Diagnostic performance of a nomogram incorporating cribriform morphology for the prediction of adverse pathology in prostate cancer at radical prostatectomy

Development and validation of prognostic nomograms in patients with hepatocellular carcinoma: a population-based study

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. The existing staging system has a limited budget capacity for HCC recurrence. The authors aimed to establish and verify two nomogram models to predict disease-free survival (DFS) and overall survival (OS) in patients with HCC. Methods: Patients diagnosed with HCC between August 2011 and March 2016 were recruited. Data were randomly divided into a training cohort and a validation cohort. Based on univariate and multivariate Cox regression analysis, independent risk factors for DFS and OS were identified, and two nomogram models were established to predict patient survival. Results: Sex, tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, tumor capsule, macrovascular invasion, AST-to-platelet ratio index, AST-to-lymphocyte ratio index, neutrophil-lymphocyte ratio and alpha-fetoprotein (AFP) were used to build the nomogram for DFS, while age, tumor size, BCLC stage, tumor capsule, macrovascular invasion, systemic immune-inflammation index, AST, total bilirubin and AFP were used to build the nomogram for OS. Calibration curves showed good agreement between the nomogram prediction and actual observation. C-indices in both nomograms were significantly higher than BCLC. Conclusion: The two nomograms improved the accuracy of individualized prediction of DFS and OS, which may help doctors screen patients with a high risk of recurrence to formulate individualized treatment plans.

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Background

There is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.

Aim

The aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.

Methods

A total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.

Results

Eight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.

Conclusions

This study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.