A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer

Abstract

Objective: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer.

Methods: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts.

Results: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful.

Conclusions: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.

Keywords: Gastric cancer; X ray; computed tomography; human epidermal growth factor receptor 2; radiomics.

1. An example of manual segmentation in gastric cancer. (A) Localized thick wall of gastric cancer with enhancement is observed on the portal venous phase computed tomography (CT) image; (B) Manual segmentation on the same axial slice is depicted with red label.

2. Feature selection with the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (A) Tuning parameter (λ) selection of LASSO model. The area under curve (AUC) was drawn versus log(λ). Vertical green lines were plotted at the best value with using 5-fold cross-validation to tune parameter (λ) selection in the LASSO model; (B) LASSO coefficient profiles of the features. Each colored line represents corresponding coefficient of each feature. Vertical green line was drawn at the selected λ, where nonzero coefficients were obtained with 7 features.

3. Receiver operating characteristic (ROC) curves of radiomics signature in training cohort (AUC: 0.782, 95% CI: 0.686−0.879) (A) and validation cohort (AUC: 0.736, 95% CI: 0.554−0.918) (B). AUC, area under the curve; 95% CI, 95% confidence interval.

4. Developed radiomics nomogram. The radiomics nomogram was built in the training cohort, with the radiomics signature and carcinoembryonic antigen (CEA) level incorporated. The CEA level was considered as 0 when the CEA value ≤5 ng/mL and considered as 1 when the CEA value >5 ng/mL.

5. Calibration curves of radiomics nomogram in training and validation cohorts. Calibration curve plots demonstrate the calibration between predicted risks of human epidermal growth factor receptor 2 (HER2)-positive status and observed outcomes of HER2-positive status in the training cohort (A) and the validation cohort (B).

6. Decision curve analysis (DCA) for radiomics nomogram in training cohort. The vertical axis displays standardized net benefit. The two horizontal axes show the correspondence between risk threshold and cost:benefit ratio.