WHO Classification of Tumours Edition Board (2020) World Health organization classification of tumours: WHO classification of tumours of soft tissue and bone, 5th edn. IARC Press, Lyon
Google Scholar
Strauss SJ, Frezza AM, Abecassis N et al Guidelines Committee, EURACAN, GENTURIS and ERN PaedCan (2021) Bone sarcomas: ESMO-EURACAN-GENTURIS-ERN PaedCan clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol 32(12):1520–1536. https://doi.org/10.1016/j.annonc.2021.08.1995
Article
CAS
PubMed
Google Scholar
National Comprehensive Cancer Network (2021) NCCN clinical practice guidelines in oncology: bone cancer, version 2. 2022. https://www.nccn.org/professionals/physician_gls/pdf/bone.pdf. Accessed 8 Oct 2021
Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446. https://doi.org/10.1016/j.ejca.2011.11.036
Article
PubMed
PubMed Central
Google Scholar
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169
Article
PubMed
Google Scholar
O’Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186. https://doi.org/10.1038/nrclinonc.2016.162
Article
CAS
PubMed
Google Scholar
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762. https://doi.org/10.1038/nrclinonc.2017.141
Article
PubMed
Google Scholar
Zhong J, Hu Y, Si L et al (2021) A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 31(3):1526–1535. https://doi.org/10.1007/s00330-020-07221-w
Article
PubMed
Google Scholar
Whiting PF, Rutjes AW, Westwood ME et al QUADAS-2 Group (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Article
PubMed
Google Scholar
Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162(1):55–63. https://doi.org/10.7326/M14-0697
Article
PubMed
Google Scholar
Park SH (2022) Guides for the successful conduct and reporting of systematic review and meta-analysis of diagnostic test accuracy studies. Korean J Radiol 23(3):295–297. https://doi.org/10.3348/kjr.2021.0963
Article
PubMed
PubMed Central
Google Scholar
Park JE, Kim D, Kim HS et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30(1):523–536. https://doi.org/10.1007/s00330-019-06360-z
Article
PubMed
Google Scholar
Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK (2020) Quality reporting of radiomics analysis in mild cognitive impairment and alzheimer’s disease: a roadmap for moving forward. Korean J Radiol 21(12):1345–1354. https://doi.org/10.3348/kjr.2020.0715
Article
PubMed
PubMed Central
Google Scholar
Park CJ, Park YW, Ahn SS et al (2022) Quality of radiomics research on brain metastasis: a roadmap to promote clinical translation. Korean J Radiol 23(1):77–88. https://doi.org/10.3348/kjr.2021.0421
Article
CAS
PubMed
PubMed Central
Google Scholar
Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157. https://doi.org/10.3322/caac.21552
Article
PubMed
PubMed Central
Google Scholar
Shur JD, Doran SJ, Kumar S et al (2021) Radiomics in oncology: a practical guide. Radiographics 41(6):1717–1732. https://doi.org/10.1148/rg.2021210037
Article
PubMed
Google Scholar
Cheng PM, Montagnon E, Yamashita R et al (2021) Deep learning: an update for radiologists. Radiographics 41(5):1427–1445. https://doi.org/10.1148/rg.2021200210
Article
PubMed
Google Scholar
Marti-Bonmati L, Koh DM, Riklund K et al (2022) Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper. Insights Imaging 13:89. https://doi.org/10.1186/s13244-022-01220-9
Article
PubMed
PubMed Central
Google Scholar
Mongan J, Moy L, Kahn CE Jr (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2(2):e200029. https://doi.org/10.1148/ryai.2020200029
Article
PubMed
PubMed Central
Google Scholar
O’Shea RJ, Sharkey AR, Cook GJR, Goh V (2021) Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol 31(10):7969–7983. https://doi.org/10.1007/s00330-021-07881-2
Article
PubMed
PubMed Central
Google Scholar
Si L, Zhong J, Huo J, et al. (2022) Deep learning in knee imaging: a systematic review utilizing a checklist for artificial intelligence in medical imaging (CLAIM). Eur Radiol 32(2):1353–1361. https://doi.org/10.1007/s00330-021-08190-4
