PT  - JOURNAL ARTICLE
AU  - Zheng, Sunyi
AU  - Wang, Shuo
AU  - Feng, Ziwei
AU  - Liang, Jing
AU  - Liu, Jiaxin
AU  - Yang, Xiaomeng
AU  - Zhang, Zhanshuo
AU  - Cui, Yuechen
AU  - Xie, Jiping
AU  - Fan, Shuxuan
AU  - Wang, Jing
AU  - Liao, Guoqing
AU  - Zhou, Haiyu
AU  - Ye, Zhaoxiang
AU  - Xiao, Jianyu
AU  - Shi, Lei
AU  - Cui, Xiaonan
AU  - Yue, Dongsheng
TI  - Temporal radiomics for non-invasive preoperative prediction of pathologic complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
AID  - 10.20892/j.issn.2095-3941.2025.0327
DP  - 2026 Jan 14
TA  - Cancer Biology & Medicine
PG  - 20250327
4099  - http://www.cancerbiomed.org/content/early/2026/01/14/j.issn.2095-3941.2025.0327.short
4100  - http://www.cancerbiomed.org/content/early/2026/01/14/j.issn.2095-3941.2025.0327.full
AB  - Objective: This study aimed to develop and validate a temporal radiomics model based on pre- and post-treatment CT scans for the preoperative prediction of pathologic complete response (pCR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunotherapy (NCI).Methods: Data from 263 patients with resectable NSCLC who underwent NCI followed by curative surgery and had both pre- and post-treatment CT scans were retrospectively collected. Patients from one hospital were randomly divided into training and internal test sets at a 7:3 ratio, while patients from two other hospitals served as the external test set. Radiomics features were extracted from the CT scans at both timepoints and delta features capturing the temporal changes were calculated. Radiomics models based on different features were developed using the least absolute shrinkage and selection operator for feature selection, followed by logistic regression. Model performance was evaluated using the area under the curve (AUC).Results: The radiomics model based on delta features yielded AUCs of 0.85, 0.76, and 0.72 in the training, internal test, and external test sets, respectively, which were superior to the radiomics models based on pre-treatment features (0.74, 0.66, and 0.62, respectively) and post-treatment features (0.80, 0.76, and 0.65, respectively). By integrating the optimal features from all three feature sources, the combined model achieved further improvements in performance, with AUCs of 0.89, 0.85, and 0.78, respectively, across the three sets.Conclusions: A CT-based radiomics model incorporating temporal features from pre- and post-treatment scans shows favorable performance for the non-invasive preoperative estimation of pCR to NCI in patients with NSCLC.The data generated in this study are available upon request from the corresponding author.