Gutierrez JC, Perez EA, Franceschi D, Moffat FL, Livingstone AS, Koniaris LG. Outcomes for soft-tissue sarcoma in 8249 cases from a large state cancer registry. J Surg Res. 2007;141:105–14.

Article  PubMed  Google Scholar 

Amin MB, Edge SB, Greene FL, Byrd DR, Brookland RK, Washington MK, et al. AJCC cancer staging manual. Springer International Publishing; 2018.

Vibhakar AM, Cassels JA, Botchu R, Rennie WJ, Shah A. Imaging update on soft tissue sarcoma. J Clin Orthop Trauma. 2021;22: 101568.

Article  PubMed  PubMed Central  Google Scholar 

Peeken JC, Nüsslin F, Combs SE. Radio-oncomics. Strahlenther Onkol. 2017;193:767–79.

Article  PubMed  Google Scholar 

Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, et al. Radiomics in radiooncology—challenging the medical physicist. Phys Med Eur J Med Phys. 2018;48:27–36.

Google Scholar 

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.

Article  PubMed  PubMed Central  Google Scholar 

Fanciullo C, Gitto S, Carlicchi E, Albano D, Messina C, Sconfienza LM. Radiomics of musculoskeletal sarcomas: a narrative review. J Imaging Sci Technol. 2022. https://doi.org/10.3390/jimaging8020045.

Article  Google Scholar 

Crombé A, Roulleau-Dugage M, Italiano A. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. Cancer Commun. 2022;42:1288–313.

Article  Google Scholar 

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;60:5471–96.

Article  PubMed  Google Scholar 

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.

Article  PubMed  PubMed Central  Google Scholar 

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.

Article  PubMed  PubMed Central  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.

Article  PubMed  Google Scholar 

Liang H-Y, Yang S-F, Zou H-M, Hou F, Duan L-S, Huang C-C, et al. Deep learning radiomics nomogram to predict lung metastasis in soft-tissue sarcoma: a multi-center study. Front Oncol. 2022;12: 897676.

Article  PubMed  PubMed Central  Google Scholar 

Hu Y, Wang H, Yue Z, Wang X, Wang Y, Luo Y, et al. A contrast-enhanced MRI-based nomogram to identify lung metastasis in soft-tissue sarcoma: A multi-centre study. Med Phys. 2022. https://doi.org/10.1002/mp.16136.

Article  PubMed  Google Scholar 

Escobar T, Vauclin S, Orlhac F, Nioche C, Pineau P, Champion L, et al. Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns. Med Phys. 2022;49:3816–29.

Article  PubMed  Google Scholar 

Vallières M, Laberge S, Diamant A, El Naqa I. Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. Phys Med Biol. 2017;62:8536–65.

Article  PubMed  Google Scholar 

Sheen H, Shin H-B, Kim JY. Comparison of radiomics prediction models for lung metastases according to four semiautomatic segmentation methods in soft-tissue sarcomas of the extremities. J Korean Phys Soc. 2022;80:247–56.

Article  CAS  Google Scholar 

Deng J, Zeng W, Shi Y, Kong W, Guo S. Fusion of FDG-PET image and clinical features for prediction of lung metastasis in soft tissue sarcomas. Comput Math Methods Med. 2020;2020:8153295.

Article  PubMed  PubMed Central  Google Scholar 

Tian L, Zhang D, Bao S, Nie P, Hao D, Liu Y, et al. Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas. Clin Radiol. 2021;76(158):e19-158.e25.

Google Scholar 

Giraudo C, Fichera G, Del Fiore P, Mocellin S, Brunello A, Rastrelli M, et al. Tumor cellularity beyond the visible in soft tissue sarcomas: Results of an ADC-based, single center, and preliminary radiomics study. Front Oncol. 2022;12: 879553.

Article  PubMed  PubMed Central  Google Scholar 

Crombé A, Fadli D, Buy X, Italiano A, Saut O, Kind M. High-grade soft-tissue sarcomas: can optimizing dynamic contrast-enhanced mri postprocessing improve prognostic radiomics models? J Magn Reson Imaging. 2020;52:282–97.

Article  PubMed  Google Scholar 

Yang Y, Ma X, Wang Y, Ding X. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest. Updates Surg. 2022;74:355–65.

Article  PubMed  Google Scholar 

González-Viguera J, Reynés-Llompart G, Lozano A. Outcomes and computed tomography radiomic features extraction in soft tissue sarcomas treated with neoadjuvant radiation therapy. Rep Pract Oncol Radiother. 2021;26:804–13.

