Nonpredictive
V
V
Sens, 90%; spec, 80%; cutoff, 70% Nonpredictive
V V
C% TLG
PREDIST, PET residual disease in solid tumor; TRG, tumor regression grade; pCR, pathological complete response; pPR, pathological partial response; SD, stable disease; PD, progression disease; Sens, sensitivity; Spec, specificity; CR, complete response; PR, Partial response; EUS, endoscopic ultrasonography.
20
51
Nonpredictive Nonpredictive
Predictive Sens, 81%; spec, 100%; cutoff, 5.4 V Sens, 100%; spec, 55%; cutoff, 52% V Sens, 82%; spec, 70%; cutoff, 42%
V
V
V
V
V V
TLGpost
& Volume 39, Number 2, February 2014
Yanagawa et al38
49
Esophageal cancer
Roedl et al36
50
27
30
Rectal cancer
Bru¨cher et al35 Esophageal cancer
81 73
69
Rectal cancer Rectal cancer
Sens, 65%; spec, Sens, 69%; Sens, 84%; Sens, 86%; 80%; cutoff, spec, 70%; spec, 80%; spec, 80%; 2.1 cm3 cutoff, 62% cutoff, 5.1 cutoff, 81% Not predictive V Not predictive V V V V V
27
Leibold et al32 Rectal cancer (ad interim assessment) Maffione et al4 Rectal cancer
Lee et al33 Maffione et al27 Bampo et al34
Sens, 85%; Sens, 81%; spec, 80%; spec, 80%; cutoff, 63% cutoff, 5.2 Nonpredictive Nonpredictive
87
Rectal cancer
Capirci et al15
V
V
21
V
Rectal cancer
V V
$% MTV
Melton et al31
V V
MTVpost
15
Nonpredictive Sens, 100%; spec, 86%; cutoff, 36% Sens, 70%; spec, 100%; cutoff, 63% Sens, 86%; spec, 85%; cutoff, 75%
Guillem et al30 Rectal cancer
V V
No. Patients Post-SUVmax
25 20
Tumor
Rectal cancer Rectal cancer
Calvo et al28 Amthauer et al29
Authors
TABLE 1. Publications on Quantitative PET Factors Predictive of the Response to Therapy in Solid Tumors, With Pathologic Findings as Criterion Standard
Clinical Nuclear Medicine Quantitative PET Factors and Solid Tumors
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Clinical Nuclear Medicine
Maffione et al
In studies where MTV, TLG, and their percentage reduction have been investigated as parameters of response to therapy, assessed results are globally controversial both in prediction power and choice of cutoff values even if performed on the same type of cancer (ie, rectal or breast cancer) (Table 1). This data heterogeneity is caused by the lack of standardization on the method for calculation of MTV (ie, different threshold methods, maximum liver uptake, tumorto-background ratio of 3:1, mediastinum uptake), because TLG is calculated from the simple multiplication of MTV and SUVmean. An international harmonized methodology remains a significant future goal.19,20 Most problems concerning the MTV after delineation relate to the moderate or low SUVmax values. When SUVmax is less than 5.5, segmentation with a threshold of, for example, 40% of the SUVmax will give a large volume that will also include areas with SUVmax of about 2.2, commonly considered as physiological tissue uptake.4 Proposals to address this problem include expert physicians or other professionals trained for semiautomatic MTV segmentation, as is used for radiotherapy planning. This would allow the consideration for the characteristics of the image (ie, diffuse vs focal uptake) and tumor biology, and for interpreting and delineating what is disease and what is not.4 On the contrary, some efforts have been performed investigating the repeatability and reproducibility of functional tumor volume measurements, and tumor delineation methods both with FDG and FLT (fluorine-levo-thymidine) PET/CT have been proposed.21,22 Results indicated that an accurate calculation could be achieved by proper standardization. The same hunt for standardization of PET response through semiquantitative and quantitative parameters led in 2009 to the framework by Wahl et al5 for PET response criteria in solid tumors (PERCIST) as a comparison with the well-known morphologic tumor response metrics (WHO criteria and response evaluation criteria in solid tumors). PET response criteria in solid tumors comprise several definitions such as ‘‘measurability of lesions at baseline,’’ ‘‘normalization of uptake,’’ ‘‘complete metabolic response,’’ ‘‘partial metabolic response,’’ ‘‘stable metabolic disease,’’ ‘‘progressive metabolic disease,’’ ‘‘overall response,’’ and ‘‘duration of response.’’ The importance of this study lies in its attempt to add more objectivity to PET reports and in harmonizing international studies. At the moment, only 2 studies have used the PERCIST for response assessment in comparison with pathologic findings4,23 with very different results of specificity (8% in rectal cancer and 96% in breast cancer). Therefore, at present, it is not possible to judge the real utility of these criteria. Nonetheless, some interesting considerations have arisen since the publication of the PERCIST framework. A remarkable editorial by Hofman and colleagues24 in 2010 entitled ‘‘Restaging: should we PERCIST without pattern recognition?’’ discussed the comparative advantages of gestalt identification of disease patterns. In fact, with concerns about the tendency to analyze imaging data by trusting quantitative parameters and cutoffs, the authors believe that this rejection of pattern recognition can lead to inaccuracies.25 In contrast, an Australian group proposes a fully automatic framework to calculate the PERCIST-based thresholding, avoiding inaccuracies in the delineation of the volume of interest reference on the right liver lobe, indispensable for PERCIST analysis. This framework consists of multiatlas registration and voxel classification for CT data to segment liver structure and delineate the volume of interest reference, which is then mapped to the PET data to derive the value of SUVlbm (SUV lean body mass) thresholding for PET to select regions of high metabolism.26 An interesting critique, stimulated by a study on rectal cancer,4 deals with the PERCIST definition of complete metabolic response (‘‘complete resolution of 18F-FDG uptake within measurable target lesion so that it is less than the mean liver activity and indistinguishable 162
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& Volume 39, Number 2, February 2014
from surrounding background blood pool levels’’). The routine practice with response assessment by PET in many different settings has increased physician consciousness that complete metabolic response after chemoradiation therapy could present with higher FDG uptake than surrounding background blood pool levels, as in the case of rectal carcinoma that is likely caused by postactinic inflammation or by physiologic tracer washed out through the intestine. These observations lead to proposals that an exclusive definition of residual disease could not be applicable for every type of tumor.27 A modification of the definition of complete metabolic response has been recently proposed to increase the specificity of PERCIST in rectal cancer. Instead of comparing the target with the blood pool uptake, the authors suggest to use 1.5 SUVmean_liver + 2 SD as a cutoff value to discriminate the complete responders from partial responders. This new criterion, named PREDIST (PET residual disease in solid tumor) has been demonstrated to have good sensitivity and specificity (82% and 55%, respectively), using a routine PET workstation.27 In conclusion, despite the relatively unsuccessful efforts to identify new parameters such as MTV and TLG, SUV, and especially its percentage difference, seems to be the most significant quantitative parameter for baseline and follow-up analysis. Systems like PERCIST have not yet been validated sufficiently. Therefore, it is essential that although any quantitative data system may be considered, the expert reader has the task of the last judgment on imaging interpretation.
REFERENCES 1. Zasadny KR, Wahl RL. Standardized uptake values of normal tissues at PET with 2-[fluorine-18]-fluoro-2-deoxy-D-glucose: variations with body weight and a method for correction. Radiology. 1993;189:847Y850. 2. Keyes JW Jr. SUV: standard uptake or silly useless value? J Nucl Med. 1995;36:1836Y1839. 3. Kelly MD, Declerck JM. SUV: reducing reconstruction-dependent variation in PET SUV. EJNMMI Res. 2011;1:16. 4. Maffione AM, Ferretti A, Grassetto G, et al. Fifteen different 18F-FDG PET/CT qualitative and quantitative parameters investigated as pathological response predictors of locally advanced rectal cancer treated by neoadjuvant chemoradiation therapy. Eur J Nucl Med Mol Imaging. 2013;40:853Y864. 5. Wahl RL, Zasadny K, Helvie M, et al. Metabolic monitoring of breast cancer chemohormonotherapy using positron emission tomography: initial evaluation. J Clin Oncol. 1993;11:2101Y2111. 6. Findlay M, Young H, Cunningham D, et al. Noninvasive monitoring of tumor metabolism using fluorodeoxyglucose and positron emission tomography in colorectal cancer liver metastases: correlation with tumor response to fluorouracil. J Clin Oncol. 1996;14:700Y708. 7. Groheux D, Delord M, Rubello D, et al. Variation of liver SUV on (18)FDGPET/CT studies in women with breast cancer. Clin Nucl Med. 2013; 38:422Y425. 8. Kuruva M, Mittal BR, Abrar ML, et al. Multivariate analysis of various factors affecting background liver and mediastinal standardized uptake values. Indian J Nucl Med. 2012;27:20Y23. 9. Bassa P, Kim EE, Inoue T, et al. Evaluation of preoperative chemotherapy using PET with fluorine-18-fluorodeoxyglucose in breast cancer. J Nucl Med. 1996;37:931Y938. 10. Young H, Baum R, Cremerius U, et al. Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European Organization for Research and Treatment of Cancer (EORTC) PET Study Group. Eur J Cancer. 1999;35:1773Y1782. 11. Capirci C, Rubello D, Chierichetti F, et al. Long-term prognostic value of 18 F-FDG PET in patients with locally advanced rectal cancer previously treated with neoadjuvant radiochemotherapy. Am J Roentgenol. 2006;187: W202YW208. 12. Capirci C, Rampin L, Erba PA, et al. Sequential FDG-PET/CT reliably predicts response of locally advanced rectal cancer to neo-adjuvant chemo-radiation therapy. Eur J Nucl Med Mol Imaging. 2007;34:1583Y1593. 13. Dedemadi G, Wexner SD. Complete response after neoadjuvant therapy in rectal cancer: to operate or not to operate? Dig Dis. 2012;30:109Y117.
