Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study

Rationale and objectives: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat of the tumor and its peritumoral microenvironment to predict progression-free survival (PFS) in patients with clinical stage IA pure-solid non-small cell lung cancer (NSCLC).

Materials and methods: 234 Patients who underwent lung resection for NSCLC from two hospitals were reviewed. Radiomic features were extracted from both intratumoral, peritumoral and habitat regions on PET. Univariate and multivariate logistic regression analyses were employed to determine significant clinical variables. Subsequently, a radiomics nomogram was developed by combining the radiomics signature with these identified clinical variables. Kaplan-Meier (KM) analysis was performed to investigate the prognostic value of the nomogram. Shapley Additive Explanations (SHAP) were used to interpret the ML models.

Results: The combination model which contained peritumoral 5 mm and habitat regions radiomics features, clinical variables obtained a strong well-performance, achieving area under the curve (AUC) of 0.905 (95% confidence interval (CI) 0.854-0.957) in the train set and 0.875 (95% CI 0.789-0.962) in the internal validation set. The radiomics signature was significantly associated with PFS, the model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use showed low-risk score given have far longer RFS than those with high-risk score (log-rank P＜0.001).

Conclusion: The habitat and peritumoral radiomics signatures serve as an independent biomarker for predicting PFS in patients with early-stage NSCLC, effectively stratified survival risk among patients with clinical stage IA pure-solid non-small cell lung cancer.