Explainable PET-based intratumoral and peritumoral machine learning model for predicting visceral pleural invasion in clinical-stage IA non-small cell lung cancer: A two-center study.

AIM: The aim of this study was to develop a PET-based machine learning model for predicting visceral pleural invasion (VPI) in patients with clinical stage IA non-small cell lung cancer.

MATERIALS AND

METHODS

A total of 294 patients and 69 patients from two institutions who underwent the 18F-FDG-PET scan were retrospectively analyzed. We extracted PET-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 4, 8 and 12 mm regions (GPTV4, GPTV8, GPTV12), respectively. Then four models were respectively established by using machine learning algorithms. The performance of the models was assessed by the receiver operating characteristic (ROC) curve and decision curve analyses (DCA). Shapley additive explanation (SHAP) was employed to explain the machine learning (ML) models and visualize variable predictions.

RESULTS

Compared with GTV, GPTV4, and GPTV12 radiomics models, the radiomics model based on GPTV8 using random forest (RF) among the 10 features demonstrated better prediction performance, with the AUC of 0.879, 0.846, and 0.745 in the training, internal validation, and external validation sets, respectively. The results of the SHAP method showed that the GLRLM_ShortRunLowGreyLevel Emphasis features were the most important factors in VPI. At the patient level, SHAP force plots provided a deep understanding for predicting VPI.

CONCLUSION

The PET-based intratumoral and peritumoral model based on machine learning offers an innovative tool for preoperative prediction of VPI in patients with lung adenocarcinoma. By employing the SHAP method, clinicians may gain a clearer insight into the factors contributing to VPI, which could enhance clinical decision-making of prognosis assessment.