Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

Abstract

To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

Prognostic Prediction Using the Clinical Data and Ultrasomics-Based Model in Acute Respiratory Distress Syndrome (ARDS) Combined with Acute Kidney Injury (AKI)

“…Higher prediction accuracy presented with a larger AUC and a P value <0.05 (two-tailed) indicated statistical significance. Decision curve analysis (DCA) of training and test sets were conducted to determine the clinical usefulness by quantifying the net benefits at different threshold probabilities in the models [ 9 ].…”

“…The authors are very grateful to Mr. ZhuYong and Mao Ying for their guidance and the statistical methods provided in his article [ 9 ]. This study was supported by the Taizhou Science and Technology Support Plan (Social Development) project (TS201908).…”

A Nomogram Model to Predict Early Recurrence of Patients With Intrahepatic Cholangiocarcinoma for Adjuvant Chemotherapy Guidance: A Multi-Institutional Analysis

“…The calibration plots showed that the prediction results were more consistent with the actual results, and DCAs showed that the predictive ability of the nomogram model was better than TNM staging in the training and testing sets. Different from our nomogram model, many scholars (21,30,31) developed radiomics nomograms by using the radiomics signature and other clinicopathological characteristics to predict the early recurrence of ICC after surgery, but the inclusion of radiomics signature also brought certain difficulties to clinical applications. Jeong et al (32) established a nomogram model to allow precise estimation of the risk of 1-, 3-, and 5-year RFS for ICC after resection by the combined Cox and logistic ranking system based on 10 and 11 covariates; however, it could not evaluate and predict early recurrence and was complex in the application despite its good predictive ability.…”

USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models

“…Survival curves were drawn by using the Kaplan–Meier method, and differences of survival rates were compared with the log rank, Breslow, and Tarone-Ware test. Other statistical analyses were done using R software (R Foundation for Statistical Computing, version 3.5.8; https://www.r-project.org/) [ 15 ].…”

“…This work was supported by grants from the Xiangyang Science and Technology Bureau: Development of Movable Head Mounted Oxygen Supply and Isolation System for Patients with COVID-19 Pneumonia (2020ZD01, PENG AN); 2021 Science and Technology Innovation Project of Xiangyang No.1 People's Hospital: Research on Risk Stratification Decision and Key Problems of Prostate Cancer Based on In-Depth Learning of MRI/Ultrasound Radiomics (XYY2021Q16, PENG AN) (preprint: 10.21203/rs.3.rs-969060/v1). The authors are very grateful to Mr. Zhu Yong for his guidance and the statistical methods provided in his article [ 15 ]. The authors gratefully acknowledge Weiping Gu and Anna Gong for assistance with translating references.…”