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Semin Musculoskelet Radiol 2024; 28(01): 049-061
DOI: 10.1055/s-0043-1776428
Review Article

Radiomics in Musculoskeletal Tumors

Daniel Brandenberger 
1   Department of Medical Imaging, Musculoskeletal Imaging, University of Toronto, Toronto, Ontario, Canada
2   Institut für Radiologie und Nuklearmedizin, Kantonsspital Baselland, Liestal, Switzerland
3   Toronto Joint Department of Medical Imaging, University Health Network, Sinai Health System, and Women's College Hospital, Mount Sinai Hospital, Toronto, Ontario, Canada
,
Lawrence M. White 
1   Department of Medical Imaging, Musculoskeletal Imaging, University of Toronto, Toronto, Ontario, Canada
3   Toronto Joint Department of Medical Imaging, University Health Network, Sinai Health System, and Women's College Hospital, Mount Sinai Hospital, Toronto, Ontario, Canada
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Abstract

Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

Keywords

sarcoma - radiomics - quantitative imaging - machine learning - artificial intelligence


Publication History

Article published online:
08 February 2024

© 2024. Thieme. All rights reserved.

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  • References

  • 1 WHO Classification of Tumours Editorial Board. Soft Tissue and Bone Tumours. 5th ed.. Lyon, France: IARC Publications; 2020
    Google Scholar
  • 2 Damerell V, Pepper MS, Prince S. Molecular mechanisms underpinning sarcomas and implications for current and future therapy. Signal Transduct Target Ther 2021; 6 (01) 246
    CrossrefPubMedGoogle Scholar
  • 3 National Institutes of Health, National Cancer Institute. Rare cancer. Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/rare-cancer . Accessed June 1, 2023
    PubMedGoogle Scholar
  • 4 National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Bone and Joint Cancer. Available at: https://seer.cancer.gov/statfacts/html/bones.html . Accessed May 23, 2023
    PubMedGoogle Scholar
  • 5 National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Soft Tissue Cancer. Available at: https://seer.cancer.gov/statfacts/html/soft.html . Accessed May 23, 2023
    PubMedGoogle Scholar
  • 6 National Institutes of Health, National Cancer Institute. Cancer Statistics Explorer Network, SEER*Explorer, 2023. Available at: https://seer.cancer.gov/statistics-network/explorer/ . Accessed May 23, 2023
    PubMedGoogle Scholar
  • 7 Raut CP, George S, Hornick JL. et al. High rates of histopathologic discordance in sarcoma with implications for clinical care. JCO 2011; 29 (15, Suppl): 10065-10065
    CrossrefPubMedGoogle Scholar
  • 8 Blay JY, Kang YK, Nishida T, von Mehren M. Gastrointestinal stromal tumours. Nat Rev Dis Primers 2021; 7 (01) 22
    CrossrefPubMedGoogle Scholar
  • 9 Van der Graaf WTA, Tesselaar MET, McVeigh TP, Oyen WJG, Fröhling S. Biology-guided precision medicine in rare cancers: Lessons from sarcomas and neuroendocrine tumours. Semin Cancer Biol 2022; 84: 228-241
    CrossrefPubMedGoogle Scholar
  • 10 Pillozzi S, Bernini A, Palchetti I. et al. Soft tissue sarcoma: an insight on biomarkers at molecular, metabolic and cellular level. Cancers (Basel) 2021; 13 (12) 3044
    CrossrefPubMedGoogle Scholar
  • 11 Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001; 69 (03) 89-95
    CrossrefPubMedGoogle Scholar
  • 12 Grünewald TG, Alonso M, Avnet S. et al. Sarcoma treatment in the era of molecular medicine. EMBO Mol Med 2020; 12 (11) e11131
    CrossrefPubMedGoogle Scholar
  • 13 Howe BM, Broski SM, Littrell LA, Pepin KM, Wenger DE. Quantitative musculoskeletal tumor imaging. Semin Musculoskelet Radiol 2020; 24 (04) 428-440
    Article in Thieme ConnectPubMedGoogle Scholar
  • 14 Engesæter IØ, Laborie LB, Lehmann TG. et al. Radiological findings for hip dysplasia at skeletal maturity. Validation of digital and manual measurement techniques. Skeletal Radiol 2012; 41 (07) 775-785
    CrossrefPubMedGoogle Scholar
  • 15 Weinstein SL, Dolan LA, Wright JG, Dobbs MB. Effects of bracing in adolescents with idiopathic scoliosis. N Engl J Med 2013; 369 (16) 1512-1521