Article
PubMed
Google Scholar
Dang Y, Hou Y (2021) The prognostic value of late gadolinium enhancement in heart diseases: an umbrella review of meta-analyses of observational studies. Eur Radiol 31(7):4528–4537. https://doi.org/10.1007/s00330-020-07437-w
Article
PubMed
Google Scholar
Gitto S, Cuocolo R, Albano D et al (2021) CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 12(1):68. https://doi.org/10.1186/s13244-021-01008-3
Article
PubMed
PubMed Central
Google Scholar
Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M (2020) Systematic review of sarcomas radiomics studies: bridging the gap between concepts and clinical applications? Eur J Radiol 132:109283. https://doi.org/10.1016/j.ejrad.2020.109283
Article
PubMed
Google Scholar
Garner P, Hopewell S, Chandler J et al Panel for updating guidance for systematic reviews (PUGs) (2016) When and how to update systematic reviews: consensus and checklist. BMJ 354:i3507. https://doi.org/10.1136/bmj.i3507
Article
PubMed
PubMed Central
Google Scholar
Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71
Article
PubMed
PubMed Central
Google Scholar
Mangiafico SS (2016) Summary and analysis of extension program evaluation in R, version 1.19.10. http://rcompanion.org/handbook/. Accessed May 2022
Cochrane screening and diagnostic test methods group (2022) Cochrane handbook for systematic reviews of diagnostic test accuracy, version 2. https://training.cochrane.org/handbook-diagnostic-test-accuracy. Accessed May 2022
Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A (2019) Intravoxel incoherent motion (IVIM) for response assessment in patients with osteosarcoma undergoing neoadjuvant chemotherapy. Eur J Radiol 119:108635. https://doi.org/10.1016/j.ejrad.2019.08.004
Article
PubMed
Google Scholar
Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A (2021) Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging. NMR Biomed 34(2):e4426. https://doi.org/10.1002/nbm.4426
Article
CAS
PubMed
Google Scholar
Baidya Kayal E, Sharma N, Sharma R, Bakhshi S, Kandasamy D, Mehndiratta A (2022) T1 mapping as a surrogate marker of chemotherapy response evaluation in patients with osteosarcoma. Eur J Radiol 148:110170. https://doi.org/10.1016/j.ejrad.2022.110170
Article
PubMed
Google Scholar
Bailly C, Leforestier R, Campion L et al (2017) Prognostic value of FDG-PET indices for the assessment of histological response to neoadjuvant chemotherapy and outcome in pediatric patients with Ewing sarcoma and osteosarcoma. PLoS One 12(8):e0183841. https://doi.org/10.1371/journal.pone.0183841
Article
CAS
PubMed
PubMed Central
Google Scholar
Chen H, Liu J, Cheng Z et al (2020) Value of radiomics nomogram based on T1WI for pretreatment prediction of relapse within 1 year in osteosarcoma: a multicenter study. Chin J Radiol 54(9):874–881. https://doi.org/10.3760/cma.j.cn112149-20200512-00675 (in Chinese)
Article
Google Scholar
Chen H, Liu J, Cheng Z et al (2020) 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 129:109066. https://doi.org/10.1016/j.ejrad.2020.109066
Article
PubMed
Google Scholar
Chen H, Zhang X, Wang X et al (2021) MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol 31(10):7913–7924. https://doi.org/10.1007/s00330-021-07748-6
Article
PubMed
Google Scholar
Cho YJ, Kim WS, Choi YH et al (2019) Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: differentiation of pulmonary metastases from non-metastatic nodules. PLoS One 14(2):e0211969. https://doi.org/10.1371/journal.pone.0211969
Article
CAS
PubMed
PubMed Central
Google Scholar
Dai Y, Yin P, Mao N et al (2020) Differentiation of pelvic osteosarcoma and ewing sarcoma using radiomic analysis based on T2-weighted images and contrast-enhanced T1-weighted images. Biomed Res Int 2020:9078603. https://doi.org/10.1155/2020/9078603
Article
CAS
PubMed
PubMed Central
Google Scholar
