A Nomogram to Predict the Risk of Stent Dysfunction After TIPS in Patients with Hepatitis B Cirrhosis.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram for the prediction of stent dysfunction after transjugular intrahepatic portosystemic shunt (TIPS) placement in patients with hepatitis B cirrhosis. MATERIALS AND METHODS: From 2012 to 2020, 355 patients with hepatitis B cirrhosis who underwent TIPS placements were enrolled in this study. A multivariable logistic regression analysis was applied to determine independent risk factors for the nomogram construction. Discrimination, calibration, and clinical usefulness of the prediction model were assessed by using receiver operating characteristic curves, calibration scatter plots, and a decision curve analysis (DCA). RESULTS: Independent factors for TIPS stent dysfunction included diabetes, previous splenectomy, the shunting branch of the portal vein, and stent position, which were used to construct the nomogram. The AUC values in the training and validation cohorts were 0.817 (95% CI: 0.731-0.903) and 0.804 (95% CI: 0.673-0.935), respectively, which suggested a good predictive ability. The calibration curves in both cohorts revealed good agreement between the predictions and actual observations. The DCA curve indicated that when the threshold probability ranged from 2% to 88%, the nomogram could provide clinical usefulness and a net benefit. CONCLUSION: The nomogram that we developed could be conveniently used to predict TIPS stent dysfunction in patients with hepatitis B cirrhosis.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study.

OBJECTIVE: To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC). METHODS: A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed. RESULT: Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model. CONCLUSION: The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

Aortic dual-energy CT angiography with low contrast medium injection rate.

The contrast medium (CM) induced nephropathy required new CT imaging protocol. This study evaluated the feasibility of low contrast medium (CM) volume and injection flow using aortic dual-energy CT (DECT) angiography with non-linear blending technique. Sixty patients were randomly assigned to two groups: control group (n=30), single-energy CT 70 ml CM at injection rate of 5 ml/s; study group (n=30), DECT mode, 0.5 ml per kg of patient weight CM at injection rate=(weight × 0.5 ml/kg)/(4+scan time). Non-linear blending technique was used for dual-energy images. Mean attention, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of aorta were compared. The level of visible renal artery branches was scored. There was no significant difference between the two groups in the mean aortic attention, SNR and CNR (all P > 0.05). Significant difference was showed in CM injection rate (p < 0.001) and volume (P < 0.001). The renal artery score had no statistically significant difference (P=0.771). Compared conventional scan and CM injection protocol, DECT with non-linear blending technique maintained the image quality of aortic CT angiography with reduced CM volume and flow rate, which could reduce the risks associated with CM injection.