* 2014 Lippincott Williams & Wilkins
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Clinical Nuclear Medicine
& Volume 39, Number 2, February 2014
14. Maffione AM, Chondrogiannis S, Marzola MC, et al. Biological target volume (BTV) overlapping segmentation system (BOSS) method for avoiding falsepositive PET findings in assessing response to neoadjuvant chemoradiation therapy in rectal cancer. Clin Nucl Med. In press. 15. Capirci C, Rubello D, Galeotti F, et al. The role of dual-time combined 18-fluorodeoxyglucose positron emission tomography and computed tomography in the staging and restaging workup of locally advanced rectal cancer, treated with preoperative chemoradiation therapy and radical surgery. Int J Radiat Oncol Biol Phys. 2009;74:1461Y1469. 16. Erdi Y, Mawlawi O, Imbriaco M, et al. Volume determination for metastatic lung lesions by adaptive PET image thresholding. Cancer. 1997;80:2505Y2509. 17. Larson SM, Erdi Y, Akhurst T, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging: the visual response score and the change in total lesion glycolysis. Clin Positron Imaging. 1999;2:159Y171. 18. Gillham CM, Lucey JA, Keogan M, et al. (18)FDG uptake during induction chemoradiation for oesophageal cancer fails to predict histomorphological tumour response. Br J Cancer. 2006;95:1174Y1179. 19. Hatt M, Cheze-Le Rest C, Aboagye EO. Reproducibility of 18F-FDG and 3’-deoxy-3’-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010;51:1368Y1376. 20. Cheebsumon P, van Velden FH, Yaqub M. Effects of image characteristics on performance of tumor delineation methods: a test-retest assessment. J Nucl Med. 2011;52:155Y158. 21. Cheebsumon P, Yaqub M, van Velden FH. Impact of [(18)F]FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology. Eur J Nucl Med Mol Imaging. 2011;38:2136Y2144. 22. Frings V, de Langen AJ, Smit EF, et al. Repeatability of metabolically active volume measurements with 18F-FDG and 18F-FLT PET in non-small cell lung cancer. J Nucl Med. 2010;51:1870Y1877. 23. Tateishi U, Miyake M, Nagaoka T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrastenhanced MR imaging-prospective assessment. Radiology. 2012;263:53Y63. 24. Hofman MS, Hicks RJ. Restaging: should we PERCIST without pattern recognition? J Nucl Med. 2010;51:1830Y1832. 25. Hashimoto Y, Tsujikawa T, Kondo C, et al. Accuracy of PET for diagnosis of solid pulmonary lesions with 18F-FDG uptake below the standardized uptake value of 2.5. J Nucl Med. 2006;47:426Y431. 26. Bi L, Kim J, Wen L, et al. Automated and robust PERCIST-based thresholding framework for whole body PET-CT studies. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:5335Y5338. 27. Maffione AM, Ferretti A, Chondrogiannis S, et al. Proposal of a new 18F-FDG PET/CT predictor of response in rectal cancer treated by neoadjuvant chemoradiation therapy and comparison with PERCIST criteria. Clin Nucl Med. 2013;38:795Y797. 28. Calvo FA, Domper M, Matute R, et al. 18F-FDG positron emission tomography staging and restaging in rectal cancer treated with preoperative chemoradiation. Int J Radiat Oncol Biol Phys. 2004;58:528Y535.
