scholarly journals Construction and validation of a nomogram for predicting cancer-specific survival in hepatocellular carcinoma patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Gaobo Huang ◽  
Pengkang Chang ◽  
Wei Zhang ◽  
Tao Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Seer Database ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Discrimination Power ◽  
Training Cohort ◽  
Good Agreement

AbstractThe prognosis of patients with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a research hotspot. This study aimed to incorporate important factors obtained from SEER database to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of patients with HCC and ICC. We obtained patient data from SEER database. The nomogram was constructed base on six prognostic factors for predicting CSS rates in HCC patients. The nomogram was validated by concordance index (C-index), the receiver operating characteristic (ROC) curve and calibration curves. A total of 3227 patients diagnosed with HCC (3038) and ICC (189) between 2010 and 2015 were included in this study. The C-index of the nomogram for HCC patients was 0.790 in the training cohort and 0.806 in the validation cohort. The 3- and 5-year AUCs were 0.811 and 0.793 in the training cohort. The calibration plots indicated that there was good agreement between the actual observations and predictions. In conclusion, we constructed and validated a nomogram for predicting the 3- and 5-year CSS in HCC patients. We have confirmed the precise calibration and excellent discrimination power of our nomogram.

scholarly journals Prediction of distant metastasis of spinal and pelvic osteosarcoma by constructing and validating nomogram:a retrospective study based on SEER database

2022 ◽  
Author(s):  
Yu Lin ◽  
Zhenyu Wang ◽  
Gang Chen ◽  
Wenge Liu
Keyword(s):  
Risk Factor ◽  
Receiver Operating Characteristic ◽  
Survival Time ◽  
Distant Metastasis ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Seer Database ◽  
Lasso Regression ◽  
Training Cohort ◽  
Kaplan Meier

Abstract Background:Spinal and pelvic osteosarcoma is a rare type of all osteosarcomas,and distant metastasis is an important factor for poor prognosis of this disease. There are no similar studies on prediction of distant metastasis of spinal and pelvic osteosarcoma. We aim to construct and validate a nomogram to predict the risk of distant metastasis of spinal and pelvic osteosarcoma.Methods:We collected the data on patients with spinal and pelvic osteosarcoma from the Surveillance, Epidemiology, and End Results(SEER) database retrospectively. The Kaplan-Meier curve was used to compare differences in survival time between patients with metastasis and non-metastasis. Total patients were randomly divided into training cohort and validation cohort. The risk factor of distant metastasis were identified via the least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic analysis. The nomogram we constructed were validated internally and externally by C-index, calibration curves,receiver operating characteristic(ROC) curve and Decision curve analysis (DCA).Results:The Kaplan-Meier curve showed that the survival time of non-metastatic patients was longer than that of metastatic patients(P＜0.001).All patients(n=358) were divided into training cohort(n=269) and validation cohort(n=89).The LASSO regression selected five meaningful variables in the training cohort. The multivariate logistic regression analysis demonstrated that surgery(yes,OR=0.175, 95%CI=0.095-0.321,p=0.000) was the independent risk factors for distant metastasis of patients with spinal and pelvic osteosarcoma. The C-index and calibration curves showed the good agreement between the predicted results and the actual results. The area under the receiver operating characteristic curve(AUC) values were 0.748(95%CI=0.687-0.817) and 0.758(95%CI=0.631-0.868) in the training and validation cohorts respectively. The DCA showed that the nomogram has a good clinical usefulness and net benefit.Conclusion:No surgery is the independent risk factor of distant metastasis of spinal and pelvic osteosarcoma. The nomogram we constructed to predict the probability of distant metastasis of patients with spinal and pelvic osteosarcoma is reliable and effective by internal and external verification.

scholarly journals Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Haisheng You ◽  
Mengmeng Teng ◽  
Chun Xia Gao ◽  
Bo Yang ◽  
Sasa Hu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prognostic Factors ◽  
Elderly Patients ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Validation Cohort ◽  
Discrimination Performance ◽  
Seer Database ◽  
Histologic Subtype ◽  
Training Cohort

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

scholarly journals Development and validation of a nomogram to predict cancer-specific survival of uveal melanoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qiaozhu Zeng ◽  
Yuou Yao ◽  
Mingwei Zhao
Keyword(s):  
Prognostic Factors ◽  
Uveal Melanoma ◽  
Validation Cohort ◽  
Staging System ◽  
Competing Risk ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Ajcc Staging System ◽  
Ajcc Staging

