ArticleNPJ digital medicine2025
Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- Review
- From Label-Free Multiphoton Imaging to Pathological Reports: A Vision-Language Breast Cancer Margin Pathological Diagnosis System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Quantitative label-free multiphoton imaging of collagen features predicts disease severity and progression in idiopathic pulmonary fibrosis.Respiratory research · 2026Observational
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Colorectal cancer recurrence remains a major challenge after curative resection, and accurate tools for early risk assessment are essential to stratify patients and guide personalized therapeutic planning. We developed MPMRecNet, a dual-stream deep learning model for predicting recurrence using multiphoton microscopy imaging of formalin-fixed paraffin-embedded tissue sections from 1071 patients across two hospitals. MPMRecNet employs MaxViT-based encoders, cross-modal attention fusion, and classification under focal loss with mixed-precision optimization. It achieved strong external validation performance (ROC-AUC = 0.849, PR-AUC = 0.664), outperforming traditional clinical predictors. Multivariable analysis confirmed MPMRecNet as the most powerful independent predictor of recurrence (OR = 5.66, p < 0.001), and a combined nomogram incorporating clinical variables further improved stratification (ROC-AUC = 0.872). MPMRecNet offers a non-destructive tool for recurrence prediction from routine pathology slides, supporting precise risk assessment and postoperative surveillance.
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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.