AI Summary & Key Takeaways
AI InsightOverall, the method was more successful in predicting LVI than PNI.
📌 3 Key Takeaways
Özet
PURPOSELymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosisin gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach.METHODSSixty-eight patients who underwent total gastrectomy with curative (R0) resection andD2-lymphadenectomy were included in this retrospective study. Texture features were extractedfrom the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists. Then, a feature selection algorithm was used to further reducethe high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vectormachine, and decision tree. Predictive performance of the ML algorithms was mainly evaluatedusing the mean area under the curve (AUC) metric.RESULTSAmong 271 texture features, 150 features had excellent reproducibility, which were included inthe further feature selection process. Dimension reduction steps yielded five texture featuresfor LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy rangesfor predicting LVI were 0.777–0.894 and 76%–81.5%, respectively. For predicting PNI, mean AUCand accuracy ranges were 0.482–0.754 and 54%–68.2%, respectively. The best performances forpredicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively.CONCLUSIONML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Overall, the method was more successful in predicting LVI than PNI.