Prognostic nomogram combining 18F-FDG PET/CT radiomics and clinical data for stage III NSCLC survival prediction

• Published in: Scientific reports Vol. 14; no. 1; pp. 20557 - 12
• Main Authors: Zhang, Yalin, Cui, Yongbin, Liu, Huiling, Chang, Cheng, Yin, Yong, Wang, Ruozheng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 18F-FDG PET/CT; 631/67/1612/1350; 692/700/1421/2160; Computed tomography; Humanities and Social Sciences; Immunotherapy; Lung cancer; multidisciplinary; Nomograms; Non-small cell lung cancer; Non-small cell lung carcinoma; Overall survive; Patients; Positron emission tomography; Prediction models; Radiomics; Science; Science (multidisciplinary); Small cell lung carcinoma; Survival; Tomography; Overall survive; F-FDG PET/CT; Immunotherapy; Non-small cell lung cancer; Radiomics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-71003-3

The aim of this study was to establish and validate the precision of a novel radiomics approach that integrates 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) scan data with clinical information to improve the prognostication of survival rates in patients diagnosed with stage III Non-Small Cell Lung Cancer (NSCLC) who are not candidates for surgery. We evaluated pretreatment 18 F-FDG PET-CT scans from 156 individuals diagnosed with stage III inoperable NSCLC at Shandong Cancer Hospital. These individuals were divided into two groups: a training set comprising 110 patients and an internal validation set consisting of 46 patients. By employing random forest classifier and cox proportional hazards model , we identified and utilized relevant features to create predictive models and a nomogram. The effectiveness of these models was assessed through the use of the receiver operating characteristics(ROC) curves, Kaplan–Meier (KM) curves, and the application of the nomogram. Our findings showed that the combined model, which integrates both clinical and radiomic data, outperformed those based solely on clinical or radiomic features in predicting 3-year overall survival(OS). Furthermore, calibration plots revealed a high level of agreement between predicted and actual survival times. The research successfully established a predictive radiomics model that integrates 18 F-FDG PET/CT imaging with clinical indicators to enhance survival predictions for patients with stage III inoperable NSCLC.