Development and validation of the OSASH score to predict overall survival of hepatocellular carcinoma after surgical resection: a dual-institutional study

Abstract

Objective To develop and validate a risk score based on preoperative clinical-radiological parameters for predicting overall survival (OS) in patients undergoing surgical resection for hepatocellular carcinoma (HCC). Methods From July 2010 to December 2021, consecutive patients with surgically-proven HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled. A preoperative OS risk score was constructed in the training cohort using a Cox regression model and validated in a propensity score-matched internal validation cohort and an external validation cohort. Results A total of 520 patients were enrolled, among whom 210, 210, and 100 patients were from the training, internal validation, and external validation cohorts, respectively. Independent predictors for OS included incomplete tumor “capsule,” mosaic architecture, tumor multiplicity, and serum alpha-fetoprotein, which were incorporated into the “OSASH score.” The C-index the OSASH score was 0.85, 0.81, and 0.62 in the training, internal, and external validation cohorts, respectively. Using 32 as the cutoff point, the OSASH score stratified patients into prognostically distinct low- and high-risk groups among all study cohorts and six subgroups (all p < 0.05). Furthermore, patients with BCLC stage B-C HCC and OSASH-low risk achieved comparable OS to that of patients with BCLC stage 0-A HCC and OSASH-high risk in the internal validation cohort (5-year OS rates, 74.7 vs. 77.8%; p = 0.964). Conclusion The OSASH score may help predict OS in HCC patients undergoing hepatectomy and identify potential surgical candidates among those with BCLC stage B-C HCC. Clinical relevance statement By incorporating three preoperative MRI features and serum AFP, the OSASH score may help predict postsurgical overall survival in patients with hepatocellular carcinoma and identify potential surgical candidates among those with BCLC stage B and C HCC. Key Points • The OSASH score incorporating three MRI features and serum AFP can be used to predict OS in HCC patients who received curative-intent hepatectomy. • The score stratified patients into prognostically distinct low- and high-risk strata in all study cohorts and six subgroups. • Among patients with BCLC stage B and C HCC, the score identified a subgroup of low-risk patients who achieved favorable outcomes after surgery.

Prediction of Microvascular Invasion and Recurrence After Curative Resection of LI-RADS Category 5 Hepatocellular Carcinoma on Gd-BOPTA Enhanced MRI

“…In contrast to previous studies in HCC populations, 23,37 we did not identify high AFP levels as an independent predictor of MVI and recurrence in patients with LR-5 HCC, which is similar to recent study for LR-5 HCC patients. 39 The potential reason for the discrepancies among these studies may include selection bias in the study population or differences in sample size.…”

“… 35 This may reflect histological differences and tumor heterogeneity, explaining why the presence of mosaic structures serves as a risk factor for poor prognosis in HCC treatment. Previous studies have established mosaic architecture as a risk factor for TACE 23 , 36 and postoperative 17 , 37 , 38 recurrence in HCC patients. In agreement with these findings, our study also observed that HCC patients with mosaic structures had worse postoperative prognosis.…”

A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study

“…The use of IA as a predictive biomarker has several advantages: CT or MRI is routinely performed preoperatively; IA can be easily recognized by distinguishing discrete arteries within the tumor and the interobserver agreement is good or excellent 32 , 33 ; IA is a prevalent feature presented in 28.9–52.3% of HCC 32 , 34 , 35 . Thus, a large proportion of HCC patients may benefit from using IA as a selection criterion.…”

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma

“…On a per-lesion basis, the following features were evaluated: (a) tumor size (cm), (b) enhancement pattern (typical vs atypical, with typical enhancement pattern referring to the presence of non-rim APHE coupled with nonperipheral “washout” [ 19 ]), (c) rim APHE, (d) corona enhancement, (e) nonsmooth tumor margin, (f) incomplete tumor “capsule,” (g) delayed central enhancement, (h) enhancing “capsule,” (i) intratumoral necrosis, (j) fat in mass, more than adjacent liver, (k) radiological cirrhosis, (l) diffuse fatty change, (m) diffuse iron overload, (n) splenomegaly, (o) ascites, (p) collateral circulation, (q) gastroesophageal varices, and (r) main portal vein diameter (cm). Definitions of the imaging features haven been described in our prior study [ 20 ].…”