ArticleNPJ precision oncology2025
Biopsy image-based deep learning for predicting pathologic response to neoadjuvant chemotherapy in patients with NSCLC.
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 5 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
5 citing papers in PubMed.
- A modular framework for antibody-drug conjugate biomarkers.Nature reviews. Clinical oncology · 2026Review
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- Immune checkpoint inhibitors in resectable non-small cell lung cancer: recent successes and ongoing challenges.Frontiers in oncology · 2026Review
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
- DeepTFtyper: an interpretable morphology-aware graph neural network for translating histopathology images into molecular subtypes in small cell lung cancer.Briefings in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Neoadjuvant chemotherapy (NAC) is a widely used therapeutic strategy for patients with resectable non-small cell lung cancer (NSCLC). However, individual responses to NAC vary widely among patients, limiting its effective clinical application. In this study, we propose a weakly supervised deep learning model, DeepDrRVT, which integrates self-supervised feature extraction and attention-based deep multiple instance learning, to improve NAC decision making from pretreatment biopsy images. DeepDrRVT demonstrated superior predictive performance and generalizability, achieving AUCs of 0.954, 0.872 and 0.848 for complete pathologic response, and 0.968, 0.893 and 0.831 for major pathologic response in the training, internal validation and external validation cohorts, respectively. The DeepDrRVT digital assessment of residual viable tumor correlated significantly with the local pathologists' visual assessment (Pearson r = 0.98, 0.80, and 0.59; digital/visual slope = 1.0, 0.8 and 0.55) and was also associated with longer disease-free survival (DFS) in all cohorts (HR = 0.455, 95% CI 0.234-0.887, P = 0.018; HR = 0.347, 95% CI 0.135-0.892, P = 0.021 and HR = 0.446, 95% CI 0.193-1.027, P = 0.051). Furthermore, DeepDrRVT remained an independent prognostic factor for DFS after adjustment for clinicopathologic variables (HR = 0.456, 95% CI 0.227-0.914, P = 0.027; HR = 0.358, 95% CI 0.135-0.949, P = 0.039 and HR = 0.419, 95% CI 0.181-0.974, P = 0.043). Thus, DeepDrRVT holds promise as an accessible and reliable tool for clinicians to make more informed treatment decisions prior to the initiation of NAC.
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.