{"title":"Omitting study limitations might have implications for the patients","authors":"Paul de Boissieu, Sylvie Chevret","doi":"10.1016/s1470-2045(24)00566-7","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00566-7","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Site-specific or empirical chemotherapy for cancer of unknown primary: the right answer?","authors":"Ryan Huey, Kanwal Raghav","doi":"10.1016/s1470-2045(24)00562-x","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00562-x","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean-Yves Blay, Sylvie Chabaud, David Perol, Axel Le Cesne
{"title":"Omitting study limitations might have implications for the patients – Authors' reply","authors":"Jean-Yves Blay, Sylvie Chabaud, David Perol, Axel Le Cesne","doi":"10.1016/s1470-2045(24)00571-0","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00571-0","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spyridon Bakas, Philipp Vollmuth, Norbert Galldiks, Thomas C Booth, Hugo J W L Aerts, Wenya Linda Bi, Benedikt Wiestler, Pallavi Tiwari, Sarthak Pati, Ujjwal Baid, Evan Calabrese, Philipp Lohmann, Martha Nowosielski, Rajan Jain, Rivka Colen, Marwa Ismail, Ghulam Rasool, Janine M Lupo, Hamed Akbari, Joerg C Tonn, Raymond Y Huang
{"title":"Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice","authors":"Spyridon Bakas, Philipp Vollmuth, Norbert Galldiks, Thomas C Booth, Hugo J W L Aerts, Wenya Linda Bi, Benedikt Wiestler, Pallavi Tiwari, Sarthak Pati, Ujjwal Baid, Evan Calabrese, Philipp Lohmann, Martha Nowosielski, Rajan Jain, Rivka Colen, Marwa Ismail, Ghulam Rasool, Janine M Lupo, Hamed Akbari, Joerg C Tonn, Raymond Y Huang","doi":"10.1016/s1470-2045(24)00315-2","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00315-2","url":null,"abstract":"Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"126 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}