Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer

作者全名："Wang, Qiang; Xu, Jianhua; Wang, Anrong; Chen, Yi; Wang, Tian; Chen, Danyu; Zhang, Jiaxing; Brismar, Torkel B. B."

作者地址："[Wang, Qiang; Brismar, Torkel B. B.] Karolinska Inst, Dept Clin Sci, Div Med Imaging & Technol, Intervent & Technol CLINTEC, Stockholm, Sweden; [Wang, Qiang; Brismar, Torkel B. B.] Karolinska Univ Hosp Huddinge, Dept Radiol, Room 601,Novum PI 6,Hiss, Halsovagen 7, S-14186 Huddinge, Stockholm, Sweden; [Xu, Jianhua] Songshan Hosp, Dept Gen Surg, Chongqing, Peoples R China; [Wang, Anrong] Chongqing Med Univ, Dept Vasc Surg, Affiliated Hosp 1, Chongqing, Peoples R China; [Wang, Anrong] Peoples Hosp Dianjiang Cty, Dept Intervent Therapy, Chongqing, Peoples R China; [Chen, Yi] Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden; [Wang, Tian] Chongqing Gen Hosp, Dept Gastroenterol, Chongqing, Peoples R China; [Chen, Danyu] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Gastroenterol & Hepatol, Guangzhou, Peoples R China; [Zhang, Jiaxing] Guizhou Prov Peoples Hosp, Dept Pharm, Guiyang, Peoples R China"

通信作者："Wang, Q (通讯作者)，Karolinska Inst, Dept Clin Sci, Div Med Imaging & Technol, Intervent & Technol CLINTEC, Stockholm, Sweden.; Wang, Q (通讯作者)，Karolinska Univ Hosp Huddinge, Dept Radiol, Room 601,Novum PI 6,Hiss, Halsovagen 7, S-14186 Huddinge, Stockholm, Sweden."

来源：RADIOLOGIA MEDICA

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000913996200001

JCR分区：Q1

影响因子：8.9

年份：2023

卷号：128

期号：2

开始页：136

结束页：148

文献类型：Review

关键词：Radiomics; Microsatellite instability; Colorectal neoplasms; Machine learning; Systematic review as topic

摘要："This study aimed to systematically summarize the performance of the machine learning-based radiomics models in the prediction of microsatellite instability (MSI) in patients with colorectal cancer (CRC). It was conducted according to the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guideline and was registered at the PROSPERO website with an identifier CRD42022295787. Systematic literature searching was conducted in databases of PubMed, Embase, Web of Science, and Cochrane Library up to November 10, 2022. Research which applied radiomics analysis on preoperative CT/MRI/PET-CT images for predicting the MSI status in CRC patients with no history of anti-tumor therapies was eligible. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) were applied to evaluate the research quality (full score 100%). Twelve studies with 4,320 patients were included. All studies were retrospective, and only four had an external validation cohort. The median incidence of MSI was 19% (range 8-34%). The area under the receiver operator curve of the models ranged from 0.78 to 0.96 (median 0.83) in the external validation cohort. The median sensitivity was 0.76 (range 0.32-1.00), and the median specificity was 0.87 (range 0.69-1.00). The median RQS score was 38% (range 14-50%), and half of the studies showed high risk in patient selection as evaluated by QUADAS-2. In conclusion, while radiomics based on pretreatment imaging modalities had a high performance in the prediction of MSI status in CRC, so far it does not appear to be ready for clinical use due to insufficient methodological quality."

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