Djuričić GJ, Ahammer H, Rajković S (2022) Directionally sensitive fractal radiomics compatible with irregularly shaped magnetic resonance tumor regions of interest: association with osteosarcoma chemoresistance. J Magn Reson Imaging. https://doi.org/10.1002/jmri.28232
Article
PubMed
Google Scholar
Dufau J, Bouhamama A, Leporq B et al (2019) Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data. Bull Cancer 106(11):983–999. https://doi.org/10.1016/j.bulcan.2019.07.005 (in French)
Article
PubMed
Google Scholar
Jeong SY, Kim W, Byun BH et al (2019) prediction of chemotherapy response of osteosarcoma using baseline 18F-FDG textural features machine learning approaches with PCA. Contrast Media Mol Imaging 2019:3515080. https://doi.org/10.1155/2019/3515080
Article
CAS
PubMed
PubMed Central
Google Scholar
Kim BC, Kim J, Kim K et al (2021) Preliminary radiogenomic evidence for the prediction of metastasis and chemotherapy response in pediatric patients with osteosarcoma using 18F-FDF PET/CT, EZRIN and KI67. Cancers (Basel) 13(11):2671. https://doi.org/10.3390/cancers13112671
Article
CAS
Google Scholar
Kim J, Jeong SY, Kim BC et al (2021) Prediction of neoadjuvant chemotherapy response in osteosarcoma using convolutional neural network of tumor center 18F-FDG PET images. Diagnostics (Basel) 11(11):1976. https://doi.org/10.3390/diagnostics11111976
Article
CAS
Google Scholar
Lee SK, Jee WH, Jung CK et al (2020) Prediction of poor responders to neoadjuvant chemotherapy in patients with osteosarcoma: additive value of diffusion-weighted MRI including volumetric analysis to standard MRI at 3T. PLoS One 15(3):e0229983. https://doi.org/10.1371/journal.pone.0229983
Article
CAS
PubMed
PubMed Central
Google Scholar
Lin P, Yang PF, Chen S et al (2020) A delta-radiomics model for preoperative evaluation of neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imaging 20(1):7. https://doi.org/10.1186/s40644-019-0283-8
Article
PubMed
PubMed Central
Google Scholar
Liu J, Lian T, Chen H et al (2021) Pretreatment prediction of relapse risk in patients with osteosarcoma using radiomics nomogram based on CT: a retrospective multicenter study. Biomed Res Int 2021:6674471. https://doi.org/10.1155/2021/6674471
Article
PubMed
PubMed Central
Google Scholar
Luo Z, Li J, Liao Y, Liu R, Shen X, Chen W (2022) Radiomics analysis of multiparametric MRI for prediction of synchronous lung metastases in osteosarcoma. Front Oncol 12:802234. https://doi.org/10.3389/fonc.2022.802234
Article
PubMed
PubMed Central
Google Scholar
Pereira HM, Leite Duarte ME, Ribeiro Damasceno I, de Oliveira Moura Santos LA, Nogueira-Barbosa MH (2021) Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma. Br J Radiol 94(1124):20201391. https://doi.org/10.1259/bjr.20201391
Article
PubMed
PubMed Central
Google Scholar
Sheen H, Kim W, Byun BH et al (2019) Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: a multivariable radiomics model. PLoS One 14(11):e0225242. https://doi.org/10.1371/journal.pone.0225242
Article
CAS
PubMed
PubMed Central
Google Scholar
Song H, Jiao Y, Wei W et al (2019) Can pretreatment 18F-FDG PET tumor texture features predict the outcomes of osteosarcoma treated by neoadjuvant chemotherapy? Eur Radiol 29(7):3945–3954. https://doi.org/10.1007/s00330-019-06074-2
Article
PubMed
Google Scholar
Wan Y, Yang P, Xu L et al (2021) Radiomics analysis combining unsupervised learning and handcrafted features: a multiple-disease study. Med Phys 48(11):7003–7015. https://doi.org/10.1002/mp.15199
Article
CAS
PubMed
Google Scholar
Wu Y, Xu L, Yang P et al (2018) Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine 34:27–34. https://doi.org/10.1016/j.ebiom.2018.07.006
Article
PubMed
PubMed Central
Google Scholar
Xu L, Yang P, Yen EA et al (2019) A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis. Phys Med Biol 64(21):215009. https://doi.org/10.1088/1361-6560/ab489f
Article
PubMed
Google Scholar
Xu L, Yang P, Hu K et al (2021) Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images. Quant Imaging Med Surg 11(4):1184–1195. https://doi.org/10.21037/qims-20-681
