{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jimenez-Sanchez D"],"funding":["Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)","Ministry of Economy and Competitiveness | Instituto de Salud Carlos III"],"pagination":["48"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10036616"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(1)"],"pubmed_abstract":["Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping."],"journal":["NPJ digital medicine"],"pubmed_title":["Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images."],"pmcid":["PMC10036616"],"funding_grant_id":["PI17/01723 and PI21/00920"],"pubmed_authors":["Jimenez-Sanchez D","Masetto I","Lopez-Janeiro A","Villalba-Esparza M","Ortiz-de-Solorzano C","Melero I","Hardisson D","Ariz M","Goubert V","Lozano MD","de Andrea CE","Kadioglu E"],"additional_accession":[]},"is_claimable":false,"name":"Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images.","description":"Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2025-04-18T17:20:26.779Z","creation":"2025-04-07T04:52:53.06Z"},"accession":"S-EPMC10036616","cross_references":{"pubmed":["36959234"],"doi":["10.1038/s41746-023-00795-x"]}}