* 2014 Lippincott Williams & Wilkins
Quantitative PET Factors and Solid Tumors
29. Amthauer H, Denecke T, Rau B. Response prediction by FDG-PET after neoadjuvant radiochemotherapy and combined regional hyperthermia of rectal cancer: correlation with endorectal ultrasound and histopathology. Eur J Nucl Med Mol Imaging. 2004;31:811Y819. 30. Guillem JG, Moore HG, Akhurst T, et al. Sequential preoperative fluorodeoxyglucose-positron emission tomography assessment of response to preoperative chemoradiation: a means for determining longterm outcomes of rectal cancer. J Am Coll Surg. 2004;199:1Y7. 31. Melton GB, Lavely WC, Jacene HA, et al. Efficacy of preoperative combined 18-fluorodeoxyglucose positron emission tomography and computed tomography for assessing primary rectal cancer response to neoadjuvant therapy. J Gastrointest Surg. 2007;11:961Y969. 32. Leibold T, Akhurst TJ, Chessin DB, et al. Evaluation of 18F-FDG-PET for early detection of suboptimal response of rectal cancer to preoperative chemoradiotherapy: a prospective analysis. Ann Surg Oncol. 2011; 18:83Y89. 33. Lee SJ, Kim JG, Lee SW, et al. Clinical implications of initial FDG-PET/CT in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Cancer Chemother Pharmacol. 2013;71:1201Y1207. 34. Bampo C, Alessi A, Fantini S, et al. Is the standardized uptake value of FDGPET/CT predictive of pathological complete response in locally advanced rectal cancer treated with capecitabine-based neoadjuvant chemoradiation? Oncology. 2013;84:191Y199. 35. Bru¨cher BL, Weber W, Bauer M, et al. Neoadjuvant therapy of esophageal squamous cell carcinoma: response evaluation by positron emission tomography. Ann Surg. 2001;233:300Y309. 36. Roedl JB, Halpern EF, Colen RR, et al. Metabolic tumor width parameters as determined on PET/CT predict disease-free survival and treatment response in squamous cell carcinoma of the esophagus. Mol Imaging Biol. 2009;11:54Y60. 37. Hatt M, Visvikis D, Pradier O, et al. Baseline 18F-FDG PET image-derived parameters for therapy response prediction in oesophageal cancer. Eur J Nucl Med Mol Imaging. 2011;38:1595Y1606. 38. Yanagawa M, Tatsumi M, Miyata H, et al. Evaluation of response to neoadjuvant chemotherapy for esophageal cancer: PET response criteria in solid tumors versus response evaluation criteria in solid tumors. J Nucl Med. 2012;53:872Y880. 39. Jacobs MA, Ouwerkerk R, Wolff AC, et al. Monitoring of neoadjuvant chemotherapy using multiparametric, 23Na sodium MR, and multimodality (PET/CT/MRI) imaging in locally advanced breast cancer. Breast Cancer Res Treat. 2011;128:119Y126. 40. Hatt M, Groheux D, Martineau A, et al. Comparison between 18F-FDG PET image-derived indices for early prediction of response to neoadjuvant chemotherapy in breast cancer. J Nucl Med. 2013;54:341Y349. 41. Im HJ, Kim TS, Park SY, et al. Prediction of tumour necrosis fractions using metabolic and volumetric 18F-FDG PET/CT indices, after one course and at the completion of neoadjuvant chemotherapy, in children and young adults with osteosarcoma. Eur J Nucl Med Mol Imaging. 2012;39:39Y49.
