scholarly journals A Non-Invasive Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Huanhuan Wang ◽  
Ye Lu ◽  
Runkun Liu ◽  
Liang Wang ◽  
Qingguang Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Lymphocyte Count ◽  
Microvascular Invasion ◽  
Enzyme Linked Immunosorbent Assay ◽  
Serum Concentrations ◽  
Training Cohort ◽  
Non Invasive ◽  
Peritumoral Enhancement ◽  
Tumor Shape

BackgroundMicrovascular invasion (MVI) is a significant predictive factor for early recurrence, metastasis, and poor prognosis of hepatocellular carcinoma. The aim of the present study is to identify preoperative factors for predicting MVI, in addition to develop and validate non-invasive nomogram for predicting MVI.MethodsA total of 381 patients with resected HCC were enrolled and divided into a training cohort (n = 267) and a validation cohort (n = 114). Serum VEGF-A level was examined by enzyme-linked immunosorbent assay (ELISA). Risk factors for MVI were assessed based on univariate and multivariate analyses in the training cohort. A nomogram incorporating independent risk predictors was established and validated.ResultThe serum VEGF-A levels in the MVI positive group (n = 198) and MVI negative group (n = 183) were 215.25 ± 105.68 pg/ml and 86.52 ± 62.45 pg/ml, respectively (P <0.05). Serum VEGF-A concentration ≥138.30 pg/ml was an independent risk factor of MVI (OR: 33.088; 95%CI: 12.871–85.057; P <0.001). Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery were identified as significant predictors for MVI. The nomogram indicated excellent predictive performance with an AUROC of 0.948 (95% CI: 0.923–0.973) and 0.881 (95% CI: 0.820–0.942) in the training and validation cohorts, respectively. The nomogram showed a good model fit and calibration.ConclusionsHigher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery are promising markers for MVI prediction in HCC. A reliable non-invasive nomogram which incorporated blood biomarkers and imaging risk factors was established and validated. The nomogram achieved desirable effectiveness in preoperatively predicting MVI in HCC patients.

scholarly journals A Nomogram Predicting Microvascular Invasion Risk in BCLC 0/A Hepatocellular Carcinoma after Curative Resection

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Shuai-Xiang Gao ◽  
Rui Liao ◽  
Hua-Qiang Wang ◽  
Dan Liu ◽  
Fang Luo
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Training Group ◽  
Microvascular Invasion ◽  
Arterial Phase ◽  
Ct Value ◽  
Delayed Phase ◽  
Curative Hepatectomy ◽  
Peritumoral Enhancement

Background. Numerous studies have shown that hepatocellular carcinoma (HCC) without microvascular invasion (MVI) may have better outcomes. This study established a preoperative MVI risk nomogram mainly incorporating three related risk factors of MVI in BCLC 0/A HCC after surgery. Methods. Independent predictors for the risk of MVI were investigated, and an MVI risk nomogram was established based on 60 patients in the training group who underwent curative hepatectomy for BCLC 0/A HCC and validated using a dataset in the validation group. Results. Univariate analysis in the training group showed that hepatitis viral B (HBV) DNA (P=0.034), tumor size (P<0.001), CT value in the venous phase (P=0.039), CT value in the delayed phase (P=0.017), peritumoral enhancement (P=0.013), visible small blood vessels in the arterial phase (P=0.002), and distance from the tumor to the inferior vena cava (IVC) (DTI, P=0.004) were risk factors significantly associated with the presence of MVI. According to multivariate analysis, the independent predictive factors of MVI, including tumor size (P=0.002), CT value in the delayed phase (P=0.018), and peritumoral enhancement (P=0.057), were incorporated in the corresponding nomogram. The nomogram displayed an unadjusted C-index of 0.851 and a bootstrap-corrected C-index of 0.832. Calibration curves also showed good agreement on the presence of MVI. ROC curve analyses showed that the nomogram had a large AUC (0.851). Conclusions. The proposed nomogram consisting of tumor size, CT value in the delayed phase, and peritumoral enhancement was associated with MVI risk in BCLC 0/A HCC following curative hepatectomy.

