Noninvasive prediction of future macrovascular invasion occurrence in hepatocellular carcinoma based on quantitative imaging analysis: A multi-center study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14623-e14623
Author(s):  
JingWei Wei ◽  
Jie Tian ◽  
Sirui Fu ◽  
Ligong Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Validation Cohort ◽  
Quantitative Imaging ◽  
Integrated Model ◽  
Preoperative Imaging ◽  
Rank Test ◽  
Imaging Analysis ◽  
Occurrence Time ◽  
Training Cohort

e14623 Background: To investigate whether preoperative imaging-based analysis could help to predict future macrovascular invasion (MaVI) occurrence in hepatocellular carcinoma (HCC). Methods: A cohort of 224 patients with HCC was enrolled from five independent medical centers (training cohort: n = 154; independent validation cohort: n = 70). Predictive clinical factors were primarily selected by uni- and multi-variable analysis. CT-based imaging analysis was performed based on extraction of 1217 radiomic features. Recursive feature elimination and random forest (RF) were chosen as the optimal radiomics modelling algorithms. A clinical-radiomics integrated model was constructed by RF modelling. Cox-regression analyses further selected risk independent factors. Risk stratification was explored by Kaplan-Meier analysis with log-rank test, regarding to MaVI occurrence time (MOT), progression free survival (PFS) and overall survival (OS). Results: The clinical-radiomics integrated model could successfully predict MaVI occurrence with areas under curve of 0.920 (training cohort, 95% confidence index [CI]: 0.875-0.965) and 0.853 (validation cohort, 95% CI: 0.737-0.970). The radiomics signature added significant improvement to the integrated model in both training and validation cohorts with p-value of 0.009 and 0.008, respectively. Radiomic features: N25_ori_gldzm_IN (hazard ratio [HR]: 0.44; p = 0.001) and N25_Coif1_ngldm_DE (HR: 0.60; p = 0.016) were selected as independent risk factors associated with MaVI occurrence time. The cox-regression model could stratified patients into high-risk and low-risk groups in MOT (p < 0.001), PFS (p = 0.003), and OS (p = 0.007). Conclusions: The noninvasive quantitative imaging analysis could enable preoperative prediction of future MaVI occurrence in HCC with prognosis implication.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen &gt;2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p &lt; 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter &gt;4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p &lt; 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

scholarly journals A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Qinqin Liu ◽  
Jing Li ◽  
Fei Liu ◽  
Weilin Yang ◽  
Jingjing Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cox Model ◽  
Accurate Estimation ◽  
Training Cohort ◽  
Dismal Prognosis ◽  
Texture Parameters ◽  
Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

scholarly journals A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongdong Zhou ◽  
Xiaoli Liu ◽  
Xinhui Wang ◽  
Fengna Yan ◽  
Peng Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Surgical Therapy ◽  
Calibration Curve ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Alpha Fetoprotein ◽  
Clinicopathologic Characteristics

Abstract Background Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. Methods A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. Results The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. Conclusions Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.

Risk Factors for Hepatocellular Carcinoma After Splenectomy in Liver Cirrhotic Patients

2021 ◽  
pp. 000313482110415
Author(s):  
Naruhiko Honmyo ◽  
Tsuyoshi Kobayashi ◽  
Shintaro Kuroda ◽  
Kentaro Ide ◽  
Masahiro Ohira ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Portal Hypertension ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Hemoglobin Level ◽  
Rank Test ◽  
Predictive Index ◽  
Preoperative Hemoglobin ◽  
Preoperative Hemoglobin Level

