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Application and progress of X‐ray, computed tomography, and magnetic resonance imaging radiomics in osteosarcoma

Peihong TengLingling RenHaifeng HaoChang LiuGuifeng Liu()
Department of Radiology, China‐Japan Union Hospital of Jilin University, Changchun, Jilin, China
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Osteosarcoma is the most common primary malignant bone tumor, often associated with a poor natural prognosis. Imaging techniques such as X‐ray, computed tomography, and magnetic resonance imaging have become essential tools in the diagnosis and treatment of osteosarcoma. The development of imaging informatics techniques, including machine learning and deep learning, has enhanced the role of medical imaging in assisting physicians with early diagnosis and treatment evaluation. This advancement further improves the diagnosis and treatment of osteosarcoma patients, facilitating more personalized care.

Abstract

Looking back on the development of radiomics in osteosarcoma over recent years, in addition to distinguishing osteosarcoma from other malignant bone tumors (mainly Ewing's sarcoma), more research directions are using radiomics to evaluate and predict the efficacy and survival of patients undergoing neoadjuvant chemotherapy. Among the three commonly used examination methods of X‐ray, CT, and magnetic resonance imaging (MRI), more and more studies have been conducted on MRI‐based radiomics, which fully reflects the advantages of MRI's high soft tissue contrast, multi‐sequence imaging, and most of the studies used a combination of imaging features and clinical features to make predictions. Some articles also considered relevant laboratory examination results and more and more studies are performing external verification. The field is now gradually developing toward multidimensional data and multicenter cooperation and data sharing.

References

[1]

Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, et al. Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine‐learning approaches. AJNR Am J Neuroradiol. 2018;39(2):208–16. https://doi.org/10.3174/ajnr.a5391

[2]

Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol. 2020;196(10):856–67. https://doi.org/10.1007/s00066-020-01626-8

[3]
Isensee F, Kickingereder P, Wick W, Bendszus M, Maier‐Hein KH. Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries: third international workshop, BrainLes 2017, held in conjunction with MICCAI 2017, Quebec city, QC, Canada, September 14, 2017, revised selected papers 3, Springer; 2018. p. 287–297.
[4]

Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS. Rapid review: radiomics and breast cancer. Breast Cancer Res Treat. 2018;169(2):217–29. https://doi.org/10.1007/s10549-018-4675-4

[5]
Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol. 2021;72:238–50. Elsevier. https://doi.org/10.1016/j.semcancer.2020.04.002
[6]

Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020;49:74–80. https://doi.org/10.1016/j.breast.2019.10.018

[7]

Lee S‐H, Park H, Ko ES. Radiomics in breast imaging from techniques to clinical applications: a review. Korean J Radiol. 2020;21(7):779. https://doi.org/10.3348/kjr.2019.0855

[8]

Moore DD, Luu HH. Osteosarcoma. Cancer Treat Res. 2014;162:65–92. https://doi.org/10.7150/thno.34157

[9]

Bandyopadhyay O, Biswas A, Bhattacharya BB. Bone‐cancer assessment and destruction pattern analysis in long‐bone X‐ray image. J Digit Imaging. 2019;32(2):300–13. https://doi.org/10.1007/s10278-018-0145-0

[10]

Sun YS, Zhang XP, Tang L, Ji JF, Gu J, Cai Y, et al. Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion‐weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology. 2010;254(1):170–8. https://doi.org/10.1148/radiol.2541082230

[11]

Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion‐weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102–25. https://doi.org/10.1593/neo.81328

[12]

Sujlana P, Skrok J, Fayad LM. Review of dynamic contrast‐enhanced MRI: technical aspects and applications in the musculoskeletal system. J Magn Reson Imaging. 2018;47(4):875–90. https://doi.org/10.1002/jmri.25810

[13]

Yoshida A. Osteosarcoma: old and new challenges. Surg Pathol Clin. 2021;14(4):567–83. https://doi.org/10.1016/j.path.2021.06.003

[14]

