Evidence map›Paper›PMID 40640822›Full record

ArticleBMC pulmonary medicine2025

Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.

Jiayi Li, Renjie Xu, Dan Wang, Zhanlue Liang, Yangqian Li, Qinglan Wang, Lingfeng Bi, Yawen Qi, Yaojie Zhou, Weimin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jiayi Li *Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Renjie Xu *Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Dan Wang *Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Zhanlue LiangDepartment of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Yangqian LiDepartment of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Qinglan WangDepartment of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Lingfeng BiDepartment of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Yawen QiDepartment of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China.
Yaojie ZhouShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China. zhouyj95@qq.com.
Weimin LiDepartment of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Guo Xue Xiang 37, Chengdu, Sichuan, 610041, China. weimin003@163.com.

Funding

National Natural Science Foundation of China 92159302Noncommunicable Chronic Diseases-National Science and Technology Major Project 2023ZD0506102; 2023ZD0506100
6 · The paper itself

Abstract

objectivesTo investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status.

methodsWe retrospectively included 2171 patients with non-small cell lung cancer (NSCLC) with pre-treatment computed tomography (CT) scans and predicting epidermal growth factor receptor (EGFR) gene sequencing from West China Hospital between January 2013 and April 2024. The deep-learning model was built for predicting EGFR / tumor protein 53 (TP53) co-occurrence status. The model performance was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further compared multi-dimension model with three one-dimension models separately, and we explored the value of combining clinical factors with machine-learning factors. Additionally, we investigated 546 patients with 56-panel next-generation sequencing and low-dose computed tomography (LDCT) to explore the biological mechanisms of radiomics.

resultsIn our cohort of 2171 patients (1,153 males, 1,018 females; median age 60 years), single-dimensional models were developed using data from 1,055 eligible patients. The multi-dimensional model utilizing a Random Forest classifier achieved superior performance, yielding the highest AUC of 0.843 for predicting EGFR/TP53 co-mutations in the test set.

conclusionThe multi-dimensional model demonstrates promising potential for non-invasive prediction of EGFR and TP53 co-mutations, facilitating early and informed clinical decision-making in NSCLC patients at risk of treatment resistance.

Indexed as

Carcinoma, Non-Small-Cell LungDrug Resistance, NeoplasmLung NeoplasmsProtein Kinase InhibitorsTumor Suppressor Protein p53AdultAgedChinaDeep LearningErbB ReceptorsFemaleHumansMaleMiddle AgedMutationRetrospective StudiesEGFR protein, humanErbB ReceptorsProtein Kinase InhibitorsTP53 protein, humanTumor Suppressor Protein p53GenotypeMachine learningMulti-modelNon-small-cell lung cancerRadiomics

Identifiers

PMID40640822
PMCPMC12243385

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.