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163
Quantitative PET Factors Predictive of the Response to Therapy in Solid Tumors Which Is the Best? Anna Margherita Maffione, MD,* Gaia Grassetto, MD,* Sotirios Chondrogiannis, MD,* Patrick M. Colletti, MD,Þ and Domenico Rubello, MD* Key Words: PET, PERCIST, SUV, SUL, RECIST, MTV, TLG, PREDIST, BOSS (Clin Nucl Med 2014;39: 160Y163)
A
ccurate staging and restaging of oncologic patients are essential components of personalized cancer care. 18F-FDG PET/CT has achieved a significant qualitative and quantitative diagnostic role in managing cancer. Since 1993, the quantification of glycolytic metabolism of a region of interest based on an SUV allowed for a reproducible metric for cancer activity.1 Nevertheless, since its introduction, the application of SUV in the clinical practice has been challenged by several prominent investigators. Although SUV measurements are quite reproducible on a given PET/CT instrument, variability between different systems has elicited the sarcastic moniker ‘‘silly useless value.’’2 Moreover, many variations of SUV have been tested in literature such as SUVmax, SUVmean, SUVpeak, SUL (SUV normalized for lean body mass), SUVbsa (SUV normalized for body surface area), and SUVref3 Thus, quantitative PET studies on the same topic may not be immediately and simply comparable. On the other hand, SUV (SUVmax in most cases) remains a pivotal tool of daily use for nuclear medicine physicians and referring clinicians who request and expect to find quantitation in the PET report rather than qualitative terms like ‘‘mild,’’ ‘‘moderate,’’ or ‘‘severe’’ uptake. Quantitation is even more important when the same lesion has to be evaluated after a treatment by experienced nuclear medicine readers.4 Moreover, the effective use of quantitative parameters for the evaluation of therapy response may allow comparison of clinical data with results known from scientific literature. In 1993, investigators reported the use of PET for monitoring breast cancer response to therapy.5 Soon after, in 1996, SUV and the ratio between FDG uptake and the normal liver were investigated as possible predictors of response to fluorouracil in colorectal cancer with liver metastases.6 Later, the ratio between the target lesion and the liver was no longer favored because the liver uptake has been shown to be quite variable.7,8 In 1996, the comparison of pre-SUV and post-SUV for monitoring the effects of therapy was demonstrated to be more sensitive for primary tumor and more specific for nodal metastasis in comparison with ultrasonography in breast cancer.9 Since 1999, the percentage of SUV decrease [$SUVmax, also called response index (RI)] has been recommended by the European Received for publication October 25, 2013; revision accepted October 30, 2013. From the *Department of Nuclear Medicine, PET Unit, Santa Maria della Misericordia Hospital, Rovigo, Italy; and †Department of Radiology, University of Southern California, Los Angeles, CA. Conflicts of interest and sources of funding: none declared. Reprints: Anna Margherita Maffione, MD, Department of Nuclear Medicine, PET Unit, Santa Maria della Misericordia Hospital, Viale Tre Martiri, 140, 45100 Rovigo, Italy. E-mail: [email protected]. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0363-9762/14/3902Y0160
160
www.nuclearmed.com
Organization for Research and Treatment of Cancer position paper as a method to assess metabolic response of tumors with FDG PET.10 The most valid parameter for response prediction and the determination of neoplastic tissue vitality after treatment is beyond doubt the post-SUVmax. Many studies have investigated the postSUVmax value as a predictor of response with the intent to identify a cutoff that could significantly discriminate the responder from the nonresponder patients. In rectal and breast cancers, most cutoffs are between 3.7 and 5.4 with a range of sensitivity (67%Y86%) and specificity (75%Y100%) reported (Table 1). Unfortunately, because of posttherapy inflammation, a relatively wide range of post-SUVmax could represent both neoplastic tissue and treatment-related phenomena.11Y13 In an attempt to reduce false-positive findings related to inflammation, an interesting new method of imaging analysis, named BOSS (biological target volume overlapping segmentation system), has been recently proposed14 using nuclear medicine and radiation oncology concepts. This method is based on the quantification of the amount of