ArticleNPJ precision oncology2026
Predicting primary resistance to third-generation EGFR-TKIs in lung adenocarcinoma using a multisource cross-modal transformer model.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
The aim of this study was to investigate the utility of a multisource cross-modal Transformer (MC-Trans) model in predicting primary resistance to third-generation epidermal growth factor receptor tyrosine kinase inhibitors (3rd-EGFR-TKIs) in patients with lung adenocarcinoma. A retrospective analysis of clinical and imaging data from 222 lung adenocarcinoma patients treated with 3rd-EGFR-TKIs was conducted. Patients were allocated to a training/validation cohort (n = 136) and two external test cohorts (n = 34 and n = 52). The Table Transformer and a Swin Transformer-based model were employed to extract features from tabular and CT imaging data, respectively, to construct the MC-Trans model. The results demonstrated that MC-Trans exhibited excellent performance in predicting primary resistance, with an ROC-AUC of 0.89, which was significantly superior to those of the unimodal models (tabular model: 0.78; CT model: 0.63). Furthermore, predictions on external Test Cohort 2 revealed that the predictive performance of MC-Trans was comparable to that of a human expert panel. The study also revealed that MC-Trans could predict the risk of disease progression even among patients without primary resistance. In conclusion, MC-Trans may serve as a valuable tool to assist in determining the therapeutic response of lung adenocarcinoma patients to 3rd-EGFR-TKIs. Keywords: Artificial intelligence; Lung Cancer; Multimodal models; Third-generation EGFR-TKIs; Primary Resistance.
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.