Tumor grading of soft tissue sarcomas using MRI-based radiomics
Tài liệu tham khảo
Gutierrez, 2007, Outcomes for soft-tissue sarcoma in 8249 cases from a large state cancer registry, J Surg Res, 141, 105, 10.1016/j.jss.2007.02.026
2017
Trojani, 1984, Soft-tissue sarcomas of adults; study of pathological prognostic variables and definition of a histopathological grading system, Int J Cancer, 33, 37, 10.1002/ijc.2910330108
Costa, 1984, The grading of soft tissue sarcomas. Results of a clinicohistopathologic correlation in a series of 163 cases, Cancer, 53, 530, 10.1002/1097-0142(19840201)53:3<530::AID-CNCR2820530327>3.0.CO;2-D
Guillou, 1997, Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma, J Clin Oncol, 15, 350, 10.1200/JCO.1997.15.1.350
Peeken, 2019, Neoadjuvant image-guided helical intensity modulated radiotherapy of extremity sarcomas – a single center experience, Radiat Oncol, 14, 4, 10.1186/s13014-019-1207-2
Peeken, 2018, Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients, Strahlenther Onkol, 194, 824, 10.1007/s00066-018-1294-2
Peeken, 2017, “Radio-oncomics” - the potential of radiomics in radiation oncology, Strahlenther Onkol, 193, 767, 10.1007/s00066-017-1175-0
Peeken, 2018, Radiomics in radiooncology – challenging the medical physicist, Phys Med, 48, 27, 10.1016/j.ejmp.2018.03.012
Peeken, 2018, Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients, Strahlenther Onkol, 194, 824, 10.1007/s00066-018-1294-2
Aerts, 2014, Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat Commun, 5, 4006, 10.1038/ncomms5006
Rios Velazquez, 2017, Somatic mutations drive distinct imaging phenotypes in lung cancer, Cancer Res, 77, 3922, 10.1158/0008-5472.CAN-17-0122
Pyka, 2016, Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas, Eur J Nucl Med Mol Imaging, 43, 133, 10.1007/s00259-015-3140-4
Liang, 2018, A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors, Clin Cancer Res
Vallières, 2015, 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, 60, 5471, 10.1088/0031-9155/60/14/5471
Spraker, 2019, MRI radiomic features are independently associated with overall survival in soft tissue sarcoma, Adv Radiat Oncol, 4, 413, 10.1016/j.adro.2019.02.003
Crombé, 2018, T2-based MRI Delta-radiomics improve response prediction in soft-tissue sarcomas treated by neoadjuvant chemotherapy, J Magn Reson Imaging, 1
Peeken, 2019, CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy, Radiother Oncol, 135, 187, 10.1016/j.radonc.2019.01.004
Fedorov, 2012, 3D slicers as an image computing platform for thw quantitative imaging network, Magn Reson Imaging, 30, 1323, 10.1016/j.mri.2012.05.001
Tustison, 2009, N4ITK: Nick's N3 ITK implementation for MRI Bias field correction, InsightJournal, 1
van Griethuysen, 2017, Computational radiomics system to decode the radiographic phenotype, Cancer Res, 77, e104, 10.1158/0008-5472.CAN-17-0339
Deist, 2018, Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers, Med Phys, 45, 3449, 10.1002/mp.12967
Leger, 2017, A comparative study of machine learning methods for time-To-event survival data for radiomics risk modelling, Sci Rep, 7, 1, 10.1038/s41598-017-13448-3
Steiger, 2019, How can radiomics be consistently applied across imagers and institutions?, Radiology, 291, 60, 10.1148/radiol.2019190051
Lucia, 2018, External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy, Eur J Nucl Med Mol Imaging, 46, 864, 10.1007/s00259-018-4231-9
Orlhac, 2019, Validation of a method to compensate multicenter effects affecting CT radiomics, Radiology, 291, 53, 10.1148/radiol.2019182023
Fortin, 2017, NeuroImage harmonization of multi-site diffusion tensor imaging data, Neuroimage, 161, 149, 10.1016/j.neuroimage.2017.08.047
Vickers, 2008, Decision curve analysis: a novel method for evaluating prediction models, Med Decis Making, 26, 565, 10.1177/0272989X06295361
Pepe, 2009, Estimation and comparison of receiver operating characteristic curves, Stata J, 9, 1, 10.1177/1536867X0900900101
Crombé, 2019, Soft-tissue sarcomas: assessment of MRI features correlating with histologic grade and patient outcome, Radiology, 291, 710, 10.1148/radiol.2019181659
Corino, 2017, Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions, J Magn Reson Imaging, 1
Zhang, 2018, Soft tissue sarcomas: preoperative predictive histopathological grading based on radiomics of MRI, Acad Radiol, 1
Shiradkar, 2016, Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI, Radiat Oncol, 11, 148, 10.1186/s13014-016-0718-3
Collins, 2015, Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, Eur Urol, 67, 1142, 10.1016/j.eururo.2014.11.025
Fletcher, 2013
Edge, 2010, The American joint committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM, Ann Surg Oncol, 17, 1471, 10.1245/s10434-010-0985-4