Background Splenectomy is sometimes indicated for portal hypertension caused by cirrhosis, which is a risk for hepatic carcinogenesis. This study aimed to identify risk factors for hepatocellular carcinoma (HCC) development after splenectomy. Methods This retrospective study included 65 patients who underwent splenectomy for portal hypertension between 2009 and 2017. Cox regression analyses were performed to identify factors related to HCC development after splenectomy. The predictive index for HCC development was constructed from the results of multivariate analysis, and 3 risk-dependent groups were defined. Discrimination among the groups was estimated using Kaplan-Meier curves and the log-rank test. Results Post-splenectomy, 36.9% of patients developed HCC. In the univariate analysis, the etiology of cirrhosis (hepatitis C virus antibody, P = .005, and hepatitis B surface antigen, P = .008, referring to non-B and non-C patients, respectively), presence of HCC history ( P < .001), and preoperative hemoglobin level ( P = .007) were related to HCC development, and the presence of HCC history ( P = .002) and preoperative hemoglobin level ( P = .022) were independent risk factors. The predictive index classified three groups at risk; the hazards in each group were significantly different (low vs middle risk, P = .035, and middle vs high risk, P = .011). Discussion The etiology of cirrhosis, presence of HCC history, and hemoglobin level were associated with HCC development after splenectomy. The predictive model may aid in HCC surveillance after splenectomy for patients with portal hypertension.

Liver regeneration after radiotherapy in patients with hepatocellular carcinoma: A simple-to-use model and nomogram.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16115-e16115
Author(s):  
Ting-Shi Su ◽  
Li-Qing Li ◽  
Shi-Xiong Liang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Stratification ◽  
Liver Regeneration ◽  
Validation Cohort ◽  
Liver Volume ◽  
Normal Liver ◽  
Target Area ◽  
Good Prediction ◽  
Characteristic Analysis ◽  
Training Cohort

e16115 Background: In the past clinical practice of radiotherapy for liver cancer, liver regeneration (LR) which is beneficial to the prevention or recovery of radiation-induced liver injury, has not received enough attention. In current study, we aimed to build and validate multivariate model for liver regeneration after radiation therapy for hepatocellular carcinoma (HCC) based on data from 2 prospective studies. Methods: Thirty patients treated with preoperative downstaging radiotherapy were prospectively included in the training cohort, and 21 patients treated with postoperative adjuvant radiotherapy were included in the validation cohort. Liver regeneration was defined as an increase of more than 10% of normal liver volume in the areas of the protected hepatic segment or lobe, without Child-Pugh class decreased and tumor progression compared to pre-radiotherapy. Model and nomogram of liver regeneration after radiotherapy were developed and validated. The cut-off points of each optimal predictors were obtained using receiver-operating characteristic analysis. Risk stratification based on the cut-off point was conducted to compare the proportion of patients with liver regeneration between subgroups. Results: After radiotherapy, 12 (40%) cases in the training cohort and 13 (61.9%) cases in the validation cohort experienced liver regeneration. The model and nomogram of liver regeneration based on SVs20 (standard residual liver volume spared from at least 20 Gy) and alanine aminotransferase (ALT) showed good prediction performance (AUC = 0.759) in training cohort and performed well (AUC = 0.808) in the validation cohort. The risk stratification according to the cutoffs of SVs20 with 303.4 mL and ALT with 43 U/L demonstrated clear differentiation in risk of liver regeneration between the training(P = 0.049) and entire cohort (P = 0.032). The proportion of patients with liver regeneration decrease progressively with 88.9% in high-probability group (ALT＜43 U/L and SVs20＜303.4 mL), 60% in high-intermediate probability group (ALT ≥43 U/L and SVs20＜303.4 mL), 43.75% in low-intermediate probability group (ALT＜43 U/L and SVs20≥303.4 mL) and 33% in low- probability group (ALT≥43 U/L and SVs20≥303.4 mL). Conclusions: SVs20 and ALT are optimal predictors for liver regeneration. This simple-to-use nomogram is beneficial to the constraints of normal liver outside the radiotherapy target area and make prognosis-based decision without complex calculations. Clinical trial information: ChiCTR1800015350. [Table: see text]

scholarly journals Prognostic Role of Serum Cytokeratin-19 Fragment (CYFRA 21-1) in Patients with Hepatocellular Carcinoma

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2776 ◽  
Author(s):  
Gian Paolo Caviglia ◽  
Michela Ciruolo ◽  
Antonella Olivero ◽  
Patrizia Carucci ◽  
Emanuela Rolle ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Stem Cell Marker ◽  
Rank Test ◽  
Cytokeratin 19 ◽  
Serum Samples ◽  
Cox Regression Analysis ◽  
Keratin 19 ◽  
New Diagnosis

