首页|University of Sao Paulo Reports Findings in Artificial Intelligence (Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder can cer: A comprehensive systematic review and meta-analysis)
University of Sao Paulo Reports Findings in Artificial Intelligence (Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder can cer: A comprehensive systematic review and meta-analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting out of Sao Paulo, Brazil, by News Rx editors, research stated, “Neoadjuvant cisplatinbased combination chemotherap y (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of respons e to NAC is essential for clinical decision-making regarding alternatives in cas e of non-response and bladder-sparing in case of complete response.” Our news journalists obtained a quote from the research from the University of S ao Paulo, “This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC. A systematic review adhering to the PRISMA guidelines was conducted until July 20 23. The study integrated articles relating to artificial intelligence and NAC re sponse in MIBC from various databases. The quality of articles was evaluated usi ng the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A m eta-analysis was subsequently performed on selected studies to determine the sen sitivity and specificity of machine learning algorithms in predicting NAC respon se. Of 655 articles identified, 12 studies comprising 1523 patients were include d, and four studies were eligible for meta-analysis. The sensitivity and specifi city of the studies were 0.62 (95% confidence interval [CI] 0.50-0.72) and 0.82 (95% CI 0.72-0.89), res pectively, with a heterogeneity score (I) of 38.5%. The machine lea rning algorithms used computed tomography, genetic, and anatomopathological data as input and exhibited promising potential for predicting NAC response. Machine -learning algorithms, especially those using computed tomography, genetic, and p athologic data, demonstrate significant potential for predicting NAC response in MIBC.”
Sao PauloBrazilSouth AmericaAlgori thmsArtificial IntelligenceBladder CancerCancerChemotherapyCyborgsDr ugs and TherapiesEmerging TechnologiesHealth and MedicineMachine LearningOncology
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