ArticleNPJ digital medicine2023
Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing.Journal for immunotherapy of cancer · 2025Guideline
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- TAMs in the Gynecological Tumor Microenvironment: Insights from Cross-Cancer Studies for Immunotherapy.Cancers · 2026Review
- Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images.IEEE transactions on medical imaging · 2026Article
- Histopathology Images-Based Deep Learning Prediction of Histological Types in Endometrial Cancer.Cancer medicine · 2026Article
- Society for Immunotherapy of Cancer: Standards for Reporting of Multiplex Immunohistochemistry/Immunofluorescence Assays (STORMI).Journal for immunotherapy of cancer · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- TRAITER: transformer-guided diagnosis and prognosis of heart failure using cell nuclear morphology and DNA damage marker.Bioinformatics (Oxford, England) · 2024Article
- Deep Learning for Grading Endometrial Cancer.The American journal of pathology · 2024Article
- Spatial tumor immune microenvironment phenotypes in ovarian cancer.NPJ precision oncology · 2024Article
- Prediction of recurrence risk in endometrial cancer with multimodal deep learning.Nature medicine · 2024Article
- Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities.Journal for immunotherapy of cancer · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.