Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer

Abstract

Purpose: To develop and validate a nomogram based on extracellular volume (ECV) fraction derived from dual-energy CT (DECT) for preoperatively predicting microsatellite instability (MSI) status in gastric cancer (GC).

Materials and methods: A total of 123 patients with GCs who underwent contrast-enhanced abdominal DECT scans were retrospectively enrolled. Patients were divided into MSI (n=41) and microsatellite stability (MSS, n=82) groups according to postoperative immunohistochemistry staining, then randomly assigned to the training (n=86) and validation cohorts (n=37). We extracted clinicopathological characteristics, CT imaging features, iodine concentrations (ICs), and normalized IC values against the aorta (nICs) in three enhanced phases. The ECV fraction derived from the iodine density map at the equilibrium phase was calculated. Univariate and multivariable logistic regression analyses were used to identify independent risk predictors for MSI status. Then, a nomogram was established, and its performance was evaluated by ROC analysis and Delong test. Its calibration performance and clinical utility were assessed by calibration curve and decision curve analysis, respectively.

Results: The ECV fraction, tumor location, and Borrmann type were independent predictors of MSI status (all P < 0.05) and were used to establish the nomogram. The nomogram yielded higher AUCs of 0.826 (0.729-0.899) and 0.833 (0.675-0.935) in training and validation cohorts than single variables (P<0.05), with good calibration and clinical utility.

Conclusions: The nomogram based on DECT-derived ECV fraction has the potential as a noninvasive biomarker to predict MSI status in GC patients.

Keywords: dual-energy CT; extracellular volume fraction; gastric cancer; microsatellite instability; nomogram.

Figure 1

Flow chart of case enrollment. √ meets this inclusion criterion.

Figure 2

A 65-year-old male with histopathologically proved MSI gastric cancer. (A) 70keV monochromatic image in venous phase. A Bormann type I advanced gastric cancer was located in gastric antrum. (B–D) Iodine concentration pseudocolor images in arterial (B), venous (C), and equilibrium (D) phases. IC values were 11.53 100µɡ/cm³, 19.02 100µɡ/cm³, and 18.91 100µɡ/cm³, respectively. The corresponding nIC values were 0.07, 0.28, and 0.51, respectively. The ECV fraction after calculation was 31.06%. (E–H) Immunohistochemical results showed MLH1-negative (E), MSH2-positive (F), MSH6-positive (G), PMS2-negative (H) (×200 magnification). MSI, microsatellite instability; IC, iodine concentration; nIC, normalized iodine concentration; ECV, extracellular volume.

Figure 3

A 62-year-old male with histopathologically proved MSS gastric cancer. (A) 70keV monochromatic image in venous phase. A Bormann type IV advanced gastric cancer was located in gastric antrum. (B–D) Iodine concentration pseudocolor images in arterial (B), venous (C), and equilibrium (D) phases. IC values were 16.72 100µɡ/cm³, 25.62 100µɡ/cm³, and 26.56 100µɡ/cm³, respectively. The corresponding nIC values were 0.09, 0.49, and 0.71, respectively. The ECV fraction after calculation was 42.18%. (E–H) Immunohistochemical results showed MLH1-positive (E), MSH2-positive (F) MSH6-positive (G), PMS2-positive (H) (×200 magnification). MSS, microsatellite stability; IC, iodine concentration; nIC, normalized iodine concentration; ECV, extracellular volume.

Figure 4

Development of nomogram for predicting MSI status. The nomogram was constructed in the training cohort via Borrmann type, DECT-derived ECV, and tumor location. In Borrmann type, the increasing numbers indicate I, II, III, and IV, respectively. In tumor location, 1 is cardia/fundus, 2 is gastric body, 3 is antrum/pylorus. MSI, microsatellite instability; DECT, dual-energy CT; ECV, extracellular volume.

Figure 5

ROC curve analyses of each individual variable and nomogram to predict MSI status in the training cohort (A) and validation cohort (B). The nomogram showed the highest AUC value of 0.826 (95% CI, 0.729–0.899) in the training cohort and 0.833 (95% CI, 0.675–0.933) in the validation cohort. MSI, microsatellite instability; ROC, receiver operating characteristic; AUC, areas under the curve.

Figure 6

Calibration curves of the nomogram in the training cohort (A) and validation cohort (B).The dotted lines (representing the nomogram) were close to the solid lines (representing an ideal model) in both cohorts, indicating the nomogram was well fitted.

Figure 7

Decision curve analysis (DCA) for the nomogram. The DCA indicated that the nomogram achieved more net benefits within the most of thresholds probabilities than either the treat-all scheme (assuming all lesions are MSI) or the treat-none scheme (assuming all lesions are MSS). MSI, microsatellite instability; MSS, microsatellite stability.