Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion

Abstract

Purpose: Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in 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.

Methods: Sixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from 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 reduce the 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 vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric.

Results: Among 271 texture features, 150 features had excellent reproducibility, which were included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy ranges for predicting LVI were 0.777-0.894 and 76%-81.5%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0.482-0.754 and 54%-68.2%, respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively.

Conclusion: ML-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.

Figure 1

Technical study pipeline. SMOTE, synthetic minority oversampling technique.

Figure 2. a, b

Segmentation technique. Using a single axial slice of the portal venous phase CT (a), tumor segmentation (b) is done manually along the whole and the largest outer margin of gastric adenocarcinoma. The contour of the segmentation was shrunk to avoid the possible inclusion of perigastric and intragastric areas.

Figure 3. a, b

The heat map shows the distribution of normalized texture feature values selected for lymphovascular (a) and perineural (b) invasion status.

Figure 4. a, b

Distribution of the selected texture features in two-dimensional space considering the classes, that is, the presence and absence of the lymphovascular (a) and perineural (b) invasion. Please refer to Table 3 for actual feature names.

Figure 5. a, b

Receiver operating characteristic (ROC) curves of the models for predicting lymphovascular (a) and perineural (b) invasion. SVM, support vector machine; Adaboost, adaptive boosting; SGD, stochastic gradient descent; k-NN, k-nearest neighbors.

Figure 6. a, b

Calibration plots show predicted and actual probability (observed average) of the machine learning models for predicting lymphovascular (a) and perineural (b) invasion. SVM, support vector machine; Adaboost, adaptive boosting; SGD, stochastic gradient descent; k-NN, k-nearest neighbors.