Abstract Background Uveal melanoma (UM) is a rare but aggressive cancer, which is the most common primary intraocular malignancy in adults. We aimed to develop and validate a competing risk nomogram to predict cancer-specific survival (CSS) of patients with UM, as well as compare its prognostic value with that of the American Joint Committee on Cancer (AJCC) staging system. Methods Data of patients diagnosed with UM from 2010 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We extracted and integrated significant prognostic factors based on competing risk regression to build a nomogram. The nomogram with an online prediction version was also created. The performance of the nomogram was evaluated using Harrell’s concordance index (C-index) and calibration plots. Receiver operating characteristic (ROC) curve was carried out to estimate clinical applicability of the model. Improvements in the predictive accuracy of our new model compared with AJCC staging system were estimated by calculating the relative integrated discrimination improvement (IDI) and the net reclassification improvement (NRI). Results A total of 839 eligible patients with primary UM were randomly assigned to a training cohort (588, 70%) and a validation cohort (251, 30%). Age, histological type, T stage and M stage were independent prognostic factors to predict CSS of UM and were incorporated in the nomogram. The calibration plots indicated that the 3- and 5-year CSS probabilities were consistent between the nomogram prediction and the actual observation. The C-index for this model was 0.778 (95% CI:0.756–0.800) and 0.786 (95% CI: 0.749–0.816) in the training cohort and validation cohort. Areas under the curve (AUCs) were 0.814, 0.771, and 0.792 in the training cohort, 0.788, 0.781 and 0.804 in the validation cohort, respectively. The NRI value in AJCC staging system was − 0.153 (95% CI -0.29 – − 0.041) for 3 years of follow-up and − 0.276 (95% CI -0.415 – − 0.132) for 5 years of follow-up. The IDI values for 3 and 5 years of follow-up in the AJCC staging system were − 0.021 (P = 0.076) and − 0.045 (P = 0.004), respectively. Conclusions We have developed and validated a competing risk nomogram to reliably predict cancer-specific survival of patients with UM. This convenient tool may be useful for evaluating cancer-specific prognosis.

Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6046-6046
Author(s):  
Sik-Kwan Chan ◽  
Cheng Lin ◽  
Shao Hui Huang ◽  
Tin Ching Chau ◽  
Qiaojuan Guo ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Bone Metastases ◽  
Validation Cohort ◽  
De Novo ◽  
Multivariable Analysis ◽  
Term Survival ◽  
Training Cohort ◽  
Study Results ◽  
Multivariable Analyses

6046 Background: The eighth edition TNM (TNM-8) classified de novo metastatic (metastatic disease at presentation) nasopharyngeal carcinoma (NPC) as M1 without further subdivision. However, survival heterogeneity exists and long-term survival has been observed in a subset of this population. We hypothesize that certain metastatic characteristics could further segregate survival for de novo M1 NPC. Methods: Patients with previously untreated de novo M1 NPC prospectively treated in two academic institutions (The University of Hong Kong [n = 69] and Provincial Clinical College of Fujian Medical University [n = 114] between 2007 and 2016 were recruited and re-staged based on TNM-8 in this study. They were randomized in 2:1 ratio to generate a training cohort (n = 120) and validation cohort (n = 63) respectively. Univariable and multivariable analyses (MVA) were performed for the training cohort to identify the anatomic prognostic factors of overall survival (OS). We then performed recursive partitioning analysis (RPA) which incorporated the anatomic prognostic factors identified in multivariable analyses and derived a new set of RPA stage groups (Anatomic-RPA groups) which predicted OS in the training cohort. The significance of Anatomic-RPA groups in the training cohort was then validated in the validation cohort. UVA and MVA were performed again on the validation cohorts to identify significant OS prognosticators. Results: The training and the validation cohorts had a median follow-up of 27.2 months and 30.2 months, respectively, with the 3-year OS of 51.6% and 51.1%, respectively. Univariable analysis (UVA) and multivariable analysis (MVA) revealed that co-existing liver and bone metastases was the only factor prognostic of OS. Anatomic-RPA groups based on the anatomic prognostic factors identified in UVA and MVA yielded good segregation (M1a: no co-existing liver and bone metastases and M1b: co-existing both liver and bone metastases; median OS 39.5 and 23.7 months respectively; P =.004). RPA for the validation set also confirmed good segregation with co-existing liver and bone metastases (M1a: no co-existing liver and bone metastases and M1b: co-existing liver and bone metastases), with median OS 47.7 and 16.0 months, respectively; P =.008). It was also the only prognostic factor in UVA and MVA in the validation cohort. Conclusions: Our Anatomic-RPA M1 stage groups with anatomical factors provided better subgroup segregation for de novo M1 NPC. The study results provide a robust justification to refine M1 categories in future editions of TNM staging classification.

Construction and Validation of a Nomogram for Predicting Progression-Free Survival in Patients with Early-Stage Testicular Germ Cell Tumor

2021 ◽  
Vol 16 ◽  
Author(s):  
Jin-Guo Chen ◽  
Jing-Quan Wang ◽  
Tian-Wen Peng ◽  
Zhe-Sheng Chen ◽  
Shan-Chao Zhao
Keyword(s):  
Prognostic Factors ◽  
Validation Cohort ◽  
Early Stage ◽  
Predictive Ability ◽  
Testicular Germ Cell Tumor ◽  
Progression Free Survival ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Free Survival ◽  
Training Cohort

