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Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer

   
@article{TLCR120029,
	author = {Fan Jiang and Nan-Feng Zhang and Yi Gao and Xin Chen and En-Tao Liu and Tian Mou},
	title = {Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer},
	journal = {Translational Lung Cancer Research},
	volume = {15},
	number = {6},
	year = {2026},
	keywords = {},
	abstract = {Background: Accurate pathological subtyping and epidermal growth factor receptor (EGFR) mutation profiling are critical for personalized non-small cell lung cancer (NSCLC) management. However, traditional invasive biopsies possess inherent limitations in dynamic monitoring and capturing tumor heterogeneity. While dual-modal positron emission tomography/computed tomography (PET/CT) imaging provides valuable non-invasive phenotypic insights, deep learning models that jointly fuse these modalities for simultaneous prediction while maintaining clinical interpretability remain scarce. Therefore, this study proposes an integrated dual-modal PET/CT radiomics framework for the simultaneous prediction of pathological subtypes and EGFR mutation status in NSCLC.Methods: This retrospective study included a total of 384 NSCLC patients with PET/CT images across three independent cohorts. From CT images, sub-regional radiomic features were systematically extracted, while PET images provided spatial metabolic heterogeneity descriptors. Building on these, a Dual-Modal Dual-task Prediction (DMDP) model was developed. This model employs a multi-scale cross-attention mechanism to fuse PET/CT information and utilizes a dual-task learning strategy to synergistically predict both EGFR mutation and pathological subtype. The model’s efficacy was fully validated through ablation studies, and its decision interpretability was assessed using gradient-weighted class activation mapping (Grad-CAM) heatmaps.Results: Significant differences were identified in PET metabolic parameters and imaging heterogeneity across pathological subtypes and EGFR mutation states (P},
	issn = {2226-4477},	url = {https://tlcr.amegroups.org/article/view/120029}
}