Reply to: Correspondence concerning “Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection”

Chan, Anthony W.H.; Berhane, Sarah; Cucchetti, Alessandro; Johnson, Philip J.

doi:10.1016/j.jhep.2018.11.026

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Of note, ERASL post-resection was developed and validated for predicting time to recurrence considering death as a censoring event, though it was initially stated to predict RFS. 29,30 Most variables were complete or had less than 10% of missing data, to the exception of positive surgical margin (17%) and albumin level (29%). To reduce potential selection bias arising from complete-case analysis, all predictive analyses (including ERS development and evaluation of ERS and post-resection ERASL predictive performances) were performed after missing data imputation using missForest, a non-parametric imputation method based on random forest that accommodates non-linearities and interactions.…”

Section: Discussionmentioning

confidence: 99%

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ERS: A simple scoring system to predict early recurrence after surgical resection for hepatocellular carcinoma

Costentin

Audureau

Park

et al. 2023

Liver International

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BackgroundSurgical resection (SR) is a potentially curative treatment of hepatocellular carcinoma (HCC) hampered by high rates of recurrence. New drugs are tested in the adjuvant setting, but standardised risk stratification tools of HCC recurrence are lacking.ObjectivesTo develop and validate a simple scoring system to predict 2‐year recurrence after SR for HCC.Methods2359 treatment‐naïve patients who underwent SR for HCC in 17 centres in Europe and Asia between 2004 and 2017 were divided into a development (DS; n = 1558) and validation set (VS; n = 801) by random sampling of participating centres. The Early Recurrence Score (ERS) was generated using variables associated with 2‐year recurrence in the DS and validated in the VS.ResultsVariables associated with 2‐year recurrence in the DS were (with associated points) alpha‐fetoprotein (<10 ng/mL:0; 10–100: 2; >100: 3), size of largest nodule (≥40 mm: 1), multifocality (yes: 2), satellite nodules (yes: 2), vascular invasion (yes: 1) and surgical margin (positive R1: 2). The sum of points provided a score ranging from 0 to 11, allowing stratification into four levels of 2‐year recurrence risk (Wolbers' C‐indices 66.8% DS and 68.4% VS), with excellent calibration according to risk categories. Wolber's and Harrell's C‐indices apparent values were systematically higher for ERS when compared to Early Recurrence After Surgery for Liver tumour post‐operative model to predict time to early recurrence or recurrence‐free survival.ConclusionsERS is a user‐friendly staging system identifying four levels of early recurrence risk after SR and a robust tool to design personalised surveillance strategies and adjuvant therapy trials.

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Section: Discussionmentioning

confidence: 99%

“…ERASL post‐resection allows the assessment of early recurrence risk after SR using clinical, biological and pathological parameters. Of note, ERASL post‐resection was developed and validated for predicting time to recurrence considering death as a censoring event, though it was initially stated to predict RFS 29,30 …”

Section: Methodsmentioning

confidence: 99%

ERS: A simple scoring system to predict early recurrence after surgical resection for hepatocellular carcinoma

Costentin

Audureau

Park

et al. 2023

Liver International

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Use of a novel index, the A-index, and its associated nomogram to predict overall survival rates after resection of primary hepatocellular carcinoma

Cai

Hou

Zhou

et al. 2020

Clinica Chimica Acta

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Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

Zhang,

Yang,

et al. 2023

World J Gastroenterol

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BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis. AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC. METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice. RESULTS Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value. CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

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Reply to: Correspondence concerning “Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection”

Cited by 3 publications

References 15 publications

ERS: A simple scoring system to predict early recurrence after surgical resection for hepatocellular carcinoma

ERS: A simple scoring system to predict early recurrence after surgical resection for hepatocellular carcinoma

Use of a novel index, the A-index, and its associated nomogram to predict overall survival rates after resection of primary hepatocellular carcinoma

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

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