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

• Published in: Scientific reports Vol. 14; no. 1; p. 20557
• Main Authors: Zhang, Yalin, Cui, Yongbin, Liu, Huiling, Chang, Cheng, Yin, Yong, Wang, Ruozheng
• Format: Journal Article
• Language: English
• Published: England 04.09.2024
• Subjects: Adult; Aged; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Female; Fluorodeoxyglucose F18; Humans; Kaplan-Meier Estimate; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Male; Middle Aged; Neoplasm Staging; Nomograms; Positron Emission Tomography Computed Tomography - methods; Prognosis; Proportional Hazards Models; Radiomics; ROC Curve; 18F-FDG PET/CT; Immunotherapy; Non-small cell lung cancer; Overall survive; Radiomics
• ISSN: 2045-2322

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 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 F-FDG PET/CT imaging with clinical indicators to enhance survival predictions for patients with stage III inoperable NSCLC.