Background: Testicular Germ Cell Tumor (TGCT) is the most common malignant tumor in young men, but there is a lack of prediction model to evaluate prognosis of patients with TGCT. Objective: To explore the prognostic factors for Progression-Free Survival (PFS) and construct a nomogram model for patients with early-stage TGCT after radical orchiectomy. Methods: Patients with TGCT from The Cancer Genome Atlas (TCGA) database were used as the training cohort; univariate and multivariate cox analysis were performed. A nomogram was constructed based on the independent prognostic factors. Patients from the Nanfang Hospital affiliated with Southern Medical University were used as the cohort to validate the predictive ability using the nomogram model. Harrell's concordance index (C-index) and calibration plots were used to evaluate the nomogram. Results: A total of 110 and 62 patients with TGCT were included in training cohort and validation cohort, respectively. Lymphatic Vascular Invasion (LVI), American Joint Committee on Cancer (AJCC) stage and adjuvant therapy were independent prognostic factors in multivariate regression analyses and were included to establish a nomogram. The C-index in the training cohort for 1-, 3-, and 5-year PFS were 0.768, 0.74 and 0.689, respectively. While the C-index for 1-, 3-, and 5-year PFS in the external validation cohort were 0.853, 0.663 and 0.609, respectively. The calibration plots for 1-, 3-, and 5-year PFS in the training and validation cohort showed satisfactory consistency between predicted and actual outcomes. The nomogram revealed a better predictive ability for PFS than AJCC staging system. Conclusion: The nomogram as a simple and visual tool to predict individual PFS in patients with TGCT could guide clinicians and clinical pharmacists in therapeutic strategy.

scholarly journals Prognostic Nutritional Index as an independent prognostic factor in locoregionally advanced squamous cell head and neck cancer

ESMO Open ◽  
2018 ◽  
Vol 3 (6) ◽  
pp. e000425 ◽  
Author(s):  
Gema Bruixola ◽  
Javier Caballero ◽  
Federica Papaccio ◽  
Angelica Petrillo ◽  
Aina Iranzo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Prognostic Nutritional Index ◽  
Training Cohort ◽  
Lymphocyte Ratio ◽  
Nutritional Index

BackgroundLocally advanced head and neck squamous cell carcinoma (LAHNSCC) is a heterogeneous disease in which better predictive and prognostic factors are needed. Apart from TNM stage, both systemic inflammation and poor nutritional status have a negative impact on survival.MethodsWe retrospectively analysed two independent cohorts of a total of 145 patients with LAHNSCC treated with induction chemotherapy followed by concurrent chemoradiotherapy at two different academic institutions. Full clinical data, including the Prognostic Nutritional Index (PNI), neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio, were analysed in a training cohort of 50 patients. Receiver operating characteristic curve analysis was used to establish optimal cut-off. Univariate and multivariate analyses of prognostic factors for overall survival (OS) were performed. Independent predictors of OS identified in multivariate analysis were confirmed in a validation cohort of 95 patients.ResultsIn the univariate analysis, low PNI (PNI<45) (p=0.001), large primary tumour (T4) (p=0.044) and advanced lymph node disease (N2b-N3) (p=0.025) were significantly associated with poorer OS in the validation cohort. The independent prognostic factors in the multivariate analysis for OS identified in the training cohort were dRNL (p=0.030) and PNI (p=0.042). In the validation cohort, only the PNI remained as independent prognostic factor (p=0.007).ConclusionsPNI is a readily available, independent prognostic biomarker for OS in LAHNSCC. Adding PNI to tumour staging could improve individual risk stratification of patients with LAHNSCC in future clinical trials.

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 Construction and Validation of Nomograms for Predicting the Prognosis of Juvenile Osteosarcoma: A Real-World Analysis in the SEER Database

2020 ◽  
Vol 19 ◽  
pp. 153303382094771
Author(s):  
Yao Jiang ◽  
Tianyu Wang ◽  
Zizheng Wei
Keyword(s):  
Predictive Performance ◽  
Seer Database ◽  
Clinical Predictors ◽  
Malignant Bone Tumors ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Ajcc Stage ◽  
High Incidence ◽  
Css Construction ◽  
Cox Analysis

Background: Osteosarcoma is one of the most common malignant bone tumors, with a high incidence in adolescence. The objective of this study was to construct prognostic nomograms for predicting the prognosis of juvenile osteosarcoma. Methods: Patients with osteosarcoma diagnosed between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The essential clinical predictors were identified with univariate and multivariate Cox analysis. Nomograms were constructed to predict the 3- and 5-year cancer- specific survival (CSS) and overall survival (OS). Concordance index (C-index) and calibration plots were performed to validate the predictive performance of nomograms. Results: We enrolled 736 adolescents with osteosarcoma from the SEER database, with 516 samples grouped into a training cohort and 220 samples grouped into a validation cohort. In multivariate analysis of the training cohort, predictors including tumor size, surgery treatment and AJCC stage were found to be associated with OS and CSS, while age was only associated with CSS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of adolescents with osteosarcoma. The C-index and calibration curves also showed the satisfactory performance of these nomograms for prognosis prediction. Conclusion: The developed nomograms are useful tools for precisely predicting the prognosis of adolescents with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

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 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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