Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach

Gulati M, Hu JS, Desai B, Hwang DH, Grant EG, Duddalwar VA (2015) Contrast-enhanced sonography for monitoring neoadjuvant chemotherapy in soft tissue sarcomas. J Ultrasound Med 34(8):1489–1499. https://doi.org/10.7863/ultra.34.8.1489

Article  Google Scholar 

Schuetze SM (2005) Imaging and response in soft tissue sarcomas. Hematol Oncol Clin North Am 19(3):471-487,vi. https://doi.org/10.1016/j.hoc.2005.03.001

Article  Google Scholar 

Schuetze SM, Baker LH, Benjamin RS, Canetta R (2008) Selection of response criteria for clinical trials of sarcoma treatment. Oncologist 13(Suppl 2):32–40. https://doi.org/10.1634/theoncologist.13-S2-32

Article  Google Scholar 

Spinnato P, Kind M, Le Loarer F, Bianchi G, Colangeli M, Sambri A, Ponti F, van Langevelde K, Crombe A (2021) Soft tissue sarcomas: the role of quantitative MRI in treatment response evaluation. Acad Radiol. https://doi.org/10.1016/j.acra.2021.08.007

Article  Google Scholar 

Baheti AD, O’Malley RB, Kim S, Keraliya AR, Tirumani SH, Ramaiya NH, Wang CL (2016) Soft-tissue sarcomas: an update for radiologists based on the revised 2013 World Health Organization Classification. AJR Am J Roentgenol 206(5):924–932. https://doi.org/10.2214/AJR.15.15498

Article  Google Scholar 

Kurland BF, Gerstner ER, Mountz JM, Schwartz LH, Ryan CW, Graham MM, Buatti JM, Fennessy FM, Eikman EA, Kumar V, Forster KM, Wahl RL, Lieberman FS (2012) Promise and pitfalls of quantitative imaging in oncology clinical trials. Magn Reson Imaging 30(9):1301–1312. https://doi.org/10.1016/j.mri.2012.06.009

Article  Google Scholar 

Stacchiotti S, Collini P, Messina A, Morosi C, Barisella M, Bertulli R, Piovesan C, Dileo P, Torri V, Gronchi A, Casali PG (2009) High-grade soft-tissue sarcomas: tumor response assessment–pilot study to assess the correlation between radiologic and pathologic response by using RECIST and Choi criteria. Radiology 251(2):447–456. https://doi.org/10.1148/radiol.2512081403

Article  Google Scholar 

Stacchiotti S, Verderio P, Messina A, Morosi C, Collini P, Llombart-Bosch A, Martin J, Comandone A, Cruz J, Ferraro A, Grignani G, Pizzamiglio S, Quagliuolo V, Picci P, Frustaci S, Dei Tos AP, Casali PG, Gronchi A (2012) Tumor response assessment by modified Choi criteria in localized high-risk soft tissue sarcoma treated with chemotherapy. Cancer 118(23):5857–5866. https://doi.org/10.1002/cncr.27624

Article  Google Scholar 

Patel DB, Matcuk GR Jr (2018) Imaging of soft tissue sarcomas. Chin Clin Oncol 7(4):35

Article  Google Scholar 

Kalisvaart GM, Bloem JL, Bovee J, van de Sande MAJ, Gelderblom H, van der Hage JA, Hartgrink HH, Krol ADG, de Geus-Oei LF, Grootjans W (2021) Personalising sarcoma care using quantitative multimodality imaging for response assessment. Clin Radiol 76(4):313.E311-313.E313. https://doi.org/10.1016/j.crad.2020.12.009

Article  Google Scholar 

Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247. https://doi.org/10.1016/j.ejca.2008.10.026

Article  CAS  Google Scholar 

Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, Verweij J, Van Glabbeke M, van Oosterom AT, Christian MC, Gwyther SG (2000) New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 92(3):205–216. https://doi.org/10.1093/jnci/92.3.205

Article  CAS  Google Scholar 

Jaffe CC (2008) Response assessment in clinical trials: implications for sarcoma clinical trial design. Oncologist 13(Suppl 2):14–18. https://doi.org/10.1634/theoncologist.13-S2-14