Keratin 19 (K19) is a cancer stem cell marker expressed by a subpopulation of hepatocellular carcinoma (HCC), associated with tumor aggressiveness. We evaluated the prognostic value of serum K19 fragment (CYFRA 21-1), in comparison or in combination with alpha-fetoprotein (AFP) and protein induced by vitamin-K absence or antagonist-II (PIVKA-II), in patients with HCC. A total of 160 patients (28F/132M; median age 62, range 44–86 years) with a new diagnosis of HCC and available serum samples collected at tumor diagnosis were analyzed retrospectively. Median overall survival (OS) after HCC diagnosis was 35.1, 95% CI 27.1–70.5 months. Multivariate Cox regression analysis showed that CYFRA 21-1 > 2.7 ng/mL (hazard ratio (HR) = 3.39, p < 0.001), AFP > 20 ng/mL (HR = 2.27, p = 0.007), and PIVKA-II > 200 mAU/mL (HR = 2.17, p = 0.020) were independent predictors of OS. The combination of biomarkers positivity allowed us to stratify patients with HCC into four risk categories associated with a progressively lower survival probability (log-rank test, p < 0.001). CYFRA 21-1 resulted an independent prognostic factor of patients with HCC and its combination with AFP and PIVKA-II might be useful to tailor personalized treatment strategies.

scholarly journals Prognostic implications of alcohol dehydrogenases in hepatocellular carcinoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiangye Liu ◽  
Tingting Li ◽  
Delong Kong ◽  
Hongjuan You ◽  
Fanyun Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
High Expression ◽  
Rank Test ◽  
Good Survival ◽  
Alcohol Dehydrogenases ◽  
Kaplan Meier ◽  
Incidence And Mortality ◽  
Prognostic Implications

Abstract Background Hepatocellular carcinoma (HCC) is a malignancy with high incidence and mortality rates worldwide. Alcohol dehydrogenases (ADHs) are huge family of dehydrogenase enzymes and associated with the prognosis of various cancers. However, comprehensive analysis of prognostic implications related to ADHs in HCC is still lacking and largely unknown. Methods The expression profiles and corresponding clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA). Wilcoxon signed-rank test was employed to evaluate the expression of ADHs. Cox regression and Kaplan-Meier analyses were used to investigate the association between clinicopathological characteristics and survival. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses were performed and visualized using R/BiocManager package. Results We found that the expression of ADH1A, ADH1B, ADH1C, ADH4, and ADH6 was significantly downregulated in HCC samples compared to normal liver samples. Our univariate and multivariate Cox regression analyses results showed that high expression of ADH1A, ADH1B, ADH1C, ADH4, and ADH6 was considered as an independent factor with an improved prognosis for the survival of HCC patients. Moreover, our Kaplan-Meier analysis results also revealed that high expression of AHD1A, ADH1B, ADH1C, ADH4, and ADH6 was significantly associated with good survival rate in HCC patients. In addition, GO, KEGG, and GSEA analyses unveiled several oncogenic signaling pathways were negatively associated high expression of ADHs in HCC. Conclusion In the present study, our results provide the potential prognostic biomarkers or molecular targets for the patients with HCC.

scholarly journals Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Qian Chen ◽  
Shu Wang ◽  
Jing-He Lang
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Validation Cohort ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Ovarian Clear Cell Carcinoma ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

scholarly journals Lymphopenia Is Associated with Gross Target Volumes and Fractions in Hepatocellular Carcinoma Patients Treated with External Beam Radiation Therapy and Also Indicates Worse Overall Survival

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hai-Ge Zhang ◽  
Ping Yang ◽  
Tao Jiang ◽  
Jian-Ying Zhang ◽  
Xue-Juan Jin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
External Beam Radiation ◽  
Rank Test ◽  
Beam Radiation ◽  
Kaplan Meier ◽  
Target Volumes ◽  
The Relationship

