A cohort study of hepatectomy-related complications and prediction model for postoperative liver failure after major liver resection in 1,441 patients without obstructive jaundice

A nomogram for the prediction of survival for colorectal signet ring cell carcinoma after surgery: A population-based study

The aim was to construct and verify a nomogram-based assessment of cancer-specific survival (CSS) in patients with colorectal signet ring cell carcinoma after surgery. Patients were collected from Surveillance, Epidemiology, and End Results program between 2004 and 2015. Independent prognostic indicators were determined in the training cohort by Cox regression model. We identified 2217 eligible patients, who were further categorized into the training set (n = 1693) as well as the validation set (n = 524). Multivariate analysis revealed that age at diagnosis, gender, grade, tumor size, T stage, N stage, and M stage were independent predictive indicators. Then, the above 7 predictive factors were incorporated into a nomogram model to assess CSS, which showed good calibration and discrimination capacities in both sets. Both internal and external calibration plot diagrams revealed that the actual results were consistent with the predicted outcomes. The time-independent area under the curves for 3-year and 5-year CSS in the nomogram were larger than American Joint Committee on Cancer and Surveillance, Epidemiology, and End Results summary stage system. Moreover, decision curve analysis indicated the clinical utility of the nomogram. The nomogram demonstrated favorable predictive accuracy of survival in colorectal signet ring cell carcinoma patients after surgery, which should be further confirmed before clinical implementation.

Development and validation of a nomogram to predict overall survival for cervical adenocarcinoma: A population-based study

This study aimed to develop and validate a nomogram for predicting the overall survival of cervical adenocarcinoma (CAC) patients using a large database comprising patients with different ethnicities. We enrolled primary CAC cases with complete clinicopathological and survival data from the Surveillance, Epidemiology, and End Results program during 2004 to 2015. For training set samples, this work applied the Cox regression model to obtain factors independently associated with patient prognosis, which could be incorporated in constructing the nomogram. Altogether 3096 qualified cases were enrolled, their survival ranged from 0 to 155 (median, 45.5) months. As revealed by multivariate regression, age, marital status, tumor size, grade, International Federation of Gynecology and Obstetrics (FIGO) classification, pelvic lymph node metastasis, surgery, and chemotherapy served as the factors to independently predict CAC (all P < .05). We later incorporated these factors for constructing the nomogram. According to the concordance index determined, this nomogram had superior discrimination over FIGO classification system (all P < .001). Based on calibration plot, the predicted value was consistent with actual measurement. As revealed by time-independent area under the curves, our constructed nomogram had superior 5-year overall survival over FIGO system. Additionally, according to decision curve analysis, our constructed nomogram showed high clinical usefulness as well as favorable discrimination. Our constructed nomogram attains favorable performances, indicating that it may be applied in predicting survival for CAC patients.

E-AHPBA-ESSO-ESSR Innsbruck consensus guidelines for preoperative liver function assessment before hepatectomy

BACKGROUND

Posthepatectomy liver failure (PHLF) contributes significantly to morbidity and mortality after liver surgery. Standardized assessment of preoperative liver function is crucial to identify patients at risk. These European consensus guidelines provide guidance for preoperative patient assessment.

METHODS

A modified Delphi approach was used to achieve consensus. The expert panel consisted of hepatobiliary surgeons, radiologists, nuclear medicine specialists, and hepatologists. The guideline process was supervised by a methodologist and reviewed by a patient representative. A systematic literature search was performed in PubMed/MEDLINE, the Cochrane library, and the WHO International Clinical Trials Registry. Evidence assessment and statement development followed Scottish Intercollegiate Guidelines Network methodology.

RESULTS

Based on 271 publications covering 4 key areas, 21 statements (at least 85 per cent agreement) were produced (median level of evidence 2- to 2+). Only a few systematic reviews (2++) and one RCT (1+) were identified. Preoperative liver function assessment should be considered before complex resections, and in patients with suspected or known underlying liver disease, or chemotherapy-associated or drug-induced liver injury. Clinical assessment and blood-based scores reflecting liver function or portal hypertension (for example albumin/bilirubin, platelet count) aid in identifying risk of PHLF. Volumetry of the future liver remnant represents the foundation for assessment, and can be combined with indocyanine green clearance or LiMAx® according to local expertise and availability. Functional MRI and liver scintigraphy are alternatives, combining FLR volume and function in one examination.

CONCLUSION

These guidelines reflect established methods to assess preoperative liver function and PHLF risk, and have uncovered evidence gaps of interest for future research.

Different Approaches of Laparoscopic Mesohepatectomy for Centrally Located Hepatocellular Carcinomas

BACKGROUND

This study aimed to describe a simple and novel positional classification system for centrally located hepatocellular carcinoma (CL-HCC), based on which different surgical approaches for laparoscopic mesohepatectomy (LMH) were chosen.

MATERIALS AND METHODS

The data of patients with CL-HCC who underwent LMH between January 2017 and December 2021 were retrospectively analyzed. The positional classification method was used to locate tumors. In addition, different approaches were used during the surgery according to the classification type.

RESULTS

All 98 patients underwent LMH, of whom 4 were converted to open surgery. Types I, Ⅱ, and Ⅲ were 24, 37, and 37, respectively. Blood transfusions were performed in only 7 patients, of which the amount was 800 (600, 900) mL. All the patients underwent R0 resection. The complication rate was 9.2% in all cases. No deaths occurred.

CONCLUSIONS

Our proposed classification system and corresponding surgical approach in LMH is conducive to the successful completion of surgery.

Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image

OBJECTIVE

The indocyanine green retention rate at 15 min (ICG-R15) is a useful tool to evaluate the functional liver reserve before hepatectomy for liver cancer. Taking ICG-R15 as criteria, we investigated the ability of a machine learning (ML)-based radiomics model produced by Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT) image in evaluating functional liver reserve of hepatocellular carcinoma (HCC) patients.

METHODS

A total of 190 HCC patients with CT, among whom 112 also with MR, were retrospectively enrolled and randomly classified into a training dataset (CT: n = 133, MR: n = 78) and a test dataset (CT: n = 57, MR: n = 34). Then, radiomics features from Gd-EOB-DTPA MRI and CT images were extracted. The features associated with the ICG-R15 classification were selected. Five ML classifiers were used for the ML-model investigation. The accuracy (ACC) and the area under curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) were utilized for ML-model performance evaluation.

RESULTS

A total of 107 different radiomics features were extracted from MRI and CT, respectively. The features related to ICG-R15 which was classified into 10%, 20% and 30% were selected. In MRI groups, classifier XGBoost performed best with its AUC = 0.917 and ACC = 0.882 when the threshold was set as ICG-R15 = 10%. When ICG-R15 = 20%, classifier Random Forest performed best with AUC = 0.979 and ACC = 0.882. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.961 and ACC = 0.941. For CT groups, the classifier XGBoost performed best when ICG-R15 = 10% with AUC = 0.822 and ACC = 0.842. When ICG-R15 = 20%, classifier SVM performed best with AUC = 0.860 and ACC = 0.842. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.938 and ACC = 0.965.

CONCLUSIONS

Both the MRI- and CT-based machine learning models are proved to be valuable noninvasive methods for functional liver reserve evaluation.

Adjuvant transarterial chemoembolization timing after radical resection is an independent prognostic factor for patients with hepatocellular carcinoma

Background

It has been reported that postoperative adjuvant TACE (PA-TACE) treatment decreases recurrence and significantly improves the survival of patients who undergo radical resection of hepatocellular carcinoma (HCC) with high-risk recurrence factors. However, when to perform PA-TACE has not been fully studied.

Methods

We retrospectively collected the clinicopathologic characteristics of the patients with HCC between October 2013 and June 2020. The optimal cutoff value for PA-TACE time was determined based on the R package “maxstat”. Logistic regression and Cox regression analysis were used to determine the effect of the choice of PA-TACE timing on prognosis.

Results

The analysis was performed on 789 patients with HCC, and 484 patients were finally involved and were divided into training cohort (378) and validation cohort (106). The PA-TACE timing was found to be associated with survival outcomes. Multivariate logistic analysis found independent predictors of the PA-TACE timing, including gender and history of HBV. Multivariate Cox analysis showed that Ki-67, tumor size, MVI and the PA-TACE timing were independent prognostic factors for RFS in HCC patients.

Conclusions

Based on this study, HCC patients with high-risk recurrence factors can receive personalized assistance in undergoing PA-TACE treatment and improve their survival outcomes.

A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma

Background

Post-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram to preoperatively predict grade B/C PHLF defined by the International Study Group on Liver Surgery Grading (ISGLS) in HCC patients undergoing major hepatectomy.

Study Design

The consecutive HCC patients who underwent major hepatectomy at the Eastern Hepatobiliary Surgery Hospital between 2008 and 2013 served as a training cohort to develop a preoperative nomogram, and patients from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify preoperative predictors of grade B/C PHLF. Multivariable logistic regression was utilized to establish a nomogram model. Internal and external validations were used to verify the performance of the nomogram. The accuracy of the nomogram was also compared with the conventional scoring models, including MELD and ALBI score.

Results

A total of 880 patients who underwent major hepatectomy (668 in the training cohort and 192 in the validation cohort) were enrolled in this study. The independent risk factors of grade B/C PHLF were age, gender, prothrombin time, total bilirubin, and CSPH, which were incorporated into the nomogram. Good prediction discrimination was achieved in the training (AUROC: 0.73) and validation (AUROC: 0.72) cohorts. The calibration curve also showed good agreement in both training and validation cohorts. The nomogram has a better performance than MELD and ALBI score models.

Conclusion

The proposed nomogram showed more accurate ability to individually predict grade B/C PHLF after major hepatectomy in HCC patients than MELD and ALBI scores.

External Validation of Six Liver Functional Reserve Models to predict Posthepatectomy Liver Failure after Major Resection for Hepatocellular Carcinoma

Objective: To validate and compare the predictive ability of albumin-bilirubin model (ALBI) with other 5 liver functional reserve models (APRI, FIB4, MELD, PALBI, King's score) for posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) who underwent major hepatectomy. Methods: Data of patients undergoing major hepatectomy for HCC from 4 hospitals between January 01, 2008 and December 31, 2019 were retrospectively analyzed. PHLF was evaluated according to the definition of the 50-50 criteria. Performances of six liver functional reserve models were determined by the area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analysis. Results: A total of 745 patients with 103 (13.8%) experienced PHLF were finally included in this study. Among six liver functional reserve models, ALBI showed the highest AUC (0.64, 95% CI: 0.58-0.69) for PHLF. All models showed good calibration and greater net benefit than treating all patients at a limit range of threshold probabilities, but the ALBI demonstrated net benefit across the largest range of threshold probabilities. Subgroup analysis also showed ALBI had good predictive performance in cirrhotic (AUC=0.63) or non-cirrhotic (AUC=0.62) patients. Conclusion: Among the six models, the ALBI model shows more accurate predictive ability for PHLF in HCC patients undergoing major hepatectomy, regardless of having cirrhosis or not.