RT Journal Article
SR Electronic
T1 Temporal radiomics for non-invasive preoperative prediction of pathologic complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
JF Cancer Biology & Medicine
JO Cancer Biology & Medicine
FD China Anti-Cancer Association
SP 20250327
DO 10.20892/j.issn.2095-3941.2025.0327
A1 Zheng, Sunyi
A1 Wang, Shuo
A1 Feng, Ziwei
A1 Liang, Jing
A1 Liu, Jiaxin
A1 Yang, Xiaomeng
A1 Zhang, Zhanshuo
A1 Cui, Yuechen
A1 Xie, Jiping
A1 Fan, Shuxuan
A1 Wang, Jing
A1 Liao, Guoqing
A1 Zhou, Haiyu
A1 Ye, Zhaoxiang
A1 Xiao, Jianyu
A1 Shi, Lei
A1 Cui, Xiaonan
A1 Yue, Dongsheng
YR 2026
UL http://www.cancerbiomed.org/content/early/2026/01/14/j.issn.2095-3941.2025.0327.abstract
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.