{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T21:52:57Z","timestamp":1789595577785,"version":"build-2803163510"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1159","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Changsha Municipal Natural Science Foundation","award":["kq2014201"],"award-info":[{"award-number":["kq2014201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,18]]},"abstract":"Abstract<\/jats:title>\n \n Objectives<\/jats:title>\n To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before March 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 1699 patients from 24 studies were included. For machine learning models in predicting pCR to nCRT, the meta-analysis calculated a pooled area under the curve (AUC) of 0.91 (95% CI, 0.88-0.93), pooled sensitivity of 0.83 (95% CI, 0.74-0.89), and pooled specificity of 0.86 (95% CI, 0.80-0.91). We investigated 6 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 6 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep-learning model was 0.93 and 0.89 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90 and 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.93, and was 0.83 in studies conducted in other countries.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This systematic study showed that machine learning has promising potential in predicting pCR to nCRT in patients with locally advanced rectal cancer. Compared to traditional machine learning models, although deep-learning-based studies are less predominant and more heterogeneous, they are able to obtain higher AUC.<\/jats:p>\n <\/jats:sec>\n \n Advances in knowledge<\/jats:title>\n Compared to traditional machine learning models, deep-learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine learning-based models may bring us closer towards precision medicine.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bjr\/tqae098","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:29:22Z","timestamp":1715387362000},"page":"1243-1254","source":"Crossref","is-referenced-by-count":25,"title":["Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis"],"prefix":"10.1093","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3279-3506","authenticated-orcid":false,"given":"Jia","family":"He","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People\u2019s Hospital , Changsha 410002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shang-xian","family":"Wang","sequence":"additional","affiliation":[{"name":"Bayer Radiology , Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-2344","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People\u2019s Hospital , Changsha 410002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"issue":"2","key":"2024061918461693500_tqae098-B1","first-page":"89","article-title":"Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors","volume":"14","author":"Rawla","year":"2019","journal-title":"Prz 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