Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients

Authors：Wang, Ping; Chen, Kaige; Han, Ying; Zhao, Min; Abiyasi, Nanding; Peng, Haiyong; Yan, Shaolei; Shang, Jiming; Shang, Naijian; Meng, Wei^*

Source：Future Oncology, 2023, 19(23): 1613-1626.

DOI：10.2217/fon-2022-1025

• contrast-enhanced computed tomography
• gastric cancer
• Lauren classification
• lymphovascular invasion
• radiomics

Background: We explored whether a model based on contrast-enhanced computed tomography radiomics features and clinicopathological factors can evaluate preoperative lymphovascular invasion (LVI) in patients with gastric cancer (GC) with Lauren classification. Methods: Based on clinical and radiomic characteristics, we established three models: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore and a combined model. The relationship between Lauren classification and LVI was analyzed using a histogram. Results: We retrospectively analyzed 495 patients with GC. The areas under the curve of the combined model were 0.8629 and 0.8343 in the training and testing datasets, respectively. The combined model showed a superior performance to the other models. Conclusion: CECT-based radiomics models can effectively predict preoperative LVI in GC patients with Lauren classification. @@@ Tweetable abstractContrast-enhanced computed tomography-based radiomics models can effectively predict the preoperative lymphovascular invasion status in patients with gastric cancer with Lauren classification.

• Institution
  哈尔滨医科大学; 1