    CrossrefPubMedGoogle Scholar
  • 16 ESR EIBALL Subcommittee. Biomarkers Inventory. Available at: https://www.myesr.org/research/biomarkers-inventory . Accessed September 6, 2023.
    PubMedGoogle Scholar
  • 17 Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M. Systematic review of sarcomas radiomics studies: bridging the gap between concepts and clinical applications?. Eur J Radiol 2020; 132: 109283
    CrossrefPubMedGoogle Scholar
  • 18 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
    CrossrefPubMedGoogle Scholar
  • 19 Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (02) 563-577
    CrossrefPubMedGoogle Scholar
  • 20 Van Griethuysen JJM, Fedorov A, Parmar C. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017; 77 (21) e104-e107
    CrossrefPubMedGoogle Scholar
  • 21 Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Trans Syst, Man, Cybern 1973; ;SMC-3(6): 610-621 . doi:10.1109/TSMC.1973.4309314
    CrossrefPubMedGoogle Scholar
  • 22 Lambin P, Rios-Velazquez E, Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48 (04) 441-446
    CrossrefPubMedGoogle Scholar
  • 23 Van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging 2020; 11 (01) 91
    CrossrefPubMedGoogle Scholar
  • 24 Xu L, Yang P, Yen EA. et al. A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis. Phys Med Biol 2019; 64 (21) 215009
    CrossrefPubMedGoogle Scholar
  • 25 Gitto S, Corino VDA, Annovazzi A. et al. 3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction. Front Oncol 2022; 12: 1016123
    CrossrefPubMedGoogle Scholar
  • 26 Gitto S, Cuocolo R, Emili I. et al. Effects of interobserver variability on 2D and 3D CT- and MRI-based texture feature reproducibility of cartilaginous bone tumors. J Digit Imaging 2021; 34 (04) 820-832
    CrossrefPubMedGoogle Scholar
  • 27 Sudjai N, Siriwanarangsun P, Lektrakul N. et al. Robustness of radiomic features: two-dimensional versus three-dimensional MRI-based feature reproducibility in lipomatous soft-tissue tumors. Diagnostics (Basel) 2023; 13 (02) 258
    CrossrefPubMedGoogle Scholar
  • 28 Park SH, Lim H, Bae BK. et al. Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer. Cancer Imaging 2021; 21 (01) 19
    CrossrefPubMedGoogle Scholar
  • 29 McCague C, Ramlee S, Reinius M. et al. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78 (02) 83-98
    CrossrefPubMedGoogle Scholar
  • 30 Chen H, Zhang X, Wang X. et al. MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol 2021; 31 (10) 7913-7924
    CrossrefPubMedGoogle Scholar
  • 31 White LM, Atinga A, Naraghi AM. et al. T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma: predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival. Skeletal Radiol 2023; 52 (03) 553-564
    CrossrefPubMedGoogle Scholar
  • 32 Chen H, Liu J, Cheng Z. et al. Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: a retrospective multicenter study. Eur J Radiol 2020; 129: 109066
    CrossrefPubMedGoogle Scholar
  • 33 Eisenhauer EA, Therasse P, Bogaerts J. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45 (02) 228-247
    CrossrefPubMedGoogle Scholar
  • 34 Peeken JC, Asadpour R, Specht K. 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
    CrossrefPubMedGoogle Scholar
  • 35 Crombé A, Périer C, Kind M. et al. T2 -based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy. J Magn Reson Imaging 2019; 50 (02) 497-510
    CrossrefPubMedGoogle Scholar
  • 36 Fields BKK, Demirjian NL, Cen SY. et al. Predicting soft tissue sarcoma response to neoadjuvant chemotherapy using an MRI-based delta-radiomics approach. Mol Imaging Biol 2023; 25 (04) 776-787
    CrossrefPubMedGoogle Scholar
  • 37 Yan R, Hao D, Li J. 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 (06) 1683-1696
    CrossrefPubMedGoogle Scholar
  • 38 Gitto S, Cuocolo R, Annovazzi A. et al. CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas. EbioMedicine 2021; 68: 103407
    CrossrefPubMedGoogle Scholar
  • 39 Gitto S, Cuocolo R, van Langevelde K. et al. MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones. EbioMedicine 2022; 75: 103757
