CT based Quantification of Intratumoral and Peritumoral Heterogeneity for diagnosing Lymphovascular Invasion for Early Stage Non-small cell lung cancer

Objective: To establish a model integrates clinical, traditional radiologic, intratumoral and peritumoral radiomics (ITR and PTR), and intratumoral and peritumoral heterogeneity (ITH and PTH) features to diagnose lymphovascular invasion (LVI) status for early stage non small cell lung cancer (NSCLC). Materials and Methods: Clinical data and chest CT imaging data of NSCLC patients who underwent surgical resection of the lungs from January 2019 to May 2021 were collected. Surgical pathology were the diagnostic gold standard to clarify the LVI status. ITR and PTR features and ITH and PTH features from the total tumor volume and peritumoral tumor volume were extracted. Then clinical, traditional radiologic, ITR and PTR, ITH and PTH models were established to diagnose LVI status. Finally, a column chart diagnostic model was constructed and the diagnostic efficacy was evaluated. Results: 308 NSCLC cases met the inclusion criteria, including 117 cases in the LVI positive group and 191 cases in the LVI negative group. They were randomly divided into training group and validation group in a 1:1 ratio. In the three cohorts of PTR_(0 - 3, -3 - 3 and 0 - 6), the PTR_0 - 6 model has better predictive performance, with area under the curve (AUC) of 0.882 and 0.824 for the training and validation groups, respectively. Gender, Vascular Convergence Sign, and N stagewere significantly related to LVI status, Finally, the combined model integrated ITH, PTR_0-6, and PTH_0-6 models, N stage and Vascular Convergence Sign has the highest diagnostic accuracy. The AUC in the training group is 0.962 and in the validation group is 0.882. Conclusions: A comprehensive diagnostic model based on clinical features, traditional radiological features, radiomic features, and heterogeneity features of NSCLC were established to diagnose LVI for early stage NSCLC, which has the highest diagnostic efficiency and can help to guide treatment decisions.