Article  Google Scholar 

Tirkes T, Hollar MA, Tann M, Kohli MD, Akisik F, Sandrasegaran K (2013) Response criteria in oncologic imaging: review of traditional and new criteria. Radiographics 33(5):1323–1341. https://doi.org/10.1148/rg.335125214

Article  Google Scholar 

World Health Organization (1979) WHO handbook for reporting results of cancer treatment. World Health Organization, Geneva, SZ

Google Scholar 

Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I (2021) Radiomics in the setting of neoadjuvant radiotherapy: a new approach for tailored treatment. Cancers (Basel) 13(14). https://doi.org/10.3390/cancers13143590

Choi H, Charnsangavej C, Faria SC, Macapinlac HA, Burgess MA, Patel SR, Chen LL, Podoloff DA, Benjamin RS (2007) Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol 25(13):1753–1759. https://doi.org/10.1200/JCO.2006.07.3049

Article  Google Scholar 

Crombe A, Le Loarer F, Cornelis F, Stoeckle E, Buy X, Cousin S, Italiano A, Kind M (2019) High-grade soft-tissue sarcoma: optimizing injection improves MRI evaluation of tumor response. Eur Radiol 29(2):545–555. https://doi.org/10.1007/s00330-018-5635-4

Article  Google Scholar 

Gennaro N, Reijers S, Bruining A, Messiou C, Haas R, Colombo P, Bodalal Z, Beets-Tan R, van Houdt W, van der Graaf WTA (2021) Imaging response evaluation after neoadjuvant treatment in soft tissue sarcomas: where do we stand? Crit Rev Oncol Hematol 160:103309. https://doi.org/10.1016/j.critrevonc.2021.103309

Article  Google Scholar 

Fields BKK, Hwang D, Cen S, Desai B, Gulati M, Hu J, Duddalwar V, Varghese B, Matcuk GR Jr (2020) Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development. Clin Imaging 63:83–93. https://doi.org/10.1016/j.clinimag.2020.02.016

Article  Google Scholar 

Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2(12):1636–1642. https://doi.org/10.1001/jamaoncol.2016.2631

Article  Google Scholar 

Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC (2011) For the Group. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiology 258(3):906–914. https://doi.org/10.1148/radiol.10100799

Article  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169

Article  Google Scholar 

Varghese BA, Cen SY, Hwang DH, Duddalwar VA (2019) Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol 212(3):520–528. https://doi.org/10.2214/AJR.18.20624

Article  Google Scholar 

Hwang DH, Varghese BA, Chang M, Deng C, Ugweze C, Cen SY (2017) Duddalwar VA. Radiomics-based quantitative biomarker discovery: development of a robust image processing infrastructure. Proc SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016017, January 26, 2017. https://doi.org/10.1117/12.2256829

Demirjian NL, Varghese BA, Cen SY, Hwang DH, Aron M, Siddiqui I, Fields BKK, Lei X, Yap FY, Rivas M, Reddy SS, Zahoor H, Liu DH, Desai M, Rhie SK, Gill IS, Duddalwar V (2022) CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur Radiol 32(4):2552–2563. https://doi.org/10.1007/s00330-021-08344-4

Article  Google Scholar 

Fields BKK, Demirjian NL, Hwang DH, Varghese BA, Cen SY, Lei X, Desai B, Duddalwar V, Matcuk GR Jr (2021) Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors. Eur Radiol 31(11):8522–8535. https://doi.org/10.1007/s00330-021-07914-w

Article  CAS  Google Scholar 

Demircioglu A (2021) Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 12(1):172. https://doi.org/10.1186/s13244-021-01115-1

Article  Google Scholar 

Crombe A, Marcellin PJ, Buy X, Stoeckle E, Brouste V, Italiano A, Le Loarer F, Kind M (2019) Soft-tissue sarcomas: assessment of MRI features correlating with histologic grade and patient outcome. Radiology 291(3):710–721. https://doi.org/10.1148/radiol.2019181659

Article  Google Scholar 

Crombe A, Perier C, Kind M, De Senneville BD, Le Loarer F, Italiano A, Buy X, Saut O (2019) T2-based MRI delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J Magn Reson Imaging 50(2):497–510. https://doi.org/10.1002/jmri.26589

