首页|Data on Rectal Cancer Reported by Shang-Xian Wang and Colleagues (Machine learni ng in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis)
Data on Rectal Cancer Reported by Shang-Xian Wang and Colleagues (Machine learni ng in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Rectal Canc er is the subject of a report. According to news reporting from Chengdu, People' s Republic of China, by NewsRx journalists, research stated, "To evaluate the pe rformance of machine learning models in predicting treatment response to neoadju vant chemoradiotherapy (nCRT) in rectal cancer using computed tomography (CT) an d magnetic resonance imaging (MRI). We searched PubMed, Embase, Cochrane Library , and Web of Science for studies published before January 2023." The news correspondents obtained a quote from the research, "The Quality Assessm ent of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodolo gical quality of the included studies, random-effects models were used to calcul ate sensitivity and specificity, I2 values were used for heterogeneity measureme nts, and subgroup analyses were carried out to detect potential sources of heter ogeneity. A total of 1690 patients from 24 studies were included. The meta-analy sis calculated a pooled area under the curve (AUC) of 0.92 (95%CI-0 .89-0.94), pooled sensitivity of 0.81 (95%CI-0.73-0.88), and pooled specificity of 0.88 (95%CI-0.82-0.92). We investigated 4 studies t hat mainly contributed to heterogeneity. After performing meta-analysis again ex cluding these 4 studies, the heterogeneity was significantly reduced. In subgrou p analysis, the pooled AUC of the deep learning model was 0.95 and was 0.88 for the traditional statistical model; the pooled AUC of studies that used diffusion -weighted imaging (DWI) was 0.90, and was 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.94, and was 0.83 in studies c onducted in other countries. Machine learning has promising potential in predict ing tumor response to nCRT in patients with locally advanced rectal cancer. Toge ther with clinical information, machine-learning based models may bring us close r toward precision medicine. Compared to traditional machine learning models, de ep learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous."
ChengduPeople's Republic of ChinaAsi aCancerCyborgsDrugs and TherapiesEmerging TechnologiesGastroenterologyHealth and MedicineMachine LearningOncologyRectal Cancer
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