Prediction of Early Recurrence After R0 Resection for Gallbladder Carcinoma of Stage T1b-T3

Optimizing Outcomes in Gallbladder Cancer: Identifying Predictors of Futile Up-Front Surgery in a Global Multi-center Study

BACKGROUND

Gallbladder cancer (GBC) has a poor prognosis, particularly in advanced stages, with surgery often offering limited survival benefit. This study aimed to identify risk factors for futile surgery (FS), defined as procedures followed by early recurrence or death.

METHODS

An international cohort of 788 patients who underwent up-front GBC surgery across 18 centers was analyzed. Futility was defined as recurrence within 5 months or death within 90 days after oncological surgery. A multivariate model was built, and an online calculator was developed to predict the probability of FS.

RESULTS

A total of 107 patients (13.6%) experienced FS, with a median survival of only 6.8 months, compared with 57.4 months for nonfutile cases. The key risk factors identified were the T3-T4 tumor stage (odds ratio [OR] 2.20; 95% confidence interval [CI] 1.30-3.71), lymph node involvement (OR 1.91; 95% CI 1.22-2.98), and multivisceral resection (OR 2.25; 95% CI 1.28-3.94). Incidental GBC diagnoses showed a lower risk of FS (OR 0.41; 95% CI 0.25-0.67). The predictive model had a strong discriminative ability (c-statistic: 0.749). Decision curve analysis demonstrated the superiority of the multivariate model over individual predictors.

CONCLUSIONS

Refining patient selection can reduce futile surgeries in GBC. The predictive model provides a valuable online tool ( https://aicep.website/?cff-form=25 ) to improve decision-making and outcomes by minimizing unnecessary interventions.

Conundrum of station 13 lymph nodes in Gallbladder Carcinoma: Retrospective tryst with a forgotten entity

Gallbladder cancers are one of the lethal cancers with dismal prognosis. There is discrepancy between various biliary societies regarding extent of regional lymph nodes of gallbladder. Some consider station 13 as regional nodes, whereas others consider it as distant metastatic node. This is a retrospective analysis of disease-free survival of stage IIIB and IVB-M0 patients in gall bladder cancer with positive station 13 lymph node. Electronic medical records of patients were used to retrieve the data. The mean and median disease-free survival of gallbladder carcinoma patients with station 13 lymph-node positivity is 16.2 months (95% CI 11.57-20.85 months) and 12 months (95% CI 9.57-14.43 months), respectively. Disease-free survival of Stage IIIB and IVB-M0 gallbladder carcinoma patients with station 13 lymph-node positivity is better than overall survival of stage IVB-M1 patients and supports the inclusion of station 13 lymph node as regional lymph-nodal basin of gall bladder carcinoma. This study paves the way for a large prospective study to consider station 13 lymph node as regional lymph node in gallbladder cancer.

Recurrence patterns and prediction of survival after recurrence for gallbladder cancer

BACKGROUND

Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict survival after recurrence (SAR) of GBC.

METHODS

Patients who underwent curative-intent resection of GBC between 1999 and 2022 were identified using an international database. An Extreme Gradient Boosting ML model to predict SAR was developed and validated.

RESULTS

Among 348 patients, 110 (31.6%) developed disease recurrence during follow-up. The most common recurrence site was local (29.1%), followed by multiple site (26.4%), liver (21.8%), peritoneal (18.2%), and lung (0.05%). The median SAR was the longest in patients with lung recurrence (36.0 months), followed by those with local recurrence (15.7 months). In contrast, patients with peritoneal (8.9 months), liver (8.5 months), or multiple-site (6.4 months) recurrence had a considerably shorter SAR. Patients with multiple-site recurrence had a worse SAR than individuals with single-site recurrence (6.4 vs 11.10 months, respectively; P =.014). The model demonstrated good performance in the evaluation and bootstrapping cohorts (area under the receiver operating characteristic curve: 71.4 and 71.0, respectively). The most influential variables were American Society of Anesthesiologists classification, local recurrence, receipt of adjuvant chemotherapy, American Joint Committee on Cancer T and N categories, and developing early disease recurrence (<12 months). To enable clinical applicability, an easy-to-use calculator was made available (https://catalano-giovanni.shinyapps.io/SARGB).

