Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma

Predictive model of positive surgical margins after radical prostatectomy based on Bayesian network analysis

Objective

This study aimed to analyze the independent risk factors for marginal positivity after radical prostatectomy and to evaluate the clinical value of the predictive model based on Bayesian network analysis.

Methods

We retrospectively analyzed the clinical data from 238 patients who had undergone radical prostatectomy, between June 2018 and May 2022. The general clinical data, prostate specific antigen (PSA)-derived indicators, puncture factors, and magnetic resonance imaging (MRI) characteristics were included as predictive variables, and univariate and multivariate analyses were conducted. We established a nomogram model based on the independent predictors and adopted BayesiaLab software to generate tree-augmented naive (TAN) and naive Bayesian models based on 15 predictor variables.

Results

Of the 238 patients included in the study, 103 exhibited positive surgical margins. Univariate analysis revealed that PSA density (PSAD) (P = 0.02), Gleason scores for biopsied tissue (P = 0.002) and the ratio of positive biopsy cores (P < 0.001), preoperative T staging (P < 0.001), and location of abnormal signals (P = 0.002) and the side of the abnormal signal (P = 0.009) were all statistically significant. The area under curve (AUC) of the established nomogram model based on independent predictors was 73.80%, the AUC of the naive Bayesian model based on 15 predictors was 82.71%, and the AUC of the TAN Bayesian model was 80.80%.

Conclusion

The predictive model of positive resection margin after radical prostatectomy based on Bayesian network demonstrated high accuracy and usefulness.

Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study

Gallbladder (GB) disease is a common pathology that needs correct and early diagnosis for the optimum medical treatment. Early diagnosis is crucial as any delay or misdiagnosis can worsen the patient situation. Incorrect diagnosis could also lead to an escalation in patient symptoms and poorer clinical outcomes. The use of Artificial Intelligence (AI) techniques, ranging from Machine Learning (ML) to Deep Learning (DL) to predict disease progression, identify abnormalities, and estimate mortality rates associated with GB disorders has increased over the past decade. To this end, this paper provides a comprehensive overview of the AI approaches used in the diagnosis of GB illnesses. This review compiles and compares relevant papers from the last decade to show how AI might enhance diagnostic precision, speed, and efficiency. Therefore, this survey gives researchers the opportunity to find out both the diagnosis of GB diseases and AI techniques in one place. The maximum accuracy rate by ML was when using SVM with 96.67%, whilst the maximum accuracy rate by DL was by utilising a unique structure of VGG, GoogleNet, ResNet, AlexNet and Inception with 98.77%. This could provide a clear path for further investigations and algorithm’s development to boost diagnostic results to improve the patient’s condition and choose the appropriate treatment.

New trends in diagnosis and management of gallbladder carcinoma

Gallbladder (GB) carcinoma, although relatively rare, is the most common biliary tree cholangiocarcinoma with aggressiveness and poor prognosis. It is closely associated with cholelithiasis and long-standing large (> 3 cm) gallstones in up to 90% of cases. The other main predisposing factors for GB carcinoma include molecular factors such as mutated genes, GB wall calcification (porcelain) or mainly mucosal microcalcifications, and GB polyps ≥ 1 cm in size. Diagnosis is made by ultrasound, computed tomography (CT), and, more precisely, magnetic resonance imaging (MRI). Preoperative staging is of great importance in decision-making regarding therapeutic management. Preoperative staging is based on MRI findings, the leading technique for liver metastasis imaging, enhanced three-phase CT angiography, or magnetic resonance angiography for major vessel assessment. It is also necessary to use positron emission tomography (PET)-CT or 18F-FDG PET-MRI to more accurately detect metastases and any other occult deposits with active metabolic uptake. Staging laparoscopy may detect dissemination not otherwise found in 20%-28.6% of cases. Multimodality treatment is needed, including surgical resection, targeted therapy by biological agents according to molecular testing gene mapping, chemotherapy, radiation therapy, and immunotherapy. It is of great importance to understand the updated guidelines and current treatment options. The extent of surgical intervention depends on the disease stage, ranging from simple cholecystectomy (T1a) to extended resections and including extended cholecystectomy (T1b), with wide lymph node resection in every case or IV-V segmentectomy (T2), hepatic trisegmentectomy or major hepatectomy accompanied by hepaticojejunostomy Roux-Y, and adjacent organ resection if necessary (T3). Laparoscopic or robotic surgery shows fewer postoperative complications and equivalent oncological outcomes when compared to open surgery, but much attention must be paid to avoiding injuries. In addition to surgery, novel targeted treatment along with immunotherapy and recent improvements in radiotherapy and chemotherapy (neoadjuvant-adjuvant capecitabine, cisplatin, gemcitabine) have yielded promising results even in inoperable cases calling for palliation (T4). Thus, individualized treatment must be applied.