superimposition between the pretreatment and posttreatment biological target volume, presumably unmasking false-positive FDG uptake areas. The simple estimate of the difference between the pre-SUVmax and post-SUVmax has been considered by Capirci and colleagues15 in a sizeable cohort of patients with rectal cancer. The absolute SUV difference was significantly predictive of response to therapy with an accuracy of 79% using a stated cutoff of 6.2. The percentage difference between the post-SUVmax and preSUVmax, typically named RI, has been the object of more attention by investigators. Numerous studies have demonstrated the high predictive power of RI, but a wide range of results are reported (sensitivity, 63%Y100%; specificity, 55%Y100%; and cutoff, 36%Y88%) (Table 1). Soon after, other 2 quantitative parameters were proposedV the metabolic tumor volume (MTV), defined as the sum of voxels with SUV greater than or equal to a selected threshold,16 and the total lesion glycolysis (TLG), introduced in 1999 by Larson et al,17 which combines SUV with MTV as a representation of tumor burden. The application of MTV and TLG in daily practice and in scientific analysis did not bloom like SUV because the calculation of MTV (and therefore TLG that derives from the multiplication of MTV and SUVmean) requires an appropriate workstation, in most cases designed for radiotherapy segmentation and consequently not routinely available. Moreover, investigators have named the MTV concept in many different ways (ie, metabolically active tumor volume, metabolic volume, functional tumor volume, PET-derived tumor volume, etc), such as for TLG that is also called MTB (metabolic tumor burden) or with the variant metabolic tumor diameter. Such name confusion could make simple research tricky. Interestingly, 1 of the first studies published on the clinical application of MTV demonstrated low accuracy in differentiating responder from nonresponder patients to chemoradiation in esophageal cancer. The authors hypothesized that the inflammatory effect of radiation may have obscured tumor-specific metabolic changes.18 Clinical Nuclear Medicine
& Volume 39, Number 2, February 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
RI
* 2014 Lippincott Williams & Wilkins
19
142
51
Esophageal cancer Esophageal cancer
Breast cancer
Osteosarcoma
Jacobs et al39
Tateishi et al23 Breast cancer
Breast cancer
Hatt et al37
Hatt et al40
Im et al41
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
www.nuclearmed.com
Predictive
V
V
V
V
V
V
V
V
V
Sens, 67%; V V spec, 96%; cutoff, 83.3 and 88.0 Sens, 67%; Sens, 63%; Sens, 89%; spec, Sens, 93%; spec, 75%; spec, 92%; 42%; cutoff, spec, 88%; cutoff, 3.7 cutoff, 48% 11.3 cm3 cutoff, 42% V Predictive Predictive Predictive
V
Predictive
V
Predictive
V
V
V
Cutoff, 75%; sens, 79%; spec, 86% (prediction of down-staging) V
Nonpredictive
V
V
V
V
V
V V
PERCIST
SUVmax difference cutoff, 6.2; sens, 71%; spec, 88% V
V
V
V V
Other Parameters
V
V
V
V
V
End Point
TRG by Mandard
Pathologic evaluation
pCR = ypT0N0 TRG by Mandard
TRG by Mandard
pCR, 995% tumor destruction; suboptimal response, G95%
TRG by Mandard
Rusch pathologic response score
Microscopic vs macroscopic CR, PR, SD, and PD (staging EUS vs pathologic evaluation) Pathologic evaluation
V
V
Histopathological necrosis fractions
Sataloff tumor response scale
Metabolic tumor 10% of cancer cells is the cutoff diameter (cutoff, 23%; between pCR and pPR sens, 91%; spec, 89%); diameter-SUV index (cutoff, 55%; sens, 91%; spec, 93%) V V V 10% of cancer cells is the cutoff between pCR and pPR V Predictive V 4 grades of response (percentage of viable residual tumor cell by Miyata) V V V 10% of cancer cells is the cutoff between pCR and pPR Nonpredictive Sens, 70%; V Qualitative pCR and spec, 96% non-pCR by pathologist
V
V
Sens, 78%; spec, Sens, 96%; 63%; cutoff, spec, 92%; 3 23.3 g/mL per cm cutoff, 56% Predictive Predictive
V
V
V
Predictive
V
V
Sens, 86%; spec, Sens, 100%; V Sens, 69%; 75%; cutoff, spec, 8% spec, 80%; 3 23.4 g/mL per cm cutoff, 94% Not predictive V V V V V Sens, 100%; PREDIST sens, spec, 10% 82%; spec, 55% V V V V
Nonpredictive
V
V
Sens, 90%; spec, 80%; cutoff, 70% Nonpredictive
V V
C% TLG
PREDIST, PET residual disease in solid tumor; TRG, tumor regression grade; pCR, pathological complete response; pPR, pathological partial response; SD, stable disease; PD, progression disease; Sens, sensitivity; Spec, specificity; CR, complete response; PR, Partial response; EUS, endoscopic ultrasonography.