Article  Google Scholar 

Peeken JC, Neumann J, Asadpour R, Leonhardt Y, Moreira JR, Hippe DS, Klymenko O, Foreman SC, von Schacky CE, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Woodruff HC, Lambin P, Nyflot MJ, Gersing AS, Combs SE (2021) Prognostic assessment in high-grade soft-tissue sarcoma patients: a comparison of semantic image analysis and radiomics. Cancers (Basel) 13(8). https://doi.org/10.3390/cancers13081929

Corino VDA, Montin E, Messina A, Casali PG, Gronchi A, Marchiano A, Mainardi LT (2018) Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging 47(3):829–840. https://doi.org/10.1002/jmri.25791

Article  Google Scholar 

Vallieres M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60(14):5471–5496. https://doi.org/10.1088/0031-9155/60/14/5471

Article  CAS  Google Scholar 

Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, Hippe DS, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Gersing AS, Woodruff HC, Lambin P, Nyflot MJ, Combs SE (2021) MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol 164:73–82. https://doi.org/10.1016/j.radonc.2021.08.023

Article  Google Scholar 

Baessler B, Weiss K, Pinto Dos Santos D (2019) Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol 54(4):221–228. https://doi.org/10.1097/RLI.0000000000000530

Article  Google Scholar 

Crombe A, Fadli D, Italiano A, Saut O, Buy X, Kind M (2020) Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J Radiol 132:109283. https://doi.org/10.1016/j.ejrad.2020.109283

Article  Google Scholar 

Gao Y, Kalbasi A, Hsu W, Ruan D, Fu J, Shao J, Cao M, Wang C, Eilber FC, Bernthal N, Bukata S, Dry SM, Nelson SD, Kamrava M, Lewis J, Low DA, Steinberg M, Hu P, Yang Y (2020) Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Phys Med Biol 65(17):175006. https://doi.org/10.1088/1361-6560/ab9e58

Article  Google Scholar 

Miao L, Cao Y, Zuo L, Zhang H, Guo C, Yang Z, Shi Z, Jiang J, Wang S, Li Y, Wang Y, Xie L, Li M, Lu N (2022) Predicting pathological complete response of neoadjuvant radiotherapy and targeted therapy for soft tissue sarcoma by whole-tumor texture analysis of multisequence MRI imaging. Eur Radiol. https://doi.org/10.1007/s00330-022-09362-6

Article  Google Scholar 

O’Neil C, Schutt R (2014) Doing data science: straight talk from the frontline, 1st edn. O’Reilly Media, Sebastopol, CA

Google Scholar 

Friston K, Ashburner J, Kiebel S, Nichols T, Penny W (2007) eds. Statistical parametric mapping: the analysis of functional brain images. 1st ed. London, UK: Academic Press, https://doi.org/10.1016/b978-0-12-372560-8.X5000-1

Lei M, Varghese B, Hwang D, Cen S, Lei X, Desai B, Azadikhah A, Oberai A, Duddalwar V (2021) Benchmarking Various radiomic toolkit features while applying the image biomarker standardization initiative toward clinical translation of radiomic analysis. J Digit Imaging 34(5):1156–1170. https://doi.org/10.1007/s10278-021-00506-6

Article  Google Scholar 

Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc: Ser B (Methodol) 57(1):289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

Article  Google Scholar 

Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann Stat 28(2). https://doi.org/10.1214/aos/1016218223

Corso F, Tini G, Lo Presti G, Garau N, De Angelis SP, Bellerba F, Rinaldi L, Botta F, Rizzo S, Origgi D, Paganelli C, Cremonesi M, Rampinelli C, Bellomi M, Mazzarella L, Pelicci PG, Gandini S, Raimondi S (2021) The challenge of choosing the best classification method in radiomic analyses: recommendations and applications to lung cancer CT images. Cancers (Basel) 13(12). https://doi.org/10.3390/cancers13123088

Gu Q, Feng Z, Liang Q, Li M, Deng J, Ma M, Wang W, Liu J, Liu P, Rong P (2019) Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol 118:32–37. https://doi.org/10.1016/j.ejrad.2019.06.025

Article  Google Scholar 

Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087. https://doi.org/10.1038/srep13087

Article  CAS  Google Scholar 

Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, Wilkens JJ, Combs SE, Nusslin F (2018) Radiomics in radiooncology - challenging the m

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