Risk prediction model for major complication after hepatectomy for malignant tumour - A validated scoring system from a university center

A virtual biopsy of liver parenchyma to predict the outcome of liver resection

The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females-median age 67 years-39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR.

Institutional Utilization of Postoperative Mortality Predicted by a Nationwide Survey-Based Risk Calculator in Patients Who Underwent Major Hepatectomy

Background

Relationship between outcomes of major hepatectomy and the mortality rate predicted by National Clinical Database Risk Calculator (NCD-RC) was examined.

Methods

Patient demographics and postoperative morbidity and mortality were compared between 30-day and in-hospital mortality rates among 55 patients who underwent major hepatectomies. The cutoff value for high-risk mortality was set at 5%. Patients were divided into 4 groups: (1) no severe complications and low predictive mortality rate (woML), (2) severe complications or mortality, and low mortality rate (wML), (3) no severe complications and high mortality rate (woMH), and (4) severe complications or mortality, and high mortality rate (wMH).

Results

Morbidity higher than Clavien Dindo III occurred in 17 patients (28%) and 30-day and in-hospital mortality in none and 2 (3%), respectively. The in-hospital mortality rate was significantly higher for male patients (P < 0.01). Age, elderly patients, diseases, and comorbidity did not significantly differ among groups. Although bile leakage was common in group wML, there were no in-hospital deaths. All surgical procedures performed in group wMH were right hepatectomy with bile duct resection (RH-BDR) for biliary malignancy, and 2 died of hepatic failure; however, the incidence of RH-BDR was not significantly higher than those in other groups.

Conclusions

Preoperative mortality rate predicted by NCD-RC was not always consistent with outcomes in actual clinical settings and further improvements are needed. In case of RH-BDR for biliary malignancy with high predictive mortality rate, careful decision making for liver function and perioperative management are required.

Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI).

Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI).

Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955–0.962) and 0.844 (95% CI: 0.833–0.886), respectively], compared with the radiomics model and the clinical model.

Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

Major postoperative complications compromise oncological outcomes of patients with intrahepatic cholangiocarcinoma after curative resection - A 13-year cohort in a tertiary center

BACKGROUND/OBJECTIVE

Hepatectomy is the mainstay of curative treatment for intrahepatic cholangiocarcinoma (ICC). The relationship between postoperative complication and oncological outcome has not been defined. We aimed to elucidate the effect of postoperative complication on long-term survival of ICC patients after curative resection.

METHODS

Data of consecutive patients who had curative resection for ICC at our hospital from 1991 to 2013 were reviewed. Patients with cholangiohepatocellular carcinoma, metastatic adenocarcinoma or Klaskin tumor were excluded. Clinicopathological data and postoperative events were extracted from database for survival analysis.

RESULTS

There were 107 patients in our series. Their median age was 61 years. The median follow-up time was 24 months. The median tumor size was 6 cm. Major hepatectomy was required in 52.3% of them. The median operation time and blood loss was 439 minutes and 0.9L respectively. R0 resection was achieved in 88.8% of them. The median length of stay was 11 days. The 30-day and 90-day mortality was 2.5% and 6.8% respectively. Major complications were found in 20.6% of them. Patients with postoperative complications had significantly inferior survival than patients without (3-yr DFS 38% vs. 27%, P = 0.001; 3-yr overall: 51% vs. 27%, P < 0.001). Multivariable analysis showed that postoperative complication was an independent factor associated with disease-free survival (OR 1.9 95% C.I. 1.10-3.24, P = 0.021) and overall survival (OR 2.1, 95% C.I. 1.13-3.93, P = 0.018).

CONCLUSION

Postoperative complication has a significant impact on ICC patients' long-term survival. Extra measures such as adjuvant chemotherapy should be considered for patients who develop major complications after surgery.

How to conduct a high-quality original study on a prognostic research topic

This is the third article of our educational series, which focuses on how to conduct a high-quality original study on a prognostic research topic from a methodological perspective. We introduce four aspects: clarifying the objectives; generating an appropriate research question; planning the study; and reporting and analyzing data. This paper has several highlights. (1) There are four types of prognostic studies: Type I-fundamental prognostic research, Type II-prognostic factors research, Type III-prediction model research, and Type IV-stratified medicine research. (2) We present the defining characteristics for each type of prognostic study. (3) For Types I-III, we suggest that "PFOT″ components (target Population, prognostic or predictive Factor[s] or a predictive model with a combination of multiple Factors, Outcome, and follow-up Time) should be included in the research questions; for Type IV, "PIFOT″ components (Intervention was added to PFOT) should be included in the research questions. (4) As with other study designs, prognostic studies should be registered to help mitigate duplication of effort across study teams and to accelerate the pace of scientific evolution. (5) Sample size calculations are an important step for prognostic studies. (6) Confounders and missing data issues should be considered carefully during study planning, reporting, and analyzing data. (7) For Type III studies, at least an internal validation should be performed, and univariable analysis to select significant variables (e.g., p-value < 0.05) for a multivariable model is not recommended. (8) A test for interaction is a necessary step for Type IV prognostic studies. A high-quality prognostic study would benefit from clinicians, methodologists, and statisticians working together.