20
51
Nonpredictive Nonpredictive
Predictive Sens, 81%; spec, 100%; cutoff, 5.4 V Sens, 100%; spec, 55%; cutoff, 52% V Sens, 82%; spec, 70%; cutoff, 42%
V
V
V
V
V V
TLGpost
& Volume 39, Number 2, February 2014
Yanagawa et al38
49
Esophageal cancer
Roedl et al36
50
27
30
Rectal cancer
Bru¨cher et al35 Esophageal cancer
81 73
69
Rectal cancer Rectal cancer
Sens, 65%; spec, Sens, 69%; Sens, 84%; Sens, 86%; 80%; cutoff, spec, 70%; spec, 80%; spec, 80%; 2.1 cm3 cutoff, 62% cutoff, 5.1 cutoff, 81% Not predictive V Not predictive V V V V V
27
Leibold et al32 Rectal cancer (ad interim assessment) Maffione et al4 Rectal cancer
Lee et al33 Maffione et al27 Bampo et al34
Sens, 85%; Sens, 81%; spec, 80%; spec, 80%; cutoff, 63% cutoff, 5.2 Nonpredictive Nonpredictive
87
Rectal cancer
Capirci et al15
V
V
21
V
Rectal cancer
V V
$% MTV
Melton et al31
V V
MTVpost
15
Nonpredictive Sens, 100%; spec, 86%; cutoff, 36% Sens, 70%; spec, 100%; cutoff, 63% Sens, 86%; spec, 85%; cutoff, 75%
Guillem et al30 Rectal cancer
V V
No. Patients Post-SUVmax
25 20
Tumor
Rectal cancer Rectal cancer
Calvo et al28 Amthauer et al29
Authors
TABLE 1. Publications on Quantitative PET Factors Predictive of the Response to Therapy in Solid Tumors, With Pathologic Findings as Criterion Standard
Clinical Nuclear Medicine Quantitative PET Factors and Solid Tumors
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Maffione et al
In studies where MTV, TLG, and their percentage reduction have been investigated as parameters of response to therapy, assessed results are globally controversial both in prediction power and choice of cutoff values even if performed on the same type of cancer (ie, rectal or breast cancer) (Table 1). This data heterogeneity is caused by the lack of standardization on the method for calculation of MTV (ie, different threshold methods, maximum liver uptake, tumorto-background ratio of 3:1, mediastinum uptake), because TLG is calculated from the simple multiplication of MTV and SUVmean. An international harmonized methodology remains a significant future goal.19,20 Most problems concerning the MTV after delineation relate to the moderate or low SUVmax values. When SUVmax is less than 5.5, segmentation with a threshold of, for example, 40% of the SUVmax will give a large volume that will also include areas with SUVmax of about 2.2, commonly considered as physiological tissue uptake.4 Proposals to address this problem include expert physicians or other professionals trained for semiautomatic MTV segmentation, as is used for radiotherapy planning. This would allow the consideration for the characteristics of the image (ie, diffuse vs focal uptake) and tumor biology, and for interpreting and delineating what is disease and what is not.4 On the contrary, some efforts have been performed investigating the repeatability and reproducibility of functional tumor volume measurements, and tumor delineation methods both with FDG and FLT (fluorine-levo-thymidine) PET/CT have been proposed.21,22 Results indicated that an accurate calculation could be achieved by proper standardization. The same hunt for standardization of PET response through semiquantitative and quantitative parameters led in 2009 to the framework by Wahl et al5 for PET response criteria in solid tumors (PERCIST) as a comparison with the well-known morphologic tumor response metrics (WHO criteria and response evaluation criteria in solid tumors). PET response criteria in solid tumors comprise several definitions such as ‘‘measurability of lesions at baseline,’’ ‘‘normalization of uptake,’’ ‘‘complete metabolic response,’’ ‘‘partial metabolic response,’’ ‘‘stable metabolic disease,’’ ‘‘progressive metabolic disease,’’ ‘‘overall response,’’ and ‘‘duration of response.’’ The importance of this study lies in its attempt to add more objectivity to PET reports and in harmonizing international studies. At the moment, only 2 studies have used the PERCIST for response assessment in comparison with pathologic findings4,23 with very different results of specificity (8% in rectal cancer and 96% in breast cancer). Therefore, at present, it is not possible to judge the real utility of these criteria. Nonetheless, some interesting considerations have arisen since the publication of the PERCIST framework. A remarkable editorial by Hofman and colleagues24 in 2010 entitled ‘‘Restaging: should we PERCIST without pattern recognition?’’ discussed the comparative advantages of gestalt identification of disease patterns. In fact, with concerns about the tendency to analyze imaging data by trusting quantitative parameters and cutoffs, the authors believe that this rejection of pattern recognition can lead to inaccuracies.25 In contrast, an Australian group proposes a fully automatic framework to calculate the PERCIST-based thresholding, avoiding inaccuracies in the delineation of the volume of interest reference on the right liver lobe, indispensable for PERCIST analysis. This framework consists of multiatlas registration and voxel classification for CT data to segment liver structure and delineate the volume of interest reference, which is then mapped to the PET data to derive the value of SUVlbm (SUV lean body mass) thresholding for PET to select regions of high metabolism.26 An interesting critique, stimulated by a study on rectal cancer,4 deals with the PERCIST definition of complete metabolic response (‘‘complete resolution of 18F-FDG uptake within measurable target lesion so that it is less than the mean liver activity and indistinguishable 162
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& Volume 39, Number 2, February 2014
from surrounding background blood pool levels’’). The routine practice with response assessment by PET in many different settings has increased physician consciousness that complete metabolic response after chemoradiation therapy could present with higher FDG uptake than surrounding background blood pool levels, as in the case of rectal carcinoma that is likely caused by postactinic inflammation or by physiologic tracer washed out through the intestine. These observations lead to proposals that an exclusive definition of residual disease could not be applicable for every type of tumor.27 A modification of the definition of complete metabolic response has been recently proposed to increase the specificity of PERCIST in rectal cancer. Instead of comparing the target with the blood pool uptake, the authors suggest to use 1.5 SUVmean_liver + 2 SD as a cutoff value to discriminate the complete responders from partial responders. This new criterion, named PREDIST (PET residual disease in solid tumor) has been demonstrated to have good sensitivity and specificity (82% and 55%, respectively), using a routine PET workstation.27 In conclusion, despite the relatively unsuccessful efforts to identify new parameters such as MTV and TLG, SUV, and especially its percentage difference, seems to be the most significant quantitative parameter for baseline and follow-up analysis. Systems like PERCIST have not yet been validated sufficiently. Therefore, it is essential that although any quantitative data system may be considered, the expert reader has the task of the last judgment on imaging interpretation.
