The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

Abstract

Background: The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades.

Methods: A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP-IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP-IMD and clinical features. The performance of these models was evaluated and compared.

Results: Model-CP-IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP-IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP-IMD and model-Combine outperformed model-CP according to decision curve analysis.

Conclusion: DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

Keywords: Dual-energy spectral CT; Gastric cancer; Histologic grade; Iodine-based material decomposition images; Radiomics.

Fig. 1

Flowchart of patient enrollment

Fig. 2

Delineation of the volume of interest (VOI). A gastric cancer located in the cardia was shown in a axial portal phase images; b iodine-based MD images; c coronal multiplanar reconstruction images. d Three-dimensional VOI of the tumor was displayed

Fig. 3

Flowchart of our study

Fig. 4

Features contained in models and their weights. a Model-CP; b model-IMD; c model-CP–IMD; d model-Combine

Fig. 5

ROC curves of the models in a training set and b testing set

Fig. 6

DeLong’s test results in a training set and b testing set

Fig. 7

Calibration curves of model-Combine in a training cohort and b testing cohort

Fig. 8

Decision curve analysis for all models in the whole dataset. A larger area under the decision curve indicates a better clinical utility. Model-Combine added more net benefit than model-CP at the range of 0.1–0.9 and model-CP–IMD added more net benefit than model-CP at the range of 0.3–1.0. In comparison to model-Clinical, model-Combine owned a larger net benefit at a range threshold probability of 0.05–0.95