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A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma.

ABSTRACT: PURPOSE:We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODS:A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram. RESULTS:The radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction nomogram. The model exhibited good calibration and discrimination in the training and validation cohorts (C-index [95% confidence interval]: 0.846 [0.787-0.905] and 0.844 [0.774-0.915], respectively). Its clinical usefulness was confirmed using a decision curve analysis. CONCLUSION:The radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

SUBMITTER: Peng J

PROVIDER: S-EPMC5951199 | biostudies-literature | 2018 May-Jun

REPOSITORIES: biostudies-literature

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A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma.

Peng Jie J Zhang Jing J Zhang Qifan Q Xu Yikai Y Zhou Jie J Liu Li L

Diagnostic and interventional radiology (Ankara, Turkey) 20180501 3

<h4>Purpose</h4>We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).<h4>Methods</h4>A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and sele ...[more]

PMID: 29770763

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Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Project description:PurposeThis study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.MethodsA retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness.ResultsMultivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC.ConclusionThe CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.

Project description:Background and aimsAs a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. Accurate preoperative prediction of MVI and its M2 classification can help clinicians to make the best treatment decision. Therefore, we aimed to establish effective nomograms to predict MVI and its M2 grade.MethodsA total of 111 patients who underwent radical resection of hepatocellular carcinoma (HCC) from January 2015 to September 2020 were retrospectively collected. We utilized logistic regression and least absolute shrinkage and selection operator (LASSO) regression to identify the independent predictive factors of MVI and its M2 classification. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. Nomograms for predicting MVI and its M2 grade were then developed by incorporating these factors. Area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were respectively used to evaluate the efficacy, accuracy, and clinical utility of the nomograms.ResultsCombined with the results of LASSO regression, logistic regression, and IDI and NRI analyses, we founded that clinical tumor-node-metastasis (TNM) stage, tumor size, Edmondson-Steiner classification, α-fetoprotein (AFP), tumor capsule, tumor margin, and tumor number were independent risk factors for MVI. Among the MVI-positive patients, only clinical TNM stage, tumor capsule, tumor margin, and tumor number were highly correlated with M2 grade. The nomograms established by incorporating the above variables had a good performance in predicting MVI (AUCMVI = 0.926) and its M2 classification (AUCM2 = 0.803). The calibration curve confirmed that predictions and actual observations were in good agreement. Significant clinical utility of our nomograms was demonstrated by DCA.ConclusionsThe nomograms of this study make it possible to do individualized predictions of MVI and its M2 classification, which may help us select an appropriate treatment plan.

Project description:ImportanceA reduced incidence of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) may be associated with a decreased risk of early tumor recurrence and better survival after partial hepatectomy.ObjectiveTo examine the association of preoperative antiviral treatment (AVT) with the incidences of MVI and posthepatectomy early tumor recurrence in HBV-related HCC.Design, setting, and participantsData on a cohort of 2362 patients who underwent R0 resection for HBV-related HCC between January 2008 and April 2010 at the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China, were reviewed. The median (interquartile range) postoperative follow-up was 44.8 (22.8-59.3) months. Data were analyzed from June 2016 to October 2017.InterventionsPreoperative AVT and partial hepatectomy.Main outcomes and measuresOverall survival and time to recurrence after surgery were calculated and compared using the Kaplan-Meier method, log-rank test, and Cox regression analysis. Independent risk factors of MVI presence were assessed by logistic regression analysis.ResultsAmong 2362 included patients, 1999 (84.6%) were men, and the median (interquartile range) age was 50.6 (43.1-57.3) years. A total of 2036 patients (86.2%) did not receive any preoperative AVT, while 326 (13.8%) received ongoing AVT more than 90 days before surgery. In the non-AVT group, compared with a preoperative HBV DNA level of less than 2000 IU/mL, a preoperative HBV DNA level of 2000 IU/mL or greater was associated with an increased risk of MVI (odds ratio [OR], 1.399; 95% CI, 1.151-1.701). Compared with the non-AVT group, patients receiving AVT had a lower incidence of MVI (38.7% [126 of 326] vs 48.6% [989 of 2036]; P = .001) and reduced risk of MVI (OR, 0.758; 95% CI, 0.575-0.998). A complete response to AVT was an independent protective factor of MVI (OR, 0.690; 95% CI, 0.500-0.952). Accordingly, preoperative AVT was associated with decreased 6-month, 1-year, and 2-year recurrences vs non-AVT (14.2%, 24.6%, and 38.5%, respectively, vs 23.4%, 37.1%, and 52.3%; P < .001); AVT was protective of early tumor recurrence (hazard ratio, 0.732; 95% CI, 0.605-0.886). In addition, patients in the non-AVT group were more likely to have multiple intrahepatic recurrences (49.1% [549 of 1119] vs 36.2% [54 of 149]; P = .003) and recurrences involving multiple hepatic segments compared with patients receiving AVT.Conclusions and relevanceA high preoperative HBV DNA level was an independent risk factor of MVI. Antiviral treatment administered more than 90 days before surgery was associated with reduced incidences of MVI and early tumor recurrence after partial hepatectomy for HBV-related HCC.

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A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma.

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A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma.

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