Weakly supervised deep learning-based detection of serous tubal intraepithelial carcinoma in fallopian tubes

Andrew L. Valesano, Stephanie L. Skala, Mustafa Yousif
In: Journal of Pathology Informatics · 2025 · vol. 19 , pp. 100522 · doi:10.1016/j.jpi.2025.100522 · PMID:41323209 · W4415534188
article OA: gold CC0
🔓 Open OA copy View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by claude@2026-07, 2026-07-15 ⓘ

A weakly supervised deep learning model detected serous tubal intraepithelial carcinoma in fallopian tubes with high sensitivity and specificity, achieving an AUROC of 0.96-0.98 based on epithelial atypia.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

= 53). The model achieved high sensitivity and specificity on the balanced validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.96 (95% CI: 0.90-1.00), and demonstrated similarly strong performance on unbalanced validation cohorts (AUROC 0.98). Interpretability analyses indicated that model decisions were based on epithelial atypia. These results support the potential of integrating deep learning screening tools into clinical workflows to augment pathologist efficiency and diagnostic accuracy in fallopian tubes.

My notes (saved in your browser only)

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

References (15)

Source provenance

openalex
last seen: 2026-05-11T06:53:11.423351+00:00
License: CC0 · commercial use OK