ArticleCancer medicine2026
Histopathology Images-Based Deep Learning Prediction of Histological Types in Endometrial Cancer.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial intelligence as a pragmatic adjunct to molecular classification in endometrial cancer.Ewha medical journal · 2026Article
- Histopathology Images-Based Deep Learning Prediction of Histological Types in Endometrial Cancer.Cancer medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
backgroundAccording to the new 2023 International Federation of Gynecology and Obstetrics staging system for endometrial cancer (EC), EC is classified into aggressive and nonaggressive histological types. Accurate diagnosis of the histological type of EC is crucial for optimizing treatment strategies and predicting patient outcomes.
objectivesTo develop and validate a deep convolutional neural network for predicting nonaggressive versus aggressive histological types from hematoxylin and eosin (H&E)-stained images of EC specimens.
methodsA deep convolutional neural network named EC-AI
resultsEC-AI
conclusionsEC-AI
Indexed as
Identifiers
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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.