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14. Maffione AM, Chondrogiannis S, Marzola MC, et al. Biological target volume (BTV) overlapping segmentation system (BOSS) method for avoiding falsepositive PET findings in assessing response to neoadjuvant chemoradiation therapy in rectal cancer. Clin Nucl Med. In press. 15. Capirci C, Rubello D, Galeotti F, et al. The role of dual-time combined 18-fluorodeoxyglucose positron emission tomography and computed tomography in the staging and restaging workup of locally advanced rectal cancer, treated with preoperative chemoradiation therapy and radical surgery. Int J Radiat Oncol Biol Phys. 2009;74:1461Y1469. 16. Erdi Y, Mawlawi O, Imbriaco M, et al. Volume determination for metastatic lung lesions by adaptive PET image thresholding. Cancer. 1997;80:2505Y2509. 17. Larson SM, Erdi Y, Akhurst T, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging: the visual response score and the change in total lesion glycolysis. Clin Positron Imaging. 1999;2:159Y171. 18. Gillham CM, Lucey JA, Keogan M, et al. (18)FDG uptake during induction chemoradiation for oesophageal cancer fails to predict histomorphological tumour response. Br J Cancer. 2006;95:1174Y1179. 19. Hatt M, Cheze-Le Rest C, Aboagye EO. Reproducibility of 18F-FDG and 3’-deoxy-3’-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010;51:1368Y1376. 20. Cheebsumon P, van Velden FH, Yaqub M. Effects of image characteristics on performance of tumor delineation methods: a test-retest assessment. J Nucl Med. 2011;52:155Y158. 21. Cheebsumon P, Yaqub M, van Velden FH. Impact of [(18)F]FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology. Eur J Nucl Med Mol Imaging. 2011;38:2136Y2144. 22. Frings V, de Langen AJ, Smit EF, et al. Repeatability of metabolically active volume measurements with 18F-FDG and 18F-FLT PET in non-small cell lung cancer. J Nucl Med. 2010;51:1870Y1877. 23. Tateishi U, Miyake M, Nagaoka T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrastenhanced MR imaging-prospective assessment. Radiology. 2012;263:53Y63. 24. Hofman MS, Hicks RJ. Restaging: should we PERCIST without pattern recognition? J Nucl Med. 2010;51:1830Y1832. 25. Hashimoto Y, Tsujikawa T, Kondo C, et al. Accuracy of PET for diagnosis of solid pulmonary lesions with 18F-FDG uptake below the standardized uptake value of 2.5. J Nucl Med. 2006;47:426Y431. 26. Bi L, Kim J, Wen L, et al. Automated and robust PERCIST-based thresholding framework for whole body PET-CT studies. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:5335Y5338. 27. Maffione AM, Ferretti A, Chondrogiannis S, et al. Proposal of a new 18F-FDG PET/CT predictor of response in rectal cancer treated by neoadjuvant chemoradiation therapy and comparison with PERCIST criteria. Clin Nucl Med. 2013;38:795Y797. 28. Calvo FA, Domper M, Matute R, et al. 18F-FDG positron emission tomography staging and restaging in rectal cancer treated with preoperative chemoradiation. Int J Radiat Oncol Biol Phys. 2004;58:528Y535.
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Quantitative PET Factors and Solid Tumors
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