Title: Comparison of preoperative two-dimensional shear wave elastography, indocyanine
green clearance test and biomarkers for post hepatectomy liver failure prediction
Version: 2 Date: 17 Feb 2021
Reviewer's report:
Pre-operative prediction of post-operative liver failure in hepatocellular carcinoma is clinically
important to improve patient survival and rational pre-operative clinical decision making by
surgeons, but there is no efficient diagnostic method for pre-operative prediction. The authors
conducted a clinically relevant retrospective study comparing 2D-SWE, ICG-R15, ALBI, APRI
and FIB-4 for preoperative prediction of postoperative hepatic resection in hepatocellular
carcinoma.
Major points:
1. The sample size in this study was too small and could be increased by increasing the time span
to increase the sample size while reducing selection bias. Also, the calculation of the sample
size is not stated in the methods section.
2. The method section of this paper shows that patient cohorts are incorporated into the training
and validation sets chronologically. The patient cohort should be randomised and then included
in the training and validation sets respectively. In small sample machine learning, the ratio of
samples in the training and validation sets is typically 7:3.
3. The calculation of P-values for the different diagnostic methods of AUC is not detailed.
Minor points:
1. The flow chart for the inclusion and exclusion of patients should be added in the figures.
2. The AUC curve for ICG-R15 is not shown in the figure 2, which is inconsistent with the
narrative in the result section of this paper.
3. The results of the AUC curves for different diagnostic methods in the training and validation
sets should be shown separately in the figures.
Title: Comparison of preoperative two-dimensional shear wave elastography, indocyanine
green clearance test and biomarkers for post hepatectomy liver failure prediction
Version: 2 Date: 17 Feb 2021
Reviewer's report:
Pre-operative prediction of post-operative liver failure in hepatocellular carcinoma is clinically
important to improve patient survival and rational pre-operative clinical decision making by
surgeons, but there is no efficient diagnostic method for pre-operative prediction. The authors
conducted a clinically relevant retrospective study comparing 2D-SWE, ICG-R15, ALBI, APRI
and FIB-4 for preoperative prediction of postoperative hepatic resection in hepatocellular
carcinoma.
Major points:
1. The sample size in this study was too small and could be increased by increasing the time span
to increase the sample size while reducing selection bias. Also, the calculation of the sample
size is not stated in the methods section.
2. The method section of this paper shows that patient cohorts are incorporated into the training
and validation sets chronologically. The patient cohort should be randomised and then included
in the training and validation sets respectively. In small sample machine learning, the ratio of
samples in the training and validation sets is typically 7:3.
3. The calculation of P-values for the different diagnostic methods of AUC is not detailed.
Minor points:
1. The flow chart for the inclusion and exclusion of patients should be added in the figures.
2. The AUC curve for ICG-R15 is not shown in the figure 2, which is inconsistent with the
narrative in the result section of this paper.
3. The results of the AUC curves for different diagnostic methods in the training and validation
sets should be shown separately in the figures.
Please review this paper.
user
Title: Comparison of preoperative two-dimensional shear wave elastography, indocyanine
green clearance test and biomarkers for post hepatectomy liver failure prediction
Version: 2 Date: 17 Feb 2021
Reviewer's report:
Pre-operative prediction of post-operative liver failure in hepatocellular carcinoma is clinically
important to improve patient survival and rational pre-operative clinical decision making by
surgeons, but there is no efficient diagnostic method for pre-operative prediction. The authors
conducted a clinically relevant retrospective study comparing 2D-SWE, ICG-R15, ALBI, APRI
and FIB-4 for preoperative prediction of postoperative hepatic resection in hepatocellular
carcinoma.
Major points:
1. The sample size in this study was too small and could be increased by increasing the time span
to increase the sample size while reducing selection bias. Also, the calculation of the sample
size is not stated in the methods section.
2. The method section of this paper shows that patient cohorts are incorporated into the training
and validation sets chronologically. The patient cohort should be randomised and then included
in the training and validation sets respectively. In small sample machine learning, the ratio of
samples in the training and validation sets is typically 7:3.
3. The calculation of P-values for the different diagnostic methods of AUC is not detailed.
Minor points:
1. The flow chart for the inclusion and exclusion of patients should be added in the figures.
2. The AUC curve for ICG-R15 is not shown in the figure 2, which is inconsistent with the
narrative in the result section of this paper.
3. The results of the AUC curves for different diagnostic methods in the training and validation
sets should be shown separately in the figures.
assistant