ArticleNPJ precision oncology2025
Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Dissecting oral premalignant carcinogenesis: spatial omics mechanisms and nanomedicine-driven therapeutic innovation.Molecular cancer · 2026Review
- Profiling the tumor immunological status in Japanese solid cancers using the tumor immune status scoring algorithm.Scientific reports · 2026Article
- Scientific Mapping and Knowledge Evolution of Global Areca Research.International dental journal · 2026Review
- Integrative single-cell and machine-learning analysis identifies acBMC cancer · 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
9 authors.
Funding
Abstract
Survival assessment for oral squamous cell carcinoma (OSCC) remains a significant clinical challenge. This study develops novel artificial intelligence (AI) platforms for assessing overall survival in OSCC patients based on 240 whole-slide images from multicenter cohorts. A comprehensive evaluation is conducted on four convolutional neural network architectures under two distinct deep learning (DL) training paradigms: supervised DL with precise annotations (PathS model, c-index = 0.809), and weakly supervised DL using slide-level labels without manual annotations (c-index = 0.707). Gradient-weighted class activation mapping reveals novel AI-based prognostic insights to simultaneously identify tumor cells and tumor-infiltrating immune cells as key predictive features. Additionally, our platform achieved significantly improved accuracy compared to conventional clinical signatures (CS model, c-index = 0.721). Furthermore, the clinical potential is enhanced through the development of a multimodal nomogram combining PathS signatures with CS (c-index = 0.817), representing a substantial advancement in personalized survival assessment for OSCC patients.
Identifiers
What Socratic 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 Socratic graph.