    CrossrefPubMedGoogle Scholar
  • 40 Wang H, Zhang J, Bao S. et al. Preoperative MRI-based radiomic machine-learning nomogram may accurately distinguish between benign and malignant soft-tissue lesions: a two-center study. J Magn Reson Imaging 2020; 52 (03) 873-882
    CrossrefPubMedGoogle Scholar
  • 41 Yin P, Mao N, Zhao C. et al. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 2019; 29 (04) 1841-1847
    CrossrefPubMedGoogle Scholar
  • 42 Yin P, Mao N, Wang S, Sun C, Hong N. Clinical-radiomics nomograms for pre-operative differentiation of sacral chordoma and sacral giant cell tumor based on 3D computed tomography and multiparametric magnetic resonance imaging. Br J Radiol 2019; 92 (1101) 20190155
    CrossrefPubMedGoogle Scholar
  • 43 Nie P, Zhao X, Wang N. et al. A computed tomography radiomics nomogram in differentiating chordoma from giant cell tumor in the axial skeleton. J Comput Assist Tomogr 2023; 47 (03) 453-459
    CrossrefPubMedGoogle Scholar
  • 44 Clark K, Vendt B, Smith K. et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26 (06) 1045-1057
    CrossrefPubMedGoogle Scholar
  • 45 Peeken JC, Bernhofer M, Spraker MB. 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: 187-196
    CrossrefPubMedGoogle Scholar
  • 46 Peng Y, Bi L, Guo Y, Feng D, Fulham M, Kim J. Deep multi-modality collaborative learning for distant metastases predication in PET-CT soft-tissue sarcoma studies. Ann Int Conf IEEE Eng Med Biol Soc 2019; 2019: 3658-3688
    PubMedGoogle Scholar
  • 47 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
    CrossrefPubMedGoogle Scholar
  • 48 Peng Y, Bi L, Kumar A, Fulham M, Feng D, Kim J. Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning. Phys Med Biol 2021;66(24):
    PubMedGoogle Scholar
  • 49 Escobar T, Vauclin S, Orlhac F. et al. Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns. Med Phys 2022; 49 (06) 3816-3829
    CrossrefPubMedGoogle Scholar
  • 50 Segmenting Soft Tissue Sarcomas. A challenge to automate tumor segmentation. Available at: https://www.kaggle.com/datasets/4quant/soft-tissue-sarcoma . Accessed June 6, 2023
    PubMedGoogle Scholar
  • 51 Zwanenburg A, Vallières M, Abdalah MA. et al. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020; 295 (02) 328-338
    CrossrefPubMedGoogle Scholar
  • 52 Hu Y, Wang H, Yue Z. et al. A contrast-enhanced MRI-based nomogram to identify lung metastasis in soft-tissue sarcoma: a multi-centre study. Med Phys 2023; 50 (05) 2961-2970
    CrossrefPubMedGoogle Scholar
  • 53 Luo Z, Li J, Liao Y, Liu R, Shen X, Chen W. Radiomics analysis of multiparametric MRI for prediction of synchronous lung metastases in osteosarcoma. Front Oncol 2022; 12: 802234
    CrossrefPubMedGoogle Scholar
  • 54 Pereira HM, Leite Duarte ME, Ribeiro Damasceno I, de Oliveira Moura Santos LA, Nogueira-Barbosa MH. Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma. Br J Radiol 2021; 94 (1124) 20201391
    CrossrefPubMedGoogle Scholar
  • 55 Yin P, Zhong J, Liu Y. et al. Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma. BMC Med Imaging 2023; 23 (01) 40
    CrossrefPubMedGoogle Scholar
  • 56 Yang Y, Zhang L, Wang T. et al. MRI fat-saturated T2-weighted radiomics model for identifying the Ki-67 index of soft tissue sarcomas. J Magn Reson Imaging 2023; 58 (02) 534-545
    CrossrefPubMedGoogle Scholar
  • 57 Peeken JC, Neumann J, Asadpour R. et al. Prognostic assessment in high-grade soft-tissue sarcoma patients: a comparison of semantic image analysis and radiomics. Cancers (Basel) 2021; 13 (08) 1929
    CrossrefPubMedGoogle Scholar
  • 58 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 (06) 1733-1745
    CrossrefPubMedGoogle Scholar
  • 59 Liu J, Lian T, Chen H. et al. Pretreatment prediction of relapse risk in patients with osteosarcoma using radiomics nomogram based on CT: a retrospective multicenter study. BioMed Res Int 2021; 2021: 6674471
    PubMedGoogle Scholar
  • 60 Wan Y, Yang P, Xu L. et al. Radiomics analysis combining unsupervised learning and handcrafted features: a multiple-disease study. Med Phys 2021; 48 (11) 7003-7015
