首页|Third Affiliated Hospital of Chongqing Medical University Reports Findings in Me lanoma (Machine learning in the prediction of immunotherapy response and prognos is of melanoma: a systematic review and meta-analysis)
Third Affiliated Hospital of Chongqing Medical University Reports Findings in Me lanoma (Machine learning in the prediction of immunotherapy response and prognos is of melanoma: 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 - Melanoma is the subject of a report. According to news originating from Chongqing, People's Republic of China, by NewsRx correspondents, research stated, "The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive t o immunotherapy and effective tools for early identification of this patient pop ulation are still lacking." Our news journalists obtained a quote from the research from the Third Affiliate d Hospital of Chongqing Medical University, "Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic re view and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy. Relevant stu dies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the inc luded studies were assessed using the Prediction Model Risk of Bias Assessment T ool (PROBAST). Meta-analysis was performed on R4.2.0. A total of 36 studies cons isting of 30 cohort studies and 6 case-control studies were included. These stud ies were mainly published between 2019 and 2022 and encompassed 75 models. The o utcome measures of this study were progression-free survival (PFS), overall surv ival (OS), and treatment response. The pooled c-index was 0.728 (95% CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0 .792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721 -0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and v alidation sets, respectively. Machine learning has considerable predictive accur acy in melanoma immunotherapy response and prognosis, especially in the former."
ChongqingPeople's Republic of ChinaA siaCancerCy-borgsDrugs and TherapiesEmerging TechnologiesHealth and Med icineImmunologyImmunotherapyMachine LearningMelanomaOncology
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