Prediction model of gleason score upgrading after radical prostatectomy based on a bayesian network

OBJECTIVE

To explore the clinical value of the Gleason score upgrading (GSU) prediction model after radical prostatectomy (RP) based on a Bayesian network.

METHODS

The data of 356 patients who underwent prostate biopsy and RP in our hospital from January 2018 to May 2021 were retrospectively analysed. Fourteen risk factors, including age, body mass index (BMI), total prostate-specific antigen (tPSA), prostate volume, total prostate-specific antigen density (PSAD), the number and proportion of positive biopsy cores, PI-RADS score, clinical stage and postoperative pathological characteristics, were included in the analysis. Data were used to establish a prediction model for Gleason score elevation based on the tree augmented naive (TAN) Bayesian algorithm. Moreover, the Bayesia Lab validation function was used to calculate the importance of polymorphic Birnbaum according to the results of the posterior analysis and to obtain the importance of each risk factor.

RESULTS

In the overall cohort, 110 patients (30.89%) had GSU. Based on all of the risk factors that were included in this study, the AUC of the model was 81.06%, and the accuracy was 76.64%. The importance ranking results showed that lymphatic metastasis, the number of positive biopsy cores, ISUP stage and PI-RADS score were the top four influencing factors for GSU after RP.

CONCLUSIONS

The prediction model of GSU after RP based on a Bayesian network has high accuracy and can more accurately evaluate the Gleason score of prostate biopsy specimens and guide treatment decisions.

Comparison of clinicopathological characteristics and survival outcomes between gallbladder mucinous adenocarcinoma and gallbladder adenocarcinoma: A propensity score-matched study

BACKGROUND

Gallbladder mucinous adenocarcinoma (GBMAC) is a rare subtype of gallbladder adenocarcinoma (GBAC), with limited knowledge of its survival outcomes from small case series and single-center retrospective analysis.

AIM

To compare the clinicopathological characteristics of GBMAC with typical GBAC and its prognostic factors to gain insights into this field.

METHODS

This study was conducted using data from the Surveillance, Epidemiology, and End Results database, including cases of GBMAC and typical GBAC diagnosed from 2010 to 2017. The Pearson chi-square test or Fisher exact test was used to examine the differences in clinicopathological features between these two cohorts. In addition, propensity score matching (PSM) analysis was performed to balance the selection biases. Univariate and multivariate Cox hazards regression analyses were performed to determine independent prognostic factors for cancer-specific survival (CSS) and overall survival (OS). The Kaplan-Meier curves and log-rank tests were used to assess the OS and CSS of GBMAC and typical GBAC patients.