Article  PubMed  PubMed Central  Google Scholar 

Peeken JC, Neumann J, Asadpour R, Leonhardt Y, Moreira JR, Hippe DS, et al. Prognostic assessment in high-grade soft-tissue sarcoma patients: a comparison of semantic image analysis and radiomics. Cancers. 2021. https://doi.org/10.3390/cancers13081929.

Article  PubMed  PubMed Central  Google Scholar 

Yang Y, Zhou Y, Zhou C, Zhang X, Ma X. MRI-based computer-aided diagnostic model to predict tumor grading and clinical outcomes in patients with soft tissue sarcoma. J Magn Reson Imaging. 2022;56:1733–45.

Article  PubMed  Google Scholar 

Spraker MB, Wootton LS, Hippe DS, Ball KC, Peeken JC, Macomber MW, et al. MRI radiomic features are independently associated with overall survival in soft tissue sarcoma. Adv Radiat Oncol. 2019;4:413–21.

Article  PubMed  PubMed Central  Google Scholar 

Annovazzi A, Ferraresi V, Covello R, Ascione A, Vari S, Petrongari MG, et al. Prognostic value of pre-treatment [18F]FDG PET/CT texture analysis in undifferentiated soft-tissue sarcoma. J Clin Med Res. 2022. https://doi.org/10.3390/jcm12010279.

Article  Google Scholar 

Fadli D, Kind M, Michot A, Le Loarer F, Crombé A. Natural changes in radiological and radiomics features on mris of soft-tissue sarcomas naïve of treatment: correlations with histology and patients’ outcomes. J Magn Reson Imaging. 2022;56:77–96.

Article  PubMed  Google Scholar 

Chen S, Li N, Tang Y, Chen B, Fang H, Qi S, et al. Radiomics analysis of fat-saturated T2-weighted MRI sequences for the prediction of prognosis in soft tissue sarcoma of the extremities and trunk treated with neoadjuvant radiotherapy. Front Oncol. 2021;11: 710649.

Article  PubMed  PubMed Central  Google Scholar 

Lee S, Jung J-Y, Nam Y, Jung C-K, Lee S-Y, Lee J, et al. Diagnosis of marginal infiltration in soft tissue sarcoma by radiomics approach using T2-weighted dixon sequence. J Magn Reson Imaging. 2023;57:752–60.

Article  PubMed  Google Scholar 

Zhao W, Huang X, Wang G, Guo J. PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma. Cancer Imaging. 2022;22:7.

Article  PubMed  PubMed Central  Google Scholar 

Tagliafico AS, Bignotti B, Rossi F, Valdora F, Martinoli C. Local recurrence of soft tissue sarcoma: a radiomic analysis. Radiol Oncol. 2019;53:300–6.

Article  PubMed  PubMed Central  Google Scholar 

Liu S, Sun W, Yang S, Duan L, Huang C, Xu J, et al. Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. Eur Radiol. 2022;32:793–805.

Article  PubMed  Google Scholar 

Casale R, Varriano G, Santone A, Messina C, Casale C, Gitto S, et al. Predicting risk of metastases and recurrence in soft-tissue sarcomas via Radiomics and Formal Methods. JAMIA Open. 2023;6:ooad025.

Article  PubMed  PubMed Central  Google Scholar 

Peeken JC, Spraker MB, Knebel C, Dapper H, Pfeiffer D, Devecka M, et al. Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine. 2019;48:332–40.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang Y, Zhu Y, Shi X, Tao J, Cui J, Dai Y, et al. Soft tissue sarcomas: preoperative predictive histopathological grading based on radiomics of MRI. Acad Radiol. 2019;26:1262–8.

Article  PubMed  Google Scholar 

Xu W, Hao D, Hou F, Zhang D, Wang H. Soft tissue sarcoma: preoperative mri-based radiomics and machine learning may be accurate predictors of histopathologic grade. AJR Am J Roentgenol. 2020;215:963–9.

Article  PubMed  Google Scholar 

Yang Y, Zhang L, Wang T, Jiang Z, Li Q, Wu Y, et al. MRI fat-saturated T2-weighted radiomics model for identifying the Ki-67 index of soft tissue sarcomas. J Magn Reson Imaging. 2022. https://doi.org/10.1002/jmri.28518.

Article  PubMed  PubMed Central  Google Scholar 

Yan R, Hao D, Li J, Liu J, Hou F, Chen H, et al. Magnetic resonance imaging-based radiomics nomogram for prediction of the histopathological grade of soft tissue sarcomas: a two-center study. J Magn Reson Imaging. 2021;53:1683–96.

Article  PubMed  Google Scholar 

Wang H, Chen H, Duan S, Hao D, Liu J. Radiomics and machine learning with multiparametric preoperative mri may accurately predict the histopathological grades of soft tissue sarcomas. J Magn Reson Imaging. 2020;51:791–7.