Radiomics in sarcoma trials:a complement to RECIST for patient assessment  

作　　者：Caryn Geady David B.Shultz Albiruni R.Abdul Razak Scott Schuetze Benjamin Haibe-Kains 

机构地区：[1]Radiation Medicine Program,Princess Margaret Cancer Centre,Toronto,ON M5G 2C1,Canada [2]Department of Medical Biophysics,University of Toronto,Toronto,ON M5G 1L7,Canada [3]Department of Medical Oncology and Hematology,Princess Margaret Cancer Centre,Toronto,ON M5G 2C1,Canada [4]Department of Internal Medicine,University of Michigan,Ann Arbor,MI 48109-5624,USA [5]Department of Computer Science,University of Toronto,Toronto,ON M5S 2E4,Canada [6]Department of Biostatistics,Dalla Lana School of Public Health,Toronto,ON M5T 3M7,Canada [7]Vector Institute for Artificial Intelligence,Toronto,ON M5G 1M1,Canada [8]Ontario Institute for Cancer Research,Toronto,ON M5G 0A3,Canada

出　　处：《Journal of Cancer Metastasis and Treatment》2022年第1期607-614,共8页癌症转移与治疗（英文版）

基　　金：This research was supported by the Sarcoma Alliance for Research through Collaboration LMSARC research fund;the philanthropic LMS360 research fund from the University of Michigan LMS360.

摘　　要：Radiological imaging has a critical role in the diagnosis of sarcomas and in evaluating therapy response assessment.The current gold standard for response assessment in solid tumors is the Response Evaluation Criteria in Solid Tumors,which evaluates changes in tumor size as a surrogate endpoint for therapeutic efficacy.However,tumors may undergo necrosis,changes in vascularization or become cystic in response to therapy,with no significant volume changes;thus,size assessments alone may not be adequate.Such morphological changes may give rise to radiographic phenotypes that are not easily detected by human operators.Fortunately,recent advances in high-performance computing and machine learning algorithms have enabled deep analysis of radiological images to extract features that can provide richer information about intensity,shape,size or volume,and texture of tumor phenotypes.There is growing evidence to suggest that these image-derived or“radiomic features”are sensitive to biological processes such as necrosis and glucose metabolism.Thus,radiomics could prove to be a critical tool for assessing treatment response and may present an integral complement to existing response criteria,opening new avenues for patient assessment in sarcoma trials.

关 键 词：Clinical trials response assessment RADIOLOGY radiomics machine learning 

分 类 号：R730.55[医药卫生—肿瘤]