scholarly journals MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Liver Cancer ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 94-106
Author(s):  
Seung Baek Hong ◽  
Sang Hyun Choi ◽  
So Yeon Kim ◽  
Ju Hyun Shim ◽  
Seung Soo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Meta Analysis ◽  
Diagnostic Odds Ratio ◽  
Microvascular Invasion ◽  
Hepatobiliary Phase ◽  
Phase Imaging ◽  
Arterial Enhancement ◽  
Mri Features ◽  
Peritumoral Enhancement

<b><i>Purpose:</i></b> Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value. <b><i>Methods:</i></b> Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features. <b><i>Results:</i></b> Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (&#x3e;5 cm) (DOR = 5.2, 95% CI [3.0–9.0]), rim arterial enhancement (4.2, 95% CI [1.7–10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8–6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4–15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2–4.4]), multifocality (7.1, 95% CI [2.6–19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5–9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4–94.8%] and 93.3% [74.5–98.5%], respectively). <b><i>Conclusions:</i></b> Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.

Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

scholarly journals Predictive value of gamma-glutamyl transpeptidase to lymphocyte count ratio in hepatocellular carcinoma patients with microvascular invasion

2020 ◽  
Author(s):  
Hongxing Zhang ◽  
Yu Zhou ◽  
Yicheng Li ◽  
Wanying Qin ◽  
Yunhua Zi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Factor ◽  
Roc Curve ◽  
Lymphocyte Count ◽  
Poor Prognosis ◽  
Microvascular Invasion ◽  
Gamma Glutamyl Transpeptidase ◽  
Gamma Glutamyl ◽  
Count Ratio ◽  
Glutamyl Transpeptidase

Abstract Background: Microvascular invasion (MVI) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). However, there is still a lack of preoperative markers to predict MVI in HCC. This study intends to explore the potential application value of the gamma-glutamyl transpeptidase (GGT) to lymphocyte count ratio (GLR) in predicting MVI in HCC and provide guidance for clinical diagnosis and treatment. Methods: From March 2010 to December 2015, 230 HCC patients who underwent surgical treatment in the Affiliated Hospital of Guilin Medical University were selected. Clinicopathological parameters between the MVI group (n = 115) and the non-MVI group (n = 115) were comparatively analyzed. The GLR was used as the potential risk factor for HCC with MVI, and its optimal cut-off value was estimated by using the receiver operating characteristic (ROC) curve. The Kaplan-Meier method was used to analyze the survival of HCC patients, and univariate and multivariate Cox regression analyses were used to establish independent predictors affecting postoperative HCC patients. Results: The GLR levels in the MVI group and non-MVI group were 84.83 ± 61.84 and 38.42 ± 33.52 (p < 0.001), respectively. According to ROC curve analysis, the optimal cut-off value of GLR was 56.0, and the area under the ROC curve (AUC) was 0.781 (95% CI, 0.719-0.833) for the risk prediction of MVI in HCC patients. Multivariate analysis showed that tumor size > 5 cm, HCC combined with MVI and GLR > 56.0 were independent risk factors for poor prognosis in HCC patients. In addition, compared with the non-MVI group, patients in the MVI group had shorter progression-free survival (PFS) and overall survival (OS). Conclusion: GLR could be a predictive biomarker of HCC after operation and a potential predictor of HCC combined with MVI. Keywords: Hepatocellular carcinoma, Microvascular invasion, GLR, Predictive

scholarly journals Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Ying Zhao ◽  
Nan Wang ◽  
Jingjun Wu ◽  
Qinhe Zhang ◽  
Tao Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Therapeutic Response ◽  
Validation Cohort ◽  
Combined Model ◽  
Training Cohort ◽  
Contrast Enhanced ◽  
Three Phase ◽  
Radiological Model

PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

scholarly journals Predictive value of gamma-glutamyl transpeptidase to lymphocyte count ratio in hepatocellular carcinoma patients with microvascular invasion

2020 ◽  
Author(s):  
Hongxing Zhang ◽  
Yu Zhou ◽  
Yicheng Li ◽  
Wanying Qin ◽  
Yunhua Zi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Factor ◽  
Roc Curve ◽  
Lymphocyte Count ◽  
Poor Prognosis ◽  
Microvascular Invasion ◽  
Gamma Glutamyl Transpeptidase ◽  
Gamma Glutamyl ◽  
Count Ratio ◽  
Glutamyl Transpeptidase