Xie L, Ji T, Guo W. Anti‐angiogenesis target therapy for advanced osteosarcoma (review). Oncol Rep. 2017;38(2):625–36. https://doi.org/10.3892/or.2017.5735

[15]

Bielack SS, Kempf‐Bielack B, Delling G, Exner GU, Flege S, Helmke K, et al. Prognostic factors in high‐grade osteosarcoma of the extremities or trunk: an analysis of 1,702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols. J Clin Oncol. 2002;20(3):776–90. https://doi.org/10.1200/jco.2002.20.3.776

[16]
Shen R, Li Z, Zhang L, Hua Y, Mao M, Li Z, et al. Osteosarcoma patients classification using plain X‐rays and metabolomic data. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018. p. 690–3. https://doi.org/10.1109/EMBC.2018.8512338
[17]

von Schacky CE, Wilhelm NJ, Schäfer VS, Leonhardt Y, Jung M, Jungmann PM, et al. Development and evaluation of machine learning models based on X‐ray radiomics for the classification and differentiation of malignant and benign bone tumors. Eur Radiol. 2022;32(9):6247–57. https://doi.org/10.1007/s00330-022-08764-w

[18]

Yin P, Wang W, Wang S, Liu T, Sun C, Liu X, 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 Imag Med Surg. 2023;13(5):3174–84. https://doi.org/10.21037/qims-22-1042

[19]

Dai Y, Yin P, Mao N, Sun C, Wu J, Cheng G, et al. Differentiation of pelvic osteosarcoma and ewing sarcoma using radiomic analysis based on T2‐weighted images and contrast‐enhanced T1‐weighted images. BioMed Res Int. 2020;2020:9078603. https://doi.org/10.1155/2020/9078603

[20]

Sami SH, Rafati AH, Hodjat P. Tissue necrosis after chemotherapy in osteosarcoma as the important prognostic factor. Saudi Med J. 2008;29(8):1124–9.

[21]

Xu L, Yang P, Hu K, Wu Y, Xu‐Welliver M, Wan Y, et al. Prediction of neoadjuvant chemotherapy response in high‐grade osteosarcoma: added value of non‐tumorous bone radiomics using CT images. Quant Imag Med Surg. 2021;11(4):1184–95. https://doi.org/10.21037/qims-20-681

[22]

Lin P, Yang PF, Chen S, Shao YY, Xu L, Wu Y, et al. A Delta‐radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high‐grade osteosarcoma. Cancer Imag. 2020;20(1):7. https://doi.org/10.1186/s40644-019-0283-8

[23]

Wang CS, Du LJ, Si MJ, Yin QH, Chen L, Shu M, et al. Noninvasive assessment of response to neoadjuvant chemotherapy in osteosarcoma of long bones with diffusion‐weighted imaging: an initial in vivo study. PLoS One. 2013;8(8):e72679. https://doi.org/10.1371/journal.pone.0072679

[24]

Huang B, Wang J, Sun M, Chen X, Xu D, Li ZP, et al. Feasibility of multi‐parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study. BMC Cancer. 2020;20(1):322. https://doi.org/10.1186/s12885-020-06825-1

[25]

Dufau J, Bouhamama A, Leporq B, Malaureille L, Beuf O, Gouin F, et al. [Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data]. Bull Cancer. 2019;106(11):983–9. https://doi.org/10.1016/j.bulcan.2019.07.005

[26]

Teo KY, Daescu O, Cederberg K, Sengupta A, Leavey PJ. Correlation of histopathology and multi‐modal magnetic resonance imaging in childhood osteosarcoma: predicting tumor response to chemotherapy. PLoS One. 2022;17(2):e0259564. https://doi.org/10.1371/journal.pone.0259564

[27]

Zhang L, Ge Y, Gao Q, Zhao F, Cheng T, Li H, et al. Machine learning‐based radiomics nomogram with dynamic contrast‐enhanced MRI of the osteosarcoma for evaluation of efficacy of neoadjuvant chemotherapy. Front Oncol. 2021;11. https://doi.org/10.3389/fonc.2021.758921

