Deep learning radiomics clinical model of PET/CT for predicting lymphovasvular invasion and prognosis in patients with non-small cell lung cancer.
Authors
Affiliations (2)
Affiliations (2)
- Department of Nuclear Medicine, Ningbo Mingzhou Hospital, Ningbo, Zhejiang, China.
- Department of Nuclear Medicine, Suzhou Hongci Hospital, Suzhou, Jiangsu, China.
Abstract
This study aimed to develop and validate an integrated model combining radiomics, deep learning (DL) features from pretreatment 2-18fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography, and clinical variables to predict lymphovascular invasion (LVI) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC). Data from 289 patients with clinically T1-3N0M0 NSCLC from Ningbo Mingzhou Hospital were retrospectively analyzed. Patients were randomly divided into a training cohort (n = 231) and an internal validation cohort (n = 58) in an 8:2 ratio. An external validation cohort (n = 93) from Suzhou Hongci Hospital was included. Radiomics features were extracted from manually segmented tumor volumes using PyRadiomics, whereas DL features were extracted from the largest axial tumor slice and its 2 adjacent slices. Feature selection was performed using Spearman correlation, minimum redundancy maximum relevance algorithm, and Least Absolute Shrinkage and Selection Operator regression. The selected features were used to train and compare multiple machine learning classifiers. Subsequently, 7 models (Clinical, Radiomics, DL, Radiomics-Clinical, DL-Clinical, DL-Radiomics, and DL-Radiomics-Clinical) were evaluated based on the area under the curve (AUC) and accuracy. Patients were stratified into high- and low-risk groups using the optimal Youden index for PFS comparison. The deep learning-radiomics-clinical (DLRC) model demonstrated superior performance, with AUCs of 0.91, 0.81, and 0.84 in the training, internal validation, and external validation cohorts, respectively. In subgroup analyses, the DLRC model showed robust performance for LVI prediction, with AUCs of 0.87 (T1), 0.77 (T2-3), 0.85 (adenocarcinoma), and 0.83 (squamous carcinoma). Kaplan-Meier analysis revealed a significantly shorter PFS in the high-risk group than in the low-risk group (P < .05). The integrated DLRC model enables accurate and noninvasive prediction of LVI status and PFS in NSCLC patients, which can guide personalized treatment strategies and aid in identifying candidates for postoperative adjuvant therapy.