CT based quantification of intratumoral and peritumoral heterogeneity for diagnosing lymphovascular invasion for early stage non-small cell lung cancer

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). 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. 366 NSCLC patients were enrolled in this retrospective study from 2 institutions, in which Institution 1 served as the basis for training (n = 154) and internal validation (n = 154) sets, while Institution 2 served as the external validation set (n = 58). 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 stage were 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 AUCs for training set, internal validation set, and external validation set were 0.963, 0.882, and 0.743, respectively. 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.