CONCLUSION

Except for lung recurrence, SAR for GBC was poor. A subset of patients with less aggressive disease biology may have favorable SAR. ML-based SAR prediction may help individuate candidates for curative re-resection when feasible.

Prognostic evaluation in gallbladder carcinoma: Introducing a composite risk model integrating nutritional and immune markers

The importance of evaluating the nutritional status and immune condition prior to surgery has gained significant attention in predicting the prognosis of cancer patients in recent years. The objective of this study is to establish a risk model for predicting the prognosis of gallbladder carcinoma (GBC) patients. Data from GBC patients who underwent radical resection at West China Hospital of Sichuan University (China) from 2014 to 2021 were retrospectively collected. A novel risk model was created by incorporating the prognostic nutritional index and glucose-to-lymphocyte ratio, and each patient was assigned a risk score. The patients were then divided into low- and high-risk cohorts, and comparisons were made between the two groups in terms of clinicopathological features and prognosis. Propensity score matching was conducted to reduce potential bias. A total of 300 GBC patients receiving radical surgery were identified and included in this study. Patients in the high-risk group were older, had higher levels of serum carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), and cancer antigen 19-9 (CA19-9), were more likely to experience postoperative complications, and had more aggressive tumor characteristics, such as poor differentiation, lymph node metastasis, and advanced tumor stage. They also had lower overall survival (OS) rates (5-year OS rate: 11.2% vs. 37.4%) and disease-free survival (DFS) rates (5-year DFS rate: 5.1% vs. 18.2%). After propensity score matching, the high-risk population still experienced poorer prognosis (5-year OS rate: 12.7% vs 20.5%; 5-year DFS rate: 3.2% vs 8.2%). The risk model combining prognostic nutritional index and glucose-to-lymphocyte ratio can serve as a standalone predictor for the prognosis and assist in optimizing the treatment approach for GBC patients.

Machine learning prediction of early recurrence after surgery for gallbladder cancer

BACKGROUND

Gallbladder cancer is often associated with poor prognosis, especially when patients experience early recurrence after surgery. Machine learning may improve prediction accuracy by analysing complex non-linear relationships. The aim of this study was to develop and evaluate a machine learning model to predict early recurrence risk after resection of gallbladder cancer.

METHODS

In this cross-sectional study, patients who underwent resection of gallbladder cancer with curative intent between 2001 and 2022 were identified using an international database. Patients were assigned randomly to a development and an evaluation cohort. Four machine learning models were trained to predict early recurrence (within 12 months) and compared using the area under the receiver operating curve (AUC).

RESULTS

Among 374 patients, 56 (15.0%) experienced early recurrence; most patients had T1 (51, 13.6%) or T2 (180, 48.1%) disease, and a subset had lymph node metastasis (120, 32.1%). In multivariable Cox analysis, resection margins (HR 2.34, 95% c.i. 1.55 to 3.80; P < 0.001), and greater AJCC T (HR 2.14, 1.41 to 3.25; P < 0.001) and N (HR 1.59, 1.05 to 2.42; P = 0.029) categories were independent predictors of early recurrence. The random forest model demonstrated the highest discrimination in the evaluation cohort (AUC 76.4, 95% c.i. 66.3 to 86.5), compared with XGBoost (AUC 74.4, 53.4 to 85.3), support vector machine (AUC 67.2, 54.4 to 80.0), and logistic regression (AUC 73.1, 60.6 to 85.7), as well as good accuracy after bootstrapping validation (AUC 75.3, 75.0 to 75.6). Patients classified as being at high versus low risk of early recurrence had much worse overall survival (36.1 versus 63.8% respectively; P < 0.001). An easy-to-use calculator was made available (https://catalano-giovanni.shinyapps.io/GallbladderER).

CONCLUSION

Machine learning-based prediction of early recurrence after resection of gallbladder cancer may help stratify patients, as well as help inform postoperative adjuvant therapy and surveillance strategies.

Unraveling early recurrence of risk factors in Gallbladder cancer: A systematic review and meta-analysis

BACKGROUND

Gallbladder cancer (GBC) is the most prevalent biliary tract tumor characterized by a high incidence of recurrence, even after curative-intent surgery. The object of this systematic review and meta-analysis was to investigate the risk factors related to early recurrence (ER).

