ArticleJournal of thoracic disease2025
Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.
Article in Journal of thoracic disease, 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
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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
5 citing papers in PubMed.
- Targeting ClassicalCancers · 2026Review
- Review
- Efficacy and mechanisms of cisplatin and sulforaphane nanoparticles in alleviating cisplatin resistance in non-small cell lung cancer.Translational lung cancer research · 2026Article
- Pharmacological strategies to overcome immune checkpoint inhibitor resistance in non-small cell lung cancer.Frontiers in oncology · 2025Review
- Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.Frontiers in medicine · 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
Background: Resistance to tyrosine kinase inhibitors remains a major clinical challenge in the treatment of non-small cell lung cancer (NSCLC) with activating epidermal growth factor receptor ( Methods: We conducted a multi-institutional retrospective study to develop and evaluate a multimodal machine learning model for predicting therapy resistance in late-stage NSCLC patients with Results: The multimodal model achieved a mean C-index of 0.82 across cross-validation folds, outperforming image-only and non-image models (C-index 0.75 and 0.77, respectively). Stratified analyses across institutions confirmed consistent performance gains with the multimodal approach. Kaplan-Meier analysis revealed that the multimodal model significantly stratified patients into distinct hazard groups (log-rank P=0.04), which unimodal models failed to achieve. Key predictors included Conclusions: This study presents a robust multimodal machine learning model for predicting therapy resistance in
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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.