[28]

Chen H, Zhang X, Wang X, Quan X, Deng Y, Lu M, 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):7913–24. https://doi.org/10.1007/s00330-021-07748-6

[29]

Bouhamama A, Leporq B, Khaled W, Nemeth A, Brahmi M, Dufau J, et al. Prediction of histologic neoadjuvant chemotherapy response in osteosarcoma using pretherapeutic MRI radiomics. Radiol Imaging Cancer. 2022;4(5):e210107. https://doi.org/10.1148/rycan.210107

[30]

White LM, Atinga A, Naraghi AM, Lajkosz K, Wunder JS, Ferguson P, 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(3):553–564. https://doi.org/10.1007/s00256-022-04098-2

[31]

Luo Z, Li J, Liao Y, Huang W, Li Y, Shen X. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high‐grade osteosarcoma using X‐ray and multiparametric MRI radiomics. J X Ray Sci Technol. 2023;31(3):611–26. https://doi.org/10.3233/xst-221352

[32]

Wu Y, Xu L, Yang P, Lin N, Huang X, Pan W, et al. Survival prediction in high‐grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine. 2018;34:27–34. https://doi.org/10.1016/j.ebiom.2018.07.006

[33]

Zhao S, Su Y, Duan J, Qiu Q, Ge X, Wang A, et al. Radiomics signature extracted from diffusion‐weighted magnetic resonance imaging predicts outcomes in osteosarcoma. J Bone Oncol. 2019;19:100263. https://doi.org/10.1016/j.jbo.2019.100263

[34]

Liu J, Lian T, Chen H, Wang X, Quan X, Deng Y, 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. https://doi.org/10.1155/2021/6674471

[35]

Chen H, Liu J, Cheng Z, Lu X, Wang X, Lu M, 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. https://doi.org/10.1016/j.ejrad.2020.109066

[36]

Meyers PA, Heller G, Healey JH, Huvos A, Applewhite A, Sun M, et al. Osteogenic sarcoma with clinically detectable metastasis at initial presentation. J Clin Oncol. 1993;11(3):449–53. https://doi.org/10.1200/jco.1993.11.3.449

[37]

Yin P, Zhong J, Liu Y, Liu T, Sun C, Liu X, et al. Clinical‐radiomics models based on plain X‐rays for prediction of lung metastasis in patients with osteosarcoma. BMC Med Imag. 2023;23(1):40. https://doi.org/10.1186/s12880-023-00991-x

[38]

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. https://doi.org/10.1259/bjr.20201391

[39]

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. https://doi.org/10.3389/fonc.2022.802234

[40]

Ottaviani G, Jaffe N. The epidemiology of osteosarcoma. Cancer Treat Res. 2009;152:3–13. https://doi.org/10.1007/978-1-4419-0284-9_1

[41]

Ritter J, Bielack S. Osteosarcoma. Ann Oncol. 2010;21:vii320–5. https://doi.org/10.1093/annonc/mdq276

[42]

Bajpai J, Gamnagatti S, Kumar R, Sreenivas V, Sharma MC, Khan SA, et al. Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis. Pediatr Radiol. 2011;41(4):441–50. https://doi.org/10.1007/s00247-010-1876-3

[43]

Hayashida Y, Yakushiji T, Awai K, Katahira K, Nakayama Y, Shimomura O, et al. Monitoring therapeutic responses of primary bone tumors by diffusion‐weighted image: initial results. Eur Radiol. 2006;16(12):2637–43. https://doi.org/10.1007/s00330-006-0342-y

iRADIOLOGY
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Cite this article:
Teng P, Ren L, Hao H, et al. Application and progress of X‐ray, computed tomography, and magnetic resonance imaging radiomics in osteosarcoma. iRADIOLOGY, 2023, 1(3): 262-268. https://doi.org/10.1002/ird3.34
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