    CrossrefPubMedGoogle Scholar
  • 61 Zhao S, Su Y, Duan J. et al. Radiomics signature extracted from diffusion-weighted magnetic resonance imaging predicts outcomes in osteosarcoma. J Bone Oncol 2019; 19: 100263
    CrossrefPubMedGoogle Scholar
  • 62 Wu Y, Xu L, Yang P. et al. Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EbioMedicine 2018; 34: 27-34
    CrossrefPubMedGoogle Scholar
  • 63 Fanciullo C, Gitto S, Carlicchi E, Albano D, Messina C, Sconfienza LM. Radiomics of musculoskeletal sarcomas: a narrative review. J Imaging 2022; 8 (02) 45
    CrossrefPubMedGoogle Scholar
  • 64 Arthur A, Johnston EW, Winfield JM. et al. Virtual biopsy in soft tissue sarcoma. How close are we?. Front Oncol 2022; 12: 892620
    CrossrefPubMedGoogle Scholar
  • 65 Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007; 8 (01) 118-127
    CrossrefPubMedGoogle Scholar
  • 66 Fournier L, Costaridou L, Bidaut L. et al; European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31 (08) 6001-6012
    CrossrefPubMedGoogle Scholar
  • 67 Shung D, Simonov M, Gentry M, Au B, Laine L. Machine learning to predict outcomes in patients with acute gastrointestinal bleeding: a systematic review. Dig Dis Sci 2019; 64 (08) 2078-2087
    CrossrefPubMedGoogle Scholar
  • 68 Aerts HJWL, Velazquez ER, Leijenaar RTH. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006
    CrossrefPubMedGoogle Scholar
  • 69 Welch ML, McIntosh C, Haibe-Kains B. et al. Vulnerabilities of radiomic signature development: the need for safeguards. Radiother Oncol 2019; 130: 2-9
    CrossrefPubMedGoogle Scholar
  • 70 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14 (12) 749-762
    CrossrefPubMedGoogle Scholar
  • 71 Mongan J, Moy L, Kahn Jr CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell 2020; 2 (02) e200029
    CrossrefPubMedGoogle Scholar
  • 72 Kocak B, Baessler B, Bakas S. et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 2023; 14 (01) 75
    CrossrefPubMedGoogle Scholar
  • 73 Paranavithana IR, Stirling D, Ros M, Field M. Systematic review of tumor segmentation strategies for bone metastases. Cancers (Basel) 2023; 15 (06) 1750
    CrossrefPubMedGoogle Scholar
  • 74 Yang Y, Zhou Y, Zhou C, Ma X. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods. Orphanet J Rare Dis 2022; 17 (01) 158
    CrossrefPubMedGoogle Scholar
  • 75 Hexiang W, Shifeng Y, Tongyu W. et al. Preoperative MRI-based deep learning radiomics machine learning model for prediction of the histopathological grade of soft tissue sarcomas. Chinese Journal of Radiology (China) 2022; 56 (07) 792-799
    PubMedGoogle Scholar
  • 76 Liu S, Sun W, Yang S. et al. Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. Eur Radiol 2022; 32 (02) 793-805
    CrossrefPubMedGoogle Scholar
  • 77 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
    CrossrefPubMedGoogle Scholar
  • 78 Yin P, Wang W, Wang S. et al. The potential for different computed tomography-based machine learning networks to automatically segment and differentiate pelvic and sacral osteosarcoma from Ewing's sarcoma. Quant Imaging Med Surg 2023; 13 (05) 3174-3184
    CrossrefPubMedGoogle Scholar
  • 79 Stewart M. Guide to Interpretable Machine Learning. Techniques to dispel the black box myth of deep learning. Published March 19, 2020. Available at: https://towardsdatascience.com/guide-to-interpretable-machine-learning-d40e8a64b6cf . Accessed August 6, 2023
    PubMedGoogle Scholar
  • 80 Hosny A, Aerts HJ, Mak RH. Handcrafted versus deep learning radiomics for prediction of cancer therapy response. Lancet Digit Health 2019; 1 (03) e106-e107
    CrossrefPubMedGoogle Scholar
  • 81 Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 2020; 128 (02) 336-359
    CrossrefPubMedGoogle Scholar
  • 82 Hosny A, Parmar C, Coroller TP. et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PloS Med 2018; 15 (11) e1002711
    CrossrefPubMedGoogle Scholar
  • 83 Fedorov A, Beichel R, Kalpathy-Cramer J. et al. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 2012; 30 (09) 1323-1341 DOI: 10.1016/j.mri.2012.05.001.
    CrossrefPubMedGoogle Scholar