Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma

Abstract

Objectives: To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA).

Methods: This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination.

Results: A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P<0.001 for both training and validation sets). A nomogram including the radiomics signature and tumor location as predictors was developed. The model showed both good calibration and good discrimination, in which C-index in the training set, 0.752 [95% confidence interval (95% CI): 0.701-0.803]; C-index in the validation set, 0.793 (95% CI: 0.711-0.874).

Conclusions: This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures, which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.

Keywords: Adenocarcinoma; X-ray computed tomography; histologic grade; nomograms; stomach neoplasm.

1

Flowchart of patient enrollment.

2

An example of manual segmentation in gastric adenocarcinoma. (A) A diffusely infiltrating mass with enhancement is shown on the portal venous phase CT image; (B) The manually segmented area is shown on the same axial slice.

3

Feature selection with LASSO binary logistic regression model. (A) Tuning parameter (λ) selection in the LASSO model using 10-fold cross-validation. AUC was plotted vs. log (λ). Using the minimum criteria and the 1 standard error of the minimum criteria (the 1-SE criteria), dotted vertical lines were drawn at the best value. The 1-SE criteria were chosen according to 10-fold cross-validation, whereas 12 radiomics features were chosen; (B) LASSO coefficient profiles of features. A coefficient profile plot was plotted vs. log (λ). As λ becomes larger, the coefficients of more features shrunk to 0. Each colored line represents the coefficient of each feature. The vertical grey line was drawn at the selected λ, where 12 features had nonzero coefficients. LASSO, least absolute shrinkage and selection operator; AUC, area under the receiver operating characteristic curve.

4

Receiver operating characteristic (ROC) curves of radiomics nomogram, radiomics signature and tumor location alone in training set (A) and validation set (B) respectively. Calibration curves of radiomics nomogram in the training set (C) and the validation set (D). AUC, area under the receiver operating characteristic curve; 95% CI, 95% confidence interval.

5

Developed radiomics nomogram. The radiomics nomogram was developed in the training set with radiomics signature and tumor location incorporated. Location, tumor location of the stomach (1, upper-third; 2, middle-third; 3, lower-third; 4, gastric stump).