Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study

Abstract

Clinically significant portal hypertension (CSPH) is associated with an incremental risk of esophageal varices and overt clinical decompensations. However, hepatic venous pressure gradient (HVPG) measurement, the gold standard for defining CSPH (HVPG≥10 mm Hg) is invasive and therefore not suitable for routine clinical practice. This study aims to develop and validate a radiomics-based model as a noninvasive method for accurate detection of CSPH in cirrhosis. The prospective multicenter diagnostic trial (CHESS1701, ClinicalTrials.gov identifier: NCT03138915) involved 385 patients with cirrhosis from five liver centers in China between August 2016 and September 2017. Patients who had both HVPG measurement and contrast-enhanced CT within 14 days prior to the catheterization were collected. The noninvasive radiomics model, termed rHVPG for CSPH was developed based on CT images in a training cohort consisted of 222 consecutive patients and the diagnostic performance was prospectively assessed in 163 consecutive patients in four external validation cohorts. rHVPG showed a good performance in detection of CSPH with a C-index of 0·849 (95%CI: 0·786-0·911). Application of rHVPG in four external prospective validation cohorts still gave excellent performance with the C-index of 0·889 (95%CI: 0·752-1·000, 0·800 (95%CI: 0·614-0·986), 0·917 (95%CI: 0·772-1·000), and 0·827 (95%CI: 0·618-1·000), respectively. Intraclass correlation coefficients for inter- and intra-observer agreement were 0·92-0·99 and 0·97-0·99, respectively. A radiomics signature was developed and prospectively validated as an accurate method for noninvasive detection of CSPH in cirrhosis. The tool of rHVPG assessment can facilitate the identification of CSPH rapidly when invasive transjugular procedure is not available.

Keywords: Hepatic venous pressure gradient; Liver cirrhosis; Noninvasive; Portal hypertension; Radiomics.

Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1

Workflow for the radiomics process. (a) Segmentation of region of interest on CT images. (b) Extraction of both texture and non-texture features. (c) Radiomics feature selection using the least absolute shrinkage and selection operator regression model. CT, computed tomography; GLCM, Gray-level co-occurence matrix; GLRLM, Gray-level run-length matrix; GLSZM, Gray-level size zone matrix; NGTDM, Neighborhood gray-level difference matrix.

Fig. 2

Flow diagram for study enrollment. ROI, region of interest. Training cohort: The 302 Hospital of PLA. Validation cohorts: Cohort 1: Beijing Shijitan Hospital; Cohort 2: The Third Xiangya Hospital; Cohort 3: Beijing Youan Hospital; Cohort 4: Xingtai People's Hospital.

Fig. 3

Radiomics feature selection using the least absolute shrinkage and selection operator (LASSO) regression model. (a) Tuning parameter (λ) selection in LASSO model used ten-fold cross-validation via minimum criteria. Dotted vertical lines were drawn both at the optimal (left) and minimum values (right) by using minimum criteria and 1 standard error of minimum criteria. A λ value of 0.0525, with log (λ), −2·947 was chosen using ten-fold cross-validation. (b) LASSO coefficient profiles of 20,648 features. A coefficient profile plot was produced versus the log (λ) sequence. Vertical line was drawn at the value selected where optimal λ resulted in 11 nonzero coefficients.

Fig. 4

Receiver operating characteristic curves of the rHVPG and other noninvasive models for detection of clinically significant portal hypertension in cirrhosis. rHVPG, radiomics-based hepatic venous pressure gradient.

Fig. 5

Receiver operating characteristic curves of the rHVPG for detection of clinically significant portal hypertension in cirrhosis in the training and validation cohorts. rHVPG, radiomics-based hepatic venous pressure gradient.