Nomogram for prediction of the international study Group of Liver Surgery (ISGLS) grade B/C Posthepatectomy liver failure in HBV-related hepatocellular carcinoma patients: an external validation and prospective application study

Abstract Background To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. Methods Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. Results There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5, 11.0 and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC = 0.868, 95%CI = 0.836–0.900), internal validation (AUC = 0.868, 95%CI = 0.811–0.926) and external validation cohorts (AUC = 0.820, 95%CI = 0.756–0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score < 169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. Conclusions The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

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Nomogram for Prediction of the International Study Group of Liver Surgery (ISGLS) Grade B/C Posthepatectomy Liver Failure in HBV-related Hepatocellular carcinoma Patients: an external validation and prospective application study

10.21203/rs.3.rs-20106/v1 ◽

2020 ◽

Author(s):

Jiazhou Ye ◽

Rong-yun Mai ◽

Wei-xing Guo ◽

Yan-yan Wang ◽

Liang Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Validation Cohort ◽

External Validation ◽

International Study ◽

Study Group ◽

Internal Validation ◽

Predictive Values ◽

Posthepatectomy Liver Failure ◽

International Study Group ◽

Prospective Application

Abstract Background & Aims: To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. Methods: Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. Results: There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5%, 11.6% and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC=0.868, 95%CI=0.808–0.880), internal validation (AUC=0.868, 95%CI=0.794–0.916) and external validation cohorts (AUC=0.820, 95%CI=0.756–0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score <169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. Conclusions: The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

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Nomogram for Prediction of the International Study Group of Liver Surgery (ISGLS) Grade B/C Posthepatectomy Liver Failure in HBV-Related Hepatocellular Carcinoma Patients: An External Validation and Prospective Application Study

10.21203/rs.3.rs-27282/v1 ◽

2020 ◽

Author(s):

Jia-zhou Ye ◽

Rong-yun Mai ◽

Wei-xing Guo ◽

Yan-yan Wang ◽

Liang Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Validation Cohort ◽

External Validation ◽

International Study ◽

Study Group ◽

Internal Validation ◽

Predictive Values ◽

Posthepatectomy Liver Failure ◽

International Study Group ◽

Prospective Application

Abstract BackgroundTo develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients.MethodsPatients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application.ResultsThere were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5%, 11.6% and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC = 0.868, 95%CI = 0.808–0.880), internal validation (AUC = 0.868, 95%CI = 0.794–0.916) and external validation cohorts (AUC = 0.820, 95%CI = 0.756–0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score < 169 or ≧ 169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise.ConclusionsThe nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

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Prediction of Posthepatectomy Liver Failure Proposed by the International Study Group of Liver Surgery

Clinical Nuclear Medicine ◽

10.1097/rlu.0000000000001913 ◽

2018 ◽

Vol 43 (2) ◽

pp. 77-81 ◽

Cited By ~ 7

Author(s):

Yoichi Mizutani ◽

Toshinori Hirai ◽

Shigeki Nagamachi ◽

Atsushi Nanashima ◽

Koichi Yano ◽

...

Keyword(s):

Liver Failure ◽

Liver Surgery ◽

International Study ◽

Study Group ◽

Posthepatectomy Liver Failure ◽

International Study Group

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The Incidence of Posthepatectomy Liver Failure Defined by the International Study Group of Liver Surgery among Living Donors

Journal of Gastrointestinal Surgery ◽

10.1007/s11605-016-3080-8 ◽

2016 ◽

Vol 20 (4) ◽

pp. 757-764 ◽

Cited By ~ 10

Author(s):

Kaori Kuramitsu ◽

Takumi Fukumoto ◽

Tsuyoshi Kobayashi ◽

Kenji Fukushima ◽

Sho Okimoto ◽

...

Keyword(s):

Liver Failure ◽

Liver Surgery ◽

International Study ◽

Living Donors ◽

Study Group ◽

Posthepatectomy Liver Failure ◽

International Study Group

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Posthepatectomy liver failure: A definition and grading by the International Study Group of Liver Surgery (ISGLS)

Surgery ◽

10.1016/j.surg.2010.10.001 ◽

2011 ◽

Vol 149 (5) ◽

pp. 713-724 ◽

Cited By ~ 891

Author(s):

Nuh N. Rahbari ◽

O. James Garden ◽

Robert Padbury ◽

Mark Brooke-Smith ◽

Michael Crawford ◽

...

Keyword(s):

Liver Failure ◽

Liver Surgery ◽

International Study ◽

Study Group ◽

Posthepatectomy Liver Failure ◽

International Study Group

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The Analysis of Posthepatectomy Liver Failure Incidence and Risk Factors Among Right Liver Living Donors According to International Study Group of Liver Surgery Definition

Transplantation Proceedings ◽

10.1016/j.transproceed.2019.01.088 ◽

2019 ◽

Vol 51 (4) ◽

pp. 1121-1126 ◽

Cited By ~ 2

Author(s):

T. Egeli ◽

T. Unek ◽

C. Agalar ◽

M. Ozbilgin ◽

S. Derici ◽

...

Keyword(s):

Risk Factors ◽

Liver Failure ◽

Liver Surgery ◽

International Study ◽

Living Donors ◽

Study Group ◽

Posthepatectomy Liver Failure ◽

International Study Group

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Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.744756 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangtian Zhao ◽

Yukun Zhou ◽

Yuan Zhang ◽

Lujun Han ◽

Li Mao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Accuracy ◽

Sensitivity And Specificity ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Arterial Phase ◽

Combined Model ◽

Internal Validation ◽

Contrast Enhanced

ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

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Development and Validation of a Machine Learning Prognostic Model for Hepatocellular Carcinoma Recurrence After Surgical Resection

Frontiers in Oncology ◽

10.3389/fonc.2020.593741 ◽

2021 ◽

Vol 10 ◽

Author(s):

Yao Huang ◽

Hengkai Chen ◽

Yongyi Zeng ◽

Zhiqiang Liu ◽

Handong Ma ◽

...

Keyword(s):

Machine Learning ◽

Hepatocellular Carcinoma ◽

Surgical Resection ◽

Prognostic Model ◽

Validation Cohort ◽

External Validation ◽

Risk Groups ◽

Time Interval ◽

Time Intervals ◽

Internal Validation

Surgical resection remains primary curative treatment for patients with hepatocellular carcinoma (HCC) while over 50% of patients experience recurrence, which calls for individualized recurrence prediction and early surveillance. This study aimed to develop a machine learning prognostic model to identify high-risk patients after surgical resection and to review importance of variables in different time intervals. The patients in this study were from two centers including Eastern Hepatobiliary Surgery Hospital (EHSH) and Mengchao Hepatobiliary Hospital (MHH). The best-performed model was determined, validated, and applied to each time interval (0–1 year, 1–2 years, 2–3 years, and 3–5 years). Importance scores were used to illustrate feature importance in different time intervals. In addition, a risk heat map was constructed which visually depicted the risk of recurrence in different years. A total of 7,919 patients from two centers were included, of which 3,359 and 230 patients experienced recurrence, metastasis or died during the follow-up time in the EHSH and MHH datasets, respectively. The XGBoost model achieved the best discrimination with a c-index of 0.713 in internal validation cohort. Kaplan-Meier curves succeed to stratify external validation cohort into different risk groups (p < 0.05 in all comparisons). Tumor characteristics contribute more to HCC relapse in 0 to 1 year while HBV infection and smoking affect patients’ outcome largely in 3 to 5 years. Based on machine learning prediction model, the peak of recurrence can be predicted for individual HCC patients. Therefore, clinicians can apply it to personalize the management of postoperative survival.

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