Abstract Background: Microvascular invasion (MVI) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). However, there is still a lack of preoperative markers to predict MVI in HCC. This study intends to explore the potential application value of the gamma-glutamyl transpeptidase (GGT) to lymphocyte count ratio (GLR) in predicting MVI in HCC and provide guidance for clinical diagnosis and treatment.Methods: From March 2010 to December 2015, 230 HCC patients who underwent surgical treatment in the Affiliated Hospital of Guilin Medical University were selected. Clinicopathological parameters between the MVI group (n = 115) and the non-MVI group (n = 115) were comparatively analyzed. The GLR was used as the potential risk factor for HCC with MVI, and its optimal cut-off value was estimated by using the receiver operating characteristic (ROC) curve. The Kaplan-Meier method was used to analyze the survival of HCC patients, and univariate and multivariate Cox regression analyses were used to establish independent predictors affecting postoperative HCC patients.Results: The GLR levels in the MVI group and non-MVI group were 84.83 ± 61.84 and 38.42 ± 33.52 (p < 0.001), respectively. According to ROC curve analysis, the optimal cut-off value of GLR was 56.0, and the area under the ROC curve (AUC) was 0.781 (95% CI, 0.719-0.833) for the risk prediction of MVI in HCC patients. Multivariate analysis showed that tumor size > 5 cm, HCC combined with MVI and GLR > 56.0 were independent risk factors for poor prognosis in HCC patients. In addition, compared with the non-MVI group, patients in the MVI group had shorter progression-free survival (PFS) and overall survival (OS).Conclusion: GLR could be a predictive biomarker of HCC after operation and a potential predictor of HCC combined with MVI.

scholarly journals Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib

2020 ◽  
Vol 10 ◽  
Author(s):  
Bin-Yan Zhong ◽  
Zhi-Ping Yan ◽  
Jun-Hui Sun ◽  
Lei Zhang ◽  
Zhong-Heng Hou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Transarterial Chemoembolization ◽  
Validation Cohort ◽  
Survival Prediction ◽  
Ann Model ◽  
Training Cohort ◽  
Prognostic Performance ◽  
Independent Risk Factors ◽  
Artificial Neural Network Ann

PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.

scholarly journals Preoperative and Postoperative Nomograms in Predicting Early Recurrence of Hepatocellular Carcinoma Without Macrovascular Invasion After Curative Resection

2021 ◽  
Author(s):  
Yanfang Zhang ◽  
Liangliang Xu ◽  
Mingqing Xu ◽  
Hong Tang
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Early Recurrence ◽  
Tumor Diameter ◽  
Training Cohort ◽  
Factors Associated ◽  
Tumor Number ◽  
Before And After

Abstract This study aimed to establish pre- and postoperative nomograms in predicting postoperative early recurrence (ER) for hepatocellular carcinoma (HCC) without macrovascular invasion. The patients who underwent curative LR for HCC from January 2012 to December 2016 in our center were divided into training and internal prospective validation cohorts. Nomograms were constructed based on the independent risk factors derived from multivariate logistic regression analyses in training cohort. The predictive performance of nomograms was validated by internal prospective validation cohort. A total of 698 patients fulfilled with eligible criteria. Among them, 265 out of 482 patients (55.0%) in training cohort and 120 out 216 (55.6%) patients in validation cohort developed ER. The preoperative risk factors associated with ER were age, alpha fetoprotein (AFP), tumor diameter, tumor number; the postoperative risk factors associated with ER were age, tumor diameter, tumor number, microvasular invasion (MVI) and differentiation. The pre- and postoperative nomograms based on these factors showed good accuracy with C-indices of 0.712 and 0.850 in training cohort, and 0.754 and 0.857 in validation cohort, respectively. The calibration curves showed optimal agreement between the prediction by the nomograms and actual observation. The area under the receiver operating characteristic curves of pre- and postoperative nomograms were 0.721 and 0.848 in training cohort, and 0.754 and 0.844 in validation cohort, respectively. Present nomograms showed good performance in predicting ER for HCC without macrovascular invasion before and after surgery, which were helpful for doctors in designation of treatments and selection of patients for regularly surveillance or administration of neoadjuvant therapies.

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