ArticleNPJ digital medicine2025
Deep multimodal fusion of patho-radiomic and clinical data for enhanced survival prediction for colorectal cancer patients.
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 6 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
6 citing papers in PubMed.
- Real-World Imaging Data: Opportunities and Challenges.JMIR medical informatics · 2026Article
- Review
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This study introduces PRISM-CRC, a novel deep learning framework designed to improve the diagnosis and prognosis of colorectal cancer (CRC) by integrating histopathology, radiology, endoscopy and clinical data. The model demonstrated high accuracy, achieving a concordance index of 0.82 for predicting 5-year disease-free survival and an AUC of 0.91 for identifying microsatellite instability (MSI) status. A key finding is the synergistic power of this multimodal approach, which significantly outperformed models using only a single data type. The PRISM-CRC risk score proved to be a strong, independent predictor of survival, offering more granular risk stratification than the traditional TNM staging system. This capability has direct clinical implications for personalizing treatment, such as identifying high-risk Stage II patients who might benefit from adjuvant chemotherapy. The study acknowledges limitations, including a modest performance decrease due to "domain shift" and classification errors in morphologically ambiguous cases, highlighting the need for future prospective trials to validate its clinical utility.
Identifiers
What OpenQuestion holds
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.