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Open Access 01-12-2024 | Soft Tissue Sarcoma | Review

The impact of radiomics in the management of soft tissue sarcoma

Authors: Riccardo De Angelis, Roberto Casale, Nicolas Coquelet, Samia Ikhlef, Ayoub Mokhtari, Paolo Simoni, Maria Antonietta Bali

Published in: Discover Oncology | Issue 1/2024

Abstract

Introduction

Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation.

Materials and methods

We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: ‘radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])’. Only original articles, referring to humans, were included.

Results

A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response.

Conclusions

With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.

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.PubMedCrossRef

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.PubMedPubMedCentralCrossRef

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

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.

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.PubMedPubMedCentralCrossRef

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.CrossRef

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.CrossRef

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.PubMedCrossRef

10.

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.PubMedPubMedCentralCrossRef

11.

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.PubMedPubMedCentralCrossRef

12.

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.PubMedCrossRef

13.

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.PubMedPubMedCentralCrossRef

14.

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.CrossRefPubMed

15.

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.PubMedCrossRef

16.

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.PubMedCrossRef

17.

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.CrossRef

18.

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.PubMedPubMedCentralCrossRef

19.

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.

20.

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.PubMedPubMedCentralCrossRef

21.

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.PubMedCrossRef

22.

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.PubMedCrossRef

23.

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.PubMedPubMedCentralCrossRef

24.

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.CrossRefPubMedPubMedCentral

25.

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.PubMedCrossRef

26.

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.PubMedPubMedCentralCrossRef

27.

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.CrossRef

28.

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.PubMedCrossRef

29.

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.PubMedPubMedCentralCrossRef

30.

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.PubMedCrossRef

31.

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.PubMedPubMedCentralCrossRef

32.

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.PubMedPubMedCentralCrossRef

33.

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.PubMedCrossRef

34.

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.PubMedPubMedCentralCrossRef

35.

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.PubMedPubMedCentralCrossRef

36.

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.PubMedCrossRef

37.

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.PubMedCrossRef

38.

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.CrossRefPubMedPubMedCentral

39.

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.PubMedCrossRef

40.

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.PubMedCrossRef

41.

Liu X, Guo L, Wang H, Guo J, Yang S, Duan L. Research on imbalance machine learning methods for MR[Formula: see text]WI soft tissue sarcoma data. BMC Med Imaging. 2022;22:149.PubMedPubMedCentralCrossRef

42.

Corino VDA, Montin E, Messina A, Casali PG, Gronchi A, Marchianò A, et al. Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging. 2018;47:829–40.PubMedCrossRef

43.

Zhu Y-F, Li Y-S, Zhang Y, Liu Y-J, Zhang Y-N, Tao J, et al. Radiomics model based on intravoxel incoherent motion and diffusion kurtosis imaging for predicting histopathological grade and Ki-67 expression level of soft tissue sarcomas. Acta Radiol. 2023; 2841851231179933.

44.

Peeken JC, Bernhofer M, Spraker MB, Pfeiffer D, Devecka M, Thamer A, et al. CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy. Radiother Oncol. 2019;135:187–96.PubMedCrossRef

45.

Giraudo C, Fichera G, Stramare R, Bisogno G, Motta R, Evangelista L, et al. Radiomic features as biomarkers of soft tissue paediatric sarcomas: preliminary results of a PET/MR study. Radiol Oncol. 2022;56:138–41.PubMedPubMedCentralCrossRef

46.

Crombé A, Bertolo F, Fadli D, Kind M, Le Loarer F, Perret R, et al. Distinct patterns of the natural evolution of soft tissue sarcomas on pre-treatment MRIs captured with delta-radiomics correlate with gene expression profiles. Eur Radiol. 2023;33:1205–18.PubMedCrossRef

47.

Timbergen MJM, Starmans MPA, Padmos GA, Grünhagen DJ, van Leenders GJLH, Hanff DF, et al. Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics. Eur J Radiol. 2020;131: 109266.PubMedCrossRef

48.

Foreman SC, Llorián-Salvador O, David DE, Rösner VKN, Rischewski JF, Feuerriegel GC, et al. Development and evaluation of MR-based radiogenomic models to differentiate atypical lipomatous tumors from lipomas. Cancers. 2023. https://doi.org/10.3390/cancers15072150.CrossRefPubMedPubMedCentral

49.

Miao L, Ma S-T, Jiang X, Zhang H-H, Wang Y-M, Li M. Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features. BMC Med Imaging. 2022;22:131.PubMedPubMedCentralCrossRef

50.

Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, et al. MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol. 2021;164:73–82.PubMedCrossRef

51.