RESULTS

The clinicopathological and demographic characteristics of GBMAC were different from typical GBAC. They included a larger proportion of patients with unmarried status, advanced American Joint Committee on Cancer (AJCC) stage, higher T stage, higher N1 stage rate and lower N0 and N2 stage rates (P < 0.05). Multivariate analyses demonstrated that surgery [OS: Hazard ratio (HR) = 2.27, P = 0.0037; CSS: HR = 2.05, P = 0.0151], chemotherapy (OS: HR = 6.41, P < 0.001; CSS: HR = 5.24, P < 0.001) and advanced AJCC stage (OS: Stage IV: HR = 28.99, P = 0.0046; CSS: Stage III: HR = 12.31, P = 0.015; stage IV: HR = 32.69, P = 0.0015) were independent prognostic indicators for OS and CSS of GBMAC patients. Furthermore, after PSM analysis, there was no significant difference between GBMAC and matched typical GBAC patients regarding OS (P = 0.82) and CSS (P = 0.69).

CONCLUSION

The biological behaviors of GBMAC are aggressive and significantly different from that of typical GBAC. However, they show similar survival prognoses. Surgery, chemotherapy, and lower AJCC stage were associated with better survival outcomes. Further research is needed in the future to verify these results.

A Bayesian network prediction model for gallbladder polyps with malignant potential based on preoperative ultrasound

BACKGROUND

It is important to identify gallbladder polyps (GPs) with malignant potential and avoid unnecessary cholecystectomy by constructing prediction model. The aim of the study is to develop a Bayesian network (BN) prediction model for GPs with malignant potential in a long diameter of 8-15 mm based on preoperative ultrasound.

METHODS

The independent risk factors for GPs with malignant potential were screened by χ² test and Logistic regression model. Prediction model was established and validated using data from 1296 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China. A BN model was established based on the independent risk variables.

RESULTS

Independent risk factors for GPs with malignant potential included age, number of polyps, polyp size (long diameter), polyp size (short diameter), and fundus. The BN prediction model identified relationships between polyp size (long diameter) and three other variables [polyp size (short diameter), fundus and number of polyps]. Each variable was assigned scores under different status and the probabilities of GPs with malignant potential were classified as [0-0.2), [0.2-0.5), [0.5-0.8) and [0.8-1] according to the total points of [- 337, - 234], [- 197, - 145], [- 123, - 108], and [- 62,500], respectively. The AUC was 77.38% and 75.13%, and the model accuracy was 75.58% and 80.47% for the BN model in the training set and testing set, respectively.

CONCLUSION

A BN prediction model was accurate and practical for predicting GPs with malignant potential patients in a long diameter of 8-15 mm undergoing cholecystectomy based on preoperative ultrasound.

Survival prediction by Bayesian network modeling for pseudomyxoma peritonei after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy

OBJECTIVES

To establish a survival prognostic model for pseudomyxoma peritonei (PMP) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) based on Bayesian network (BN).

METHODS

453 PMP patients were included from the database at our center. The dataset was divided into a training set to establish BN model and a testing set to perform internal validation at a ratio of 8:2. From the training set, univariate and multivariate analyses were performed to identify independent prognostic factors for BN model construction. The confusion matrix, receiver operating characteristic (ROC) curve and the area under curve (AUC) were used to evaluate the performance of the BN model.

RESULTS

The univariate and multivariate analyses identified 7 independent prognostic factors: gender, previous operation history, histological grading, lymphatic metastasis, peritoneal cancer index, completeness of cytoreduction and splenectomy (all p < 0.05). Based on independent factors, the BN model of training set was established. After internal validation, the accuracy and AUC of the BN model were 70.3% and 73.5%, respectively.

CONCLUSION

The BN model provides a reasonable level of predictive performance for PMP patients undergoing CRS + HIPEC.

Trends in Primary Gallbladder Cancer Incidence and Incidence-based Mortality in the United States, 1973 to 2015

OBJECTIVES

Primary gallbladder cancer (GBC) is the most common biliary tract cancer with poor survival despite aggressive treatment. This study aimed to investigate the trends of GBC incidence and incidence-based mortality (IBM) over the last 4 decades.

MATERIALS AND METHODS

GBC cases diagnosed between 1973 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence rates, IBM rates, and annual percent changes (APCs) were calculated and stratified according to population and tumor characteristics.

