Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal–Hilar Lymph Nodes in Non-Small-Cell Lung Cancer - Ouyang, Ming-li; Wang, Yi-ran; Deng, Qing-shan; Zhu, Ye-fei; Zhao, Zhen-hua; Wang, Ling; Wang, Liang-xing; Tang, Kun (Frontiers in Oncology, 2021)
Background: Accurate evaluation of lymph node (LN) status is critical for determining the treatment options in patients with non-small cell lung cancer (NSCLC). This study aimed to develop and validate a 18F-FDG PET-based radiomic model for the identification of metastatic LNs from the hypermetabolic mediastinal-hilar LNs in NSCLC. Methods: We retrospectively reviewed 259 patients with hyper-metabolic LNs who underwent pretreatment 18F-FDG PET/CT and were pathologically confirmed as NSCLC from two centers. 228 LNs were allocated to a training cohort (LN = 159) and an internal validation cohort (LN = 69) from one center (7:3 ratio), and 60 LNs were enrolled to an external validation cohort from the other. Radiomic features were extracted from LNs of PET images. PET radiomics signature was constructed by multivariable logistic regression after using the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation. The PET radiomics signature (model 1) and independent predictors from CT image features and clinical data (model 2) were incorporated into a combined model (model 3). A nomogram was plotted for the complex model, and the performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. Results: The area under the curve (AUC) values of the model 1 were 0.820,0.785 and 0.808 in the training cohort, internal, and external validation cohort, respectively, showing good diagnostic efficacy for lymph nodes metastasis (LNM). Furthermore, the model 2 was able to discriminate metastatic LNs in the training cohort (AUC 0.780), internal (AUC 0.794), and external validation cohort (AUC 0.802), respectively. Model 3 showed optimal diagnostic performance among the three cohorts, with an AUC of 0.874, 0.845, and 0.841, respectively. The nomogram based on the model 3 showed good discrimination and calibration. Conclusions: Our study revealed that PET radiomics signature, especially when integrated with CT imaging features, showed the ability to identify true and false positives of mediastinal–hilar LNM detected by PET/CT in patients with NSCLC, which would help clinicians to make individual treatment decisions.