Fields BKK, Demirjian NL, Cen SY, Varghese BA, Hwang DH, Lei X, et al. Predicting soft tissue sarcoma response to neoadjuvant chemotherapy using an mri-based delta-radiomics approach. Mol Imaging Biol. 2023. https://doi.org/10.1007/s11307-023-01803-y.CrossRefPubMedPubMedCentral

52.

Crombé A, Périer C, Kind M, De Senneville BD, Le Loarer F, Italiano A, et al. T -based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J Magn Reson Imaging. 2019;50:497–510.PubMedCrossRef

53.

Tomaszewski MR, Fan S, Garcia A, Qi J, Kim Y, Gatenby RA, et al. AI-radiomics can improve inclusion criteria and clinical trial performance. Tomography. 2022;8:341–55.PubMedPubMedCentralCrossRef

54.

Gao Y, Kalbasi A, Hsu W, Ruan D, Fu J, Shao J, et al. Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Phys Med Biol. 2020;65: 175006.PubMedCrossRef

55.

Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE-MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging. 2019;50:1773–88.PubMedCrossRef

56.

Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38.PubMedCrossRef

57.

Bologna M, Corino VDA, Montin E, Messina A, Calareso G, Greco FG, et al. Assessment of stability and discrimination capacity of radiomic features on apparent diffusion coefficient images. J Digit Imaging. 2018;31:879–94.PubMedPubMedCentralCrossRef

58.

Thrussell I, Winfield JM, Orton MR, Miah AB, Zaidi SH, Arthur A, et al. Radiomic features from diffusion-weighted MRI of retroperitoneal soft-tissue sarcomas are repeatable and exhibit change after radiotherapy. Front Oncol. 2022;12: 899180.PubMedPubMedCentralCrossRef

59.

Yue Z, Wang X, Wang Y, Wang H, Jiang W. Clinical-radiomics nomogram from T1W, T1CE, and T2FS MRI for improving diagnosis of soft-tissue sarcoma. Mol Imaging Biol. 2022;24:995–1006.PubMedCrossRef

60.

Aouadi S, Torfeh T, Arunachalam Y, Paloor S, Riyas M, Hammoud R, et al. Investigation of radiomics and deep convolutional neural networks approaches for glioma grading. Biomed Phys Eng Express. 2023. https://doi.org/10.1088/2057-1976/acc33a.CrossRefPubMed

61.

Starmans MPA, Timbergen MJM, Vos M, Padmos GA, Grünhagen DJ, Verhoef C, et al. The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies. BioRxiv. 2021. https://doi.org/10.1101/2021.08.19.21262238.CrossRef

62.

Sedaghat S, Ravesh MS, Sedaghat M, Both M, Jansen O. Configuration of soft-tissue sarcoma on MRI correlates with grade of malignancy. Radiol Oncol. 2021;55:158–63.PubMedPubMedCentralCrossRef

63.

Sedaghat S, Salehi Ravesh M, Sedaghat M, Meschede J, Jansen O, Both M. Does the primary soft-tissue sarcoma configuration predict configuration of recurrent tumors on magnetic resonance imaging? Acta Radiol. 2022;63:642–51.PubMedCrossRef

64.

Sedaghat S, Sedaghat M, Meschede J, Jansen O, Both M. Diagnostic value of MRI for detecting recurrent soft-tissue sarcoma in a long-term analysis at a multidisciplinary sarcoma center. BMC Cancer. 2021. https://doi.org/10.1186/s12885-021-08113-y.CrossRefPubMedPubMedCentral

65.

Sedaghat S, Schmitz F, Meschede J, Sedaghat M. Systematic analysis of post-treatment soft-tissue edema and seroma on MRI in 177 sarcoma patients. Surg Oncol. 2020;35:218–23.PubMedCrossRef

66.

Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, et al. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol. 2021;31:1526–35.PubMedCrossRef

67.

Spadarella G, Stanzione A, Akinci D’Antonoli T, Andreychenko A, Fanni SC, Ugga L, et al. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol. 2023;33:1884–94.PubMedCrossRef

68.

Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix A-N, et al. A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev. 2022;42:426–40.PubMedCrossRef

Title: The impact of radiomics in the management of soft tissue sarcoma
Authors: Riccardo De Angelis
Roberto Casale
Nicolas Coquelet
Samia Ikhlef
Ayoub Mokhtari
Paolo Simoni
Maria Antonietta Bali
Publication date: 01-12-2024
Publisher: Springer US
Keywords: Soft Tissue Sarcoma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: Discover Oncology / Issue 1/2024
Print ISSN: 1868-8497
Electronic ISSN: 2730-6011
DOI: https://doi.org/10.1007/s12672-024-00908-2

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