Article
PubMed
PubMed Central
Google Scholar
Yin P, Zhi X, Sun C et al (2021) Radiomics models for the preoperative prediction of pelvic and sacral tumor types: a single-center retrospective study of 795 cases. Front Oncol 11:709659. https://doi.org/10.3389/fonc.2021.709659
Article
PubMed
PubMed Central
Google Scholar
Zhang L, Ge Y, Gao Q et al (2021) Machine learning-based radiomics nomogram with dynamic contrast-enhanced MRI of the osteosarcoma for evaluation of efficacy of neoadjuvant chemotherapy. Front Oncol 11:758921. https://doi.org/10.3389/fonc.2021.758921
Article
PubMed
PubMed Central
Google Scholar
Zhao S, Su Y, Duan J et al (2019) Radiomics signature extracted from diffusion-weighted magnetic resonance imaging predicts outcomes in osteosarcoma. J Bone Oncol 19:100263. https://doi.org/10.1016/j.jbo.2019.100263
Article
PubMed
PubMed Central
Google Scholar
Zhong J, Zhang C, Hu Y et al (2022) Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram. Eur Radiol. https://doi.org/10.1007/s00330-022-08735-1
Article
PubMed
PubMed Central
Google Scholar
Guiot J, Vaidyanathan A, Deprez L et al (2022) A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev 42(1):426–440. https://doi.org/10.1002/med.21846
Article
PubMed
Google Scholar
Collins GS, Dhiman P, Andaur Navarro CL et al (2021) Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11(7):e048008. https://doi.org/10.1136/bmjopen-2020-048008
Article
PubMed
PubMed Central
Google Scholar
Huang L, Xia W, Zhang B, Qiu B, Gao X (2017) MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images. Comput Methods Progr Biomed 143:67–74. https://doi.org/10.1016/j.cmpb.2017.02.013
Article
Google Scholar
Zhang R, Huang L, Xia W, Zhang B, Qiu B, Gao X (2018) Multiple supervised residual network for osteosarcoma segmentation in CT images. Comput Med Imaging Graph 63:1–8. https://doi.org/10.1016/j.compmedimag.2018.01.006
Article
PubMed
Google Scholar
Wu J, Yang S, Gou F et al (2022) Intelligent segmentation medical assistance system for MRI images of osteosarcoma in developing countries. Comput Math Methods Med 2022:7703583. https://doi.org/10.1155/2022/7703583
Article
PubMed
PubMed Central
Google Scholar
Huang B, Wang J, Sun M et al (2020) 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 20(1):322. https://doi.org/10.1186/s12885-020-06825-1
Article
CAS
PubMed
PubMed Central
Google Scholar
Sounderajah V, Ashrafian H, Rose S et al (2021) A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med 27(10):1663–1665. https://doi.org/10.1038/s41591-021-01517-0
Article
CAS
PubMed
Google Scholar
Vasey B, Nagendran M, Campbell B et al DECIDE-AI expert group (2022) Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 28(5):924–933. https://doi.org/10.1038/s41591-022-01772-9
Article
CAS
PubMed
Google Scholar
Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ, SPIRIT-AI and CONSORT-AI Working Group; SPIRIT-AI and CONSORT-AI Steering Group; SPIRIT-AI and CONSORT-AI Consensus Group (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26(9):1351–1363. https://doi.org/10.1038/s41591-020-1037-7
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, SPIRIT-AI and CONSORT-AI Working Group (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26(9):1364–1374. https://doi.org/10.1038/s41591-020-1034-x
Article
CAS
PubMed
PubMed Central
Google Scholar
Sounderajah V, Ashrafian H, Aggarwal R et al (2020) Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI steering group. Nat Med 26(6):807–808. https://doi.org/10.1038/s41591-020-0941-1
Article
CAS
PubMed
Google Scholar
Shelmerdine SC, Arthurs OJ, Denniston A, Sebire NJ (2021) Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health Care Inform 28(1):e100385. https://doi.org/10.1136/bmjhci-2021-100385
Article
PubMed
PubMed Central
Google Scholar
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338. https://doi.org/10.1148/radiol.2020191145
Article
PubMed
Google Scholar