ArticleJCO clinical cancer informatics2025
Artificial Intelligence-Based Model Exploiting Hematoxylin and Eosin Images to Predict Rare Gene Mutations in Patients With Lung Adenocarcinoma.
Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAccurately identifying gene mutations in lung cancer is crucial for treatment, while molecular diagnostic methods are time-consuming and complex. This study aims to develop an advanced deep learning model to address this issue.
methodsIn this study, the ResNeXt101 model framework was established to predict the gene mutation status in lung adenocarcinoma. The model was trained and validated using data from two cohorts: cohort 1, comprising 144 patients from the First Affiliated Hospital of China Medical University, and cohort 2, which includes 69 patients from the The Cancer Genome Atlas-Lung Adenocarcinoma public database. The model was trained and validated on the two data sets, respectively, and they served as external test sets for each other to further verify the performance of the model. Additionally, we tested the trained model on a metastatic cancer data set, which included metastases to organs outside the lungs. The performance of the model was evaluated using the AUC, accuracy, precision, recall, and F1 score.
resultsIn cohort 1, the model achieved an AUC ranging from 0.93 to 1. In the external test on cohort 2, it performed well in predicting five of the six genes (AUC = 0.85-1). When tested on the metastatic cancer data set, it successfully predicted mutations of three of the six genes (AUC = 0.72-0.80).
conclusionThe artificial intelligence model developed in this study has a high accuracy in predicting gene mutations in lung adenocarcinoma, which is conducive to improving the management of patients with lung adenocarcinoma and promoting precision medicine.
Indexed as
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.