Pretreatment CT-Based Machine Learning Radiomics Model Predicts Response in Inoperable Stage III NSCLC Treated with Concurrent Radiochemotherapy Plus PD-1 Inhibitors
Objective To develop and validate a CT-based radiomics model for predicting sequential immunotherapy response after concurrent radiochemotherapy (CCRT) in patients with unresectable stage III non-small cell lung cancer (NSCLC). Methods The study retrospectively included 71 patients who received sequ...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-06-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338251351109 |
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| Summary: | Objective To develop and validate a CT-based radiomics model for predicting sequential immunotherapy response after concurrent radiochemotherapy (CCRT) in patients with unresectable stage III non-small cell lung cancer (NSCLC). Methods The study retrospectively included 71 patients who received sequential immunotherapy after concurrent chemoradiotherapy (CCRT) between January 2019 and December 2022, while prospectively including 17 additional patients between January 2023 and July 2023. The study documented each patient's tumor response and prognosis within two months of completing treatment. Patients were then categorized based on their treatment response, resulting in the identification of two distinct groups: treatment-responsive group and treatment-insensitive group. First, ITK-SNAP software was used to delineate the primary tumor lesions in the lung window and define a region of interest (ROI). Second, features were extracted using Python (version 3.6) and filtered using Least absolute shrinkage and selection operator regression. Third, radiological models were built using six machine learning algorithms: logistic regression (LR), discriminant analysis (DA), neural network (NN), random forest (RF), support vector machine (SVM) and K-Nearest Neighbour (KNN). Finally, Kaplan-Meier survival analysis was performed for high- and low-risk patients predicted by radiomic modeling. Results Based on the performance of radiomics models constructed by various machine learning algorithms in the prospective validation set, the LR with the highest AUC value (AUC: 90.00%) was finally selected, which also performed well in the independent test set (AUC: 84.96%). Risk stratification of patients based on the radiomic model constructed by LR was excellent for PFS (P = 0.001) and OS (P = 0.019) in the training set, PFS (P = 0.010) and OS (P = 0.028) in the prospective validation set, and PFS (P = 0.014) and OS (P = 0.041) in the test set. Conclusion Pretreatment CT-based radiomics model accurately and efficiently predicts treatment response and risk stratification in patients with unresectable stage III NSCLC treated with concurrent chemoradiotherapy and sequential programmed death-1 inhibitor therapy. Prior to prospective data collection, the study was registered with the China Clinical Trial Registry under the trial registration name: Prediction of concurrent chemoradiotherapy efficacy and its related molecular signaling pathway by medical artificial intelligence model based on CT of lung cancer, with the registration number: ChiCTR2100053175 ( https://www.chictr.org.cn/showproj.html?proj = 136872 ). |
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| ISSN: | 1533-0338 |