Purpose. To investigate whether lymphocyte nadir induced by radiation is associated with survival and explore its underlying risk factors in patients with hepatocellular carcinoma (HCC). Methods. Total lymphocyte counts were collected from 184 HCC patients treated by radiotherapy (RT) with complete follow-up. Associations between gross tumor volumes (GTVs) and radiation-associated parameters with lymphocyte nadir were evaluated by Pearson/Spearman correlation analysis and multiple linear regression. Kaplan–Meier analysis, log-rank test, as well as univariate and multivariate Cox regression were performed to assess the relationship between lymphocyte nadir and overall survival (OS). Results. GTVs and fractions were negatively related with lymphocyte nadir (p<0.001 and p=0.001, respectively). Lymphocyte nadir and Barcelona Clinic Liver Cancer (BCLC) stage were independent prognostic factors predicting OS of HCC patients (all p<0.001). Patients in the GTV ≤55.0 cc and fractions ≤16 groups were stratified by lymphocyte nadir, and the group with the higher lymphocyte counts (LCs) showed longer survival than the group with lower LCs (p<0.001 and p=0.006, respectively). Patient distribution significantly differed among the RT fraction groups according to BCLC stage (p<0.001). However, stratification of patients in the same BCLC stage by RT fractionation showed that the stereotactic body RT (SBRT) group achieved the best survival. Furthermore, there were significant differences in lymphocyte nadir among patients in the SBRT group. Conclusions. A lower lymphocyte nadir during RT was associated with worse survival among HCC patients. Smaller GTVs and fractions reduced the risk of lymphopenia.

scholarly journals A Novel Online Calculator Based on Serum Biomarkers to Detect Hepatocellular Carcinoma among Patients with Hepatitis B

2019 ◽  
Vol 65 (12) ◽  
pp. 1543-1553 ◽  
Author(s):  
Tian Yang ◽  
Hao Xing ◽  
Guoqiang Wang ◽  
Nianyue Wang ◽  
Miaoxia Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Validation Cohort ◽  
Area Under The Curve ◽  
Serum Biomarkers ◽  
Web Based ◽  
Training Cohort ◽  
B Virus ◽  
Online Calculator ◽  
Diagnostic Sensitivity And Specificity

Abstract BACKGROUND Early detection of hepatocellular carcinoma (HCC) among hepatitis B virus (HBV)-infected patients remains a challenge, especially in China. We sought to create an online calculator of serum biomarkers to detect HCC among patients with chronic hepatitis B (CHB). METHODS Participants with HBV-HCC, CHB, HBV-related liver cirrhosis (HBV-LC), benign hepatic tumors, and healthy controls (HCs) were recruited at 11 Chinese hospitals. Potential serum HCC biomarkers, protein induced by vitamin K absence or antagonist-II (PIVKA-II), α-fetoprotein (AFP), lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3) and α-L-fucosidase (AFU) were evaluated in the pilot cohort. The calculator was built in the training cohort via logistic regression model and validated in the validation cohort. RESULTS In the pilot study, PIVKA-II and AFP showed better diagnostic sensitivity and specificity compared with AFP-L3 and AFU and were chosen for further study. A combination of PIVKA-II and AFP demonstrated better diagnostic accuracy in differentiating patients with HBV-HCC from patients with CHB or HBV-LC than AFP or PIVKA-II alone [area under the curve (AUC), 0.922 (95% CI, 0.908–0.935), sensitivity 88.3% and specificity 85.1% for the training cohort; 0.902 (95% CI, 0.875–0.929), 87.8%, and 81.0%, respectively, for the validation cohort]. The nomogram including AFP, PIVKA-II, age, and sex performed well in predicting HBV-HCC with good calibration and discrimination [AUC, 0.941 (95% CI, 0.929–0.952)] and was validated in the validation cohort [AUC, 0.931 (95% CI, 0.909–0.953)]. CONCLUSIONS Our results demonstrated that a web-based calculator including age, sex, AFP, and PIVKA-II accurately predicted the presence of HCC in patients with CHB. ClinicalTrials.gov Identifier NCT03047603

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