Strauss SJ, Frezza AM, Abecassis N et al (2021) Bone sarcomas: ESMO–EURACAN–GENTURIS–ERN PaedCan Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 32:1520–1536. https://doi.org/10.1016/j.annonc.2021.08.1995
Article CAS PubMed Google Scholar
Gatta G, Capocaccia R, Botta L et al (2017) Burden and centralised treatment in Europe of rare tumours: results of RARECAREnet—a population-based study. Lancet Oncol 18:1022–1039. https://doi.org/10.1016/S1470-2045(17)30445-X
Zhao X, Wu Q, Gong X et al (2021) Osteosarcoma: a review of current and future therapeutic approaches. BioMed Eng OnLine 20:24. https://doi.org/10.1186/s12938-021-00860-0
Article PubMed PubMed Central Google Scholar
Yu H, Gao L, Shi R et al (2023) Monitoring early responses to neoadjuvant chemotherapy and the factors affecting neoadjuvant chemotherapy responses in primary osteosarcoma. Quant Imaging Med Surg 13:3716–3725. https://doi.org/10.21037/qims-22-1095
Article PubMed PubMed Central Google Scholar
Picci P, Bacci G, Campanacci M et al (1985) Histologic evaluation of necrosis in osteosarcoma induced by chemotherapy. Regional mapping of viable and nonviable tumor. Cancer 56:1515–1521. https://doi.org/10.1002/1097-0142(19851001)56:7%3c1515::aid-cncr2820560707%3e3.0.co;2-6
Article CAS PubMed Google Scholar
Vangala N, Uppin SG, Rao KN et al (2021) Prognostic significance of clinical and post-neoadjuvant chemotherapy associated histomorphological parameters in osteosarcoma: a retrospective study from a tertiary care center. Indian J Med Paediatr Oncol 42:547–553. https://doi.org/10.1055/s-0041-1740113
Kalisvaart GM, Van Den Berghe T, Grootjans W et al (2024) Evaluation of response to neoadjuvant chemotherapy in osteosarcoma using dynamic contrast-enhanced MRI: development and external validation of a model. Skeletal Radiol 53:319–328. https://doi.org/10.1007/s00256-023-04402-8
Oh C, Bishop MW, Cho SY et al (2023) 18F-FDG PET/CT in the management of osteosarcoma. J Nucl Med 64:842–851. https://doi.org/10.2967/jnumed.123.265592
Article CAS PubMed Google Scholar
Pennington Z, Ahmed AK, Cottrill E et al (2019) Systematic review on the utility of magnetic resonance imaging for operative management and follow-up for primary sarcoma—lessons from extremity sarcomas. Ann Transl Med 7:225–225. https://doi.org/10.21037/atm.2019.01.59
Article PubMed PubMed Central Google Scholar
Yildirim O, Al Khatalin M, Kargin OA, Camurdan VB (2022) MRI for evaluation of preoperative chemotherapy in osteosarcoma. Radiography 28:593–604. https://doi.org/10.1016/j.radi.2022.04.008
Article CAS PubMed 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 20:322. https://doi.org/10.1186/s12885-020-06825-1
Article CAS PubMed PubMed Central Google Scholar
Hao Y, An R, Xue Y et al (2021) Prognostic value of tumoral and peritumoral magnetic resonance parameters in osteosarcoma patients for monitoring chemotherapy response. Eur Radiol 31:3518–3529. https://doi.org/10.1007/s00330-020-07338-y
Dyke JP, Panicek DM, Healey JH et al (2003) Osteogenic and ewing sarcomas: estimation of necrotic fraction during induction chemotherapy with dynamic contrast-enhanced MR imaging. Radiology 228:271–278. https://doi.org/10.1148/radiol.2281011651
Bouhamama A, Leporq B, Khaled W et al (2022) Prediction of histologic neoadjuvant chemotherapy response in osteosarcoma using pretherapeutic MRI radiomics. Radiol Imaging Cancer 4:e210107. https://doi.org/10.1148/rycan.210107
Article 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 29:3945–3954. https://doi.org/10.1007/s00330-019-06074-2
Liu C, Xi Y, Li M et al (2019) Monitoring response to neoadjuvant chemotherapy of primary osteosarcoma using diffusion kurtosis magnetic resonance imaging: initial findings. Korean J Radiol 20:801. https://doi.org/10.3348/kjr.2018.0453
Article PubMed PubMed Central Google Scholar
Fan M, Chen H, You C et al (2021) Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer. Front Mol Biosci 8:622219. https://doi.org/10.3389/fmolb.2021.622219
Article CAS PubMed PubMed Central Google Scholar
Crenn V, Biteau K, Amiaud J et al (2017) Bone microenvironment has an influence on the histological response of osteosarcoma to chemotherapy: retrospective analysis and preclinical modeling. Am J Cancer Res 7:2333–2349
CAS PubMed PubMed Central Google Scholar
Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213. https://doi.org/10.1038/bjc.2014.512
Article CAS PubMed PubMed Central Google Scholar
Corrias G, Micheletti G, Barberini L et al (2022) Texture analysis imaging “what a clinical radiologist needs to know.” Eur J Radiol 146:110055. https://doi.org/10.1016/j.ejrad.2021.110055
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 31:7913–7924. https://doi.org/10.1007/s00330-021-07748-6
Li S, Dai Y, Chen J et al (2024) MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges. Cancer Imaging 24:107. https://doi.org/10.1186/s40644-024-00758-9
Article PubMed PubMed Central Google Scholar
Du T, Zhao H (2022) Habitat analysis of breast cancer-enhanced MRI reflects BRCA1 mutation determined by immunohistochemistry. Biomed Res Int 2022:1–9. https://doi.org/10.1155/2022/9623173
Beig N, Bera K, Prasanna P et al (2020) Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res 26:1866–1876. https://doi.org/10.1158/1078-0432.CCR-19-2556
Article CAS PubMed PubMed Central Google Scholar
Blackledge MD, Winfield JM, Miah A et al (2019) Supervised machine-learning enables segmentation and evaluation of heterogeneous post-treatment changes in multi-parametric MRI of soft-tissue sarcoma. Front Oncol 9:941. https://doi.org/10.3389/fonc.2019.00941
Article PubMed PubMed Central Google Scholar
BaidyaKayal E, Kandasamy D, Yadav R et al (2020) Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: a computer aided system process. Eur J Radiol 133:109359. https://doi.org/10.1016/j.ejrad.2020.109359
Teo KY, Daescu O, Cederberg K et al (2022) Correlation of histopathology and multi-modal magnetic resonance imaging in childhood osteosarcoma: predicting tumor response to chemotherapy. PLoS ONE 17:e0259564. https://doi.org/10.1371/journal.pone.0259564
Article CAS PubMed PubMed Central Google Scholar
Ferrari S, Bielack SS, Smeland S et al (2018) EURO-B.O.S.S.: a European study on chemotherapy in bone-sarcoma patients aged over 40: outcome in primary high-grade osteosarcoma. Tumori 104:30–36. https://doi.org/10.5301/tj.5000696
Article CAS PubMed Google Scholar
Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001
留言 (0)