ArticleNPJ precision oncology2025
AI-powered prediction model for neoadjuvant chemotherapy efficacy: comprehensive analysis of breast cancer histological images.
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 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.
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Breast cancer patients exhibit variable responses to neoadjuvant therapy (NAT), necessitating robust predictive biomarkers. We developed an artificial intelligence (AI)-driven integrated predictive model (IPM) combining histopathological, clinical, and immune features to address this challenge. Using whole-slide images from 1035 patients across four centers, we compared tumor epithelium (TE-score), stroma (TS-score), and whole-tumor (TR-score) deep learning biomarkers, identifying TR-score as optimal (AUC = 0.729 vs. 0.686/0.719 for TE/TS-scores). The IPM, incorporating TR-score and clinical variables, demonstrated superior NAT response prediction versus clinicopathological models (validation AUC = 0.780 vs. 0.706, p < 0.001), with 10% higher accuracy. Immune profiling further enhanced performance (AUC = 0.831 vs. 0.822, p = 0.183). These results establish the biological and clinical validity of TR-score for characterizing tumor-stroma interactions, with IPM providing a generalizable framework for precision oncology. The model's stability across multicenter cohorts (AUCs 0.781-0.816) and incremental value of immune data suggest its utility in guiding NAT decision-making and trial stratification.
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.