{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhou X"],"funding":["Innovative Research Team (in Science and Technology) in University of Henan Province","Henan Province Key Research and Development and Promotion Projects","National Natural Science Foundation of China","Major Science and Technology Projects of Henan Province","National Key Clinical Discipline, and the program of Guangdong Provincial Clinical Research Center for Digestive Diseases"],"pagination":["1523"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11658449"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<h4>Background</h4>Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.<h4>Methods</h4>A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent featur"],"journal":["BMC cancer"],"pubmed_title":["Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence."],"pmcid":["PMC11658449"],"funding_grant_id":["82201709","24IRTSTHN042","232102310315","2020B1111170004","232102310009","82302298","221100310500"],"pubmed_authors":["Liu Y","Cao W","Lu Y","Zhao Q","Wang C","Gao Z","Zhou X","Dai J","Zhao Z","Zhao Y","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence.","description":"<h4>Background</h4>Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.<h4>Methods</h4>A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent featur","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-04T21:46:16.127Z","creation":"2025-04-04T21:46:16.127Z"},"accession":"S-EPMC11658449","cross_references":{"pubmed":["39696090"],"doi":["10.1186/s12885-024-13292-5"]}}