Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
366-03-001 ISMRM Abstract

Intratumoral Habitat and Peritumor Radiomics for Progression Risk Stratification of Patients with Soft Tissue Sarcoma

Primary: Analysis Methods - Radiomics
Secondary: Musculoskeletal - Other Musculoskeletal
366-03-001 · Radiomics: Body · Monday, 11 May, 1:50 PM–2:45 PM · Digital Posters Row G
Keywords: Habitat radiomics Soft Tissue Sarcoma Progression Risk Stratification
Accepted
Haoyu Liang 1, Mengxiao Liu2, Yang Song3
1Huashan Hospital, Fudan University, China
2MR Research Collaboration Team, Diagnostic Imaging, Siemens Healthineers Ltd, Shanghai, China
3MR Research Collaboration Team, Siemens Healthineers Ltd. Shanghai, Shanghai, China
Presenting Author: Haoyu Liang

Synopsis

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References

1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin 2012;62(1):10-29. doi: 10.3322/caac.20138 [doi]
2. Hansen T, Katenkamp K, Brodhun M, Katenkamp D. Low-grade fibrosarcoma--report on 39 not otherwise specified cases and comparison with defined low-grade fibrosarcoma types. Histopathology 2006;49(2):152-160. doi: 10.1111/j.1365-2559.2006.02480.x [doi]
3. Tan MC, Brennan MF, Kuk D, Agaram NP, Antonescu CR, Qin LX, Moraco N, Crago AM, Singer S. Histology-based Classification Predicts Pattern of Recurrence and Improves Risk Stratification in Primary Retroperitoneal Sarcoma. Ann Surg 2016;263(3):593-600. doi: 10.1097/sla.0000000000001149 [doi]
4. MacDermed DM, Miller LL, Peabody TD, Simon MA, Luu HH, Haydon RC, Montag AG, Undevia SD, Connell PP. Primary tumor necrosis predicts distant control in locally advanced soft-tissue sarcomas after preoperative concurrent chemoradiotherapy. Int J Radiat Oncol Biol Phys 2010;76(4):1147-1153. doi: 10.1016/j.ijrobp.2009.03.015 [doi]
5. Liu S, Sun W, Yang S, Duan L, Huang C, Xu J, Hou F, Hao D, Yu T, Wang H. Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. Eur Radiol 2022;32(2):793-805. doi: 10.1007/s00330-021-08221-0 [doi]
6. Tagliafico AS, Bignotti B, Rossi F, Valdora F, Martinoli C. Local recurrence of soft tissue sarcoma: a radiomic analysis. Radiol Oncol 2019;53(3):300-306. doi: 10.2478/raon-2019-0041 [doi]
7. Vallières M, Freeman CR, Skamene SR, El Naqa I. 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 2015;60(14):5471-5496. doi: 10.1088/0031-9155/60/14/5471 [doi]
8. Spraker MB, Wootton LS, Hippe DS, Ball KC, Peeken JC, Macomber MW, Chapman TR, Hoff MN, Kim EY, Pollack SM, Combs SE, Nyflot MJ. MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma. Adv Radiat Oncol 2019;4(2):413-421. doi: 10.1016/j.adro.2019.02.003 [doi]
9. Crombé A, Périer C, Kind M, De Senneville BD, Le Loarer F, Italiano A, Buy X, Saut O. T(2) -based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J Magn Reson Imaging 2019;50(2):497-510. doi: 10.1002/jmri.26589 [doi]
10. Gatenby RA, Grove O, Gillies RJ. Quantitative imaging in cancer evolution and ecology. Radiology 2013;269(1):8-15. doi: 10.1148/radiol.13122697 [doi]
11. Gu Y, She Y, Xie D, Dai C, Ren Y, Fan Z, Zhu H, Sun X, Xie H, Jiang G, Chen C. A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma. Ann Thorac Surg 2018;106(1):214-220. doi: 10.1016/j.athoracsur.2018.02.026 [doi]
12. Zhong Y, Yuan M, Zhang T, Zhang YD, Li H, Yu TF. Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma. AJR Am J Roentgenol 2018;211(1):109-113. doi: 10.2214/ajr.17.19074 [doi]
13. Even AJG, Reymen B, La Fontaine MD, Das M, Mottaghy FM, Belderbos JSA, De Ruysscher D, Lambin P, van Elmpt W. Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer. Radiother Oncol 2017;125(3):379-384. doi: 10.1016/j.radonc.2017.09.041 [doi]
14. Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw Open 2019;2(4):e192561. doi: 10.1001/jamanetworkopen.2019.2561 [doi]
15. Wang T, She Y, Yang Y, Liu X, Chen S, Zhong Y, Deng J, Zhao M, Sun X, Xie D, Chen C. Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non-Small Cell Lung Cancer. Radiology 2022;302(2):425-434. doi: 10.1148/radiol.2021210109 [doi]
16. Schröder MS, Culhane AC, Quackenbush J, Haibe-Kains B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 2011;27(22):3206-3208. doi: 10.1093/bioinformatics/btr511 [doi]
17. Hu Y, Xie C, Yang H, Ho JWK, Wen J, Han L, Chiu KWH, Fu J, Vardhanabhuti V. Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma. JAMA Netw Open 2020;3(9):e2015927. doi: 10.1001/jamanetworkopen.2020.15927 [doi]

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http://echo.ismrm.org/p/ISMRM2026/366-03-001