Work

CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study

Year: 2026

Type: article

Abstract: Accurate risk assessment after EGFR-TKI resistance is important for guiding subsequent management of patients with EGFR-mutant lung adenocarcinoma. In this multicenter retrospective study, we developed and externally validated a computed tomography (CT)-based deep learning model using pretreatment CT images from 525 patients. A 2.5D ResNet-101 model showed consistent performance across training and external validation cohorts and enabled risk stratification for progression-free and overall survival. The deep learning score remained an independent prognostic factor after adjustment for clinical variables and demonstrated a continuous association with survival risk. These findings support the use of imaging-based deep learning approaches for individualized prognostic assessment in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance.

Source: iScience

DOI: 10.1016/j.isci.2026.117242

Authors: Shuai Qie, Yasong Shi, Jingyun Li, Sicong Jia, Ming Liu

Institutions: Hebei Medical University, Third Hospital of Hebei Medical University, Affiliated Hospital of Hebei University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,240