METHODS

A systematic literature review was conducted in PubMed, Embase, Cochrane Library, and Web of Science to identify published articles up to February 2024. Data on risk factors associated with ER reported by two or more studies were collected. Selection of different effect models based on data heterogeneity.

RESULTS

Out of 6497 initially identified articles based on our search strategies, only 5 were eligible and included in this meta-analysis and 12 ER-related factors were collected. The overall recurrence rate was reported between 32.3% and 61.0 %, and the ER rate ranged from 19.6% to 26.5 %. Concentrations of CA19-9 (OR 3.03 95 % CI 2.20-4.17) and CEA (OR 1.85 95 % CI 1.24-2.77), tumor differentiation (OR 2.79, 95 % CI 1.86-4.20), AJCC T stage (OR 7.64, 95%CI 3.40-17.18), lymphovascular invasion (OR 2.71, 95 % CI 1.83-4.03), perineural invasion (OR 2.71, 95 % CI 1.79-4.12), liver involvement (OR 5.69, 95%CI 3.78-8.56) and adjuvant therapy (OR 2.19, 95 % CI 1.06-4.55) were identified as the risk factors of ER.

CONCLUSION

This study may provide valuable insights for early identification of increased ER risk and making informed decisions regarding the comprehensive diagnosis and treatment of patients with GBC. To draw more definitive conclusions, there is a need for high-quality prospective studies involving multiple centers and diverse racial populations.

Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Objectives

It is significant to develop effective prognostic strategies and techniques for improving the survival rate of gallbladder carcinoma (GBC). We aim to develop the prediction model from multi-clinical indicators combined artificial intelligence (AI) algorithm for the prognosis of GBC.

Methods

A total of 122 patients with GBC from January 2015 to December 2019 were collected in this study. Based on the analysis of correlation, relative risk, receiver operator characteristic curve, and importance by AI algorithm analysis between clinical factors and recurrence and survival, the two multi-index classifiers (MIC1 and MIC2) were obtained. The two classifiers combined eight AI algorithms to model the recurrence and survival. The two models with the highest area under the curve (AUC) were selected to test the performance of prognosis prediction in the testing dataset.

Results

The MIC1 has ten indicators, and the MIC2 has nine indicators. The combination of the MIC1 classifier and the "avNNet" model can predict recurrence with an AUC of 0.944. The MIC2 classifier and "glmet" model combination can predict survival with an AUC of 0.882. The Kaplan-Meier analysis shows that MIC1 and MIC2 indicators can effectively predict the median survival of DFS and OS, and there is no statistically significant difference in the prediction results of the indicators (MIC1: χ² = 6.849, P = 0.653; MIC2: χ² = 9.14, P = 0.519).

Conclusions

The MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.

Laparoscopic segment 4b+5 liver resection for stage T3 gallbladder cancer

BACKGROUND

There is still controversy over whether to perform laparoscopic surgery for T3 stage gallbladder cancer. In addition, the necessity of segment 4b+5 liver resection for stage T3 gallbladder has not been reported. This article aims to explore the safety, effectiveness, and short-term prognosis of laparoscopic segment 4b+5 liver resection for T3 stage gallbladder cancer.

METHODS

This is a retrospective multicenter propensity score-matched study. Disease-free survival, perioperative complications, and intraoperative safety were analyzed to evaluate safety and effectiveness.

RESULTS

There was no significant difference in the incidence of intraoperative bleeding, number of lymph nodes obtained, postoperative complications, or disease-free survival (DFS) between the open group (OG) and laparoscopic group (LG) (P > 0.05). The DFS time of the S4b+5 resection group (S4b5) was longer than that of the wedge group (P = 0.016). Cox regression showed that positive margins (HR, 5.32; 95% CI 1.03-27.63; P = 0.047), lymph node metastasis (HR, 2.70; 95% CI 1.31-5.53; P = 0.007), and liver S4b+5 resection (HR, 0.30; 95% CI 0.14-0.66; P = 0.003) were independent risk factors for DFS. The operative time of indocyanine green (ICG) fluorescence-guided liver S4b5 segment resection was shorter than that of traditional laparoscopic S4b+5 resection guided by hepatic veins (P ≤ 0.001).

CONCLUSION

Laparoscopic liver S4b+5 resection for T3 stage gallbladder cancer is safe and feasible and can prolong DFS. ICG fluorescence-guided negative staining may reduce the difficulty of the operation.