RESULTS

The cohort consisted of 10,792 predominantly white (81%) and female (71%) GBC patients. The overall GBC incidence decreased by 1.65% (95% confidence interval [CI]: 1.45% to 1.84%) per year since 1973, but has plateaued since 2002. IBM decreased by 1.69% (95% CI: 1.22% to 2.16%) per year from 1980 to 2015; the rate of decrease in IBM rates was lower during 1997 to 2015 (APC: -1.19%, 95% CI: -1.68% to -0.71%) compared with 1980 to 1997 (APC: -3.13%, 95% CI: -3.68% to -2.58%).

CONCLUSIONS

The incidence and IBM rates of GBC have been decreasing over the last 40 years, but the decrease plateaued over the last 2 decades. The effects of treatment modalities, including laparoscopic cholecystectomy, adjuvant chemotherapy, and radiation on the incidence and IBM of GBC need to be further investigated.

Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network

Background

The existing prognostic models of rectal cancer after radical resection ignored the relationships among prognostic factors and their mutual effects on prognosis. Thus, a new modeling method is required to remedy this defect. The present study aimed to construct a new prognostic prediction model based on the Bayesian network (BN), a machine learning tool for data mining, clinical decision-making, and prognostic prediction.

Methods

From January 2015 to December 2017, the clinical data of 705 patients with rectal cancer who underwent radical resection were analyzed. The entire cohort was divided into training and testing datasets. A new prognostic prediction model based on BN was constructed and compared with a nomogram.

Results

A univariate analysis showed that age, Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), Carbohydrate antigen 125 (CA125), preoperative chemotherapy, macropathology type, tumor size, differentiation status, T stage, N stage, vascular invasion, KRAS mutation, and postoperative chemotherapy were associated with overall survival (OS) of the training dataset. Based on the above-mentioned variables, a 3-year OS prognostic prediction BN model of the training dataset was constructed using the Tree Augmented Naïve Bayes method. In addition, age, CEA, CA19-9, CA125, differentiation status, T stage, N stage, KRAS mutation, and postoperative chemotherapy were identified as independent prognostic factors of the training dataset through multivariate Cox regression and were used to construct a nomogram. Then, based on the testing dataset, the two models were evaluated using the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) of ROC of the BN model and nomogram was 80.11 and 74.23%, respectively.

Conclusion

The present study established a BN model for prognostic prediction of rectal cancer for the first time, which was demonstrated to be more accurate than a nomogram.

Can patients with gallbladder adenocarcinoma and liver metastases obtain survival benefit from surgery? A population-based study

According to international guidelines, surgical treatment is not recommended for gallbladder adenocarcinoma with liver metastases (GCL), and research on the clinical implications of surgery in GCL is very scarce. In this study, we aimed to investigate whether surgery is an effective means to improve survival in GCL. Data were collected from the Surveillance, Epidemiology, and End Results database. A total of 612 people diagnosed with GCL were identified. According to their treatment, patients were categorised into 4 groups: synchronous resection of the primary tumour and liver metastases (SPL), synchronous primary site and other resection (SPO), single resection of the primary site (SPS), and no resection (NR). Our study findings showed that 34 (5.6%) patients received SPL, 18 (2.9%) had SPO, 185 (30.2%) underwent SPS, and 375 (61.3%) received NR. Our analysis showed that surgical treatment was an independent protective prognostic factor for gallbladder cancer cause-specific survival. Groups who underwent SPL, SPO, and SPS showed gradually decreasing survival benefit compared with the NR group (median survival: 9, 5, 4, and 2 months, respectively). Notably, mortality in the SPL, SPO, and SPS groups were significantly different compared with NR group, and the hazard ratio were gradually increased, which were 0.402 (95% confidence interval [CI] 0.264-0.611), 0.463 (95% CI 0.274-0.784), and 0.597 (95% CI 0.457-0.779), respectively (all P < 0.05). Survival in patients with GCL was significantly improved via surgery of the primary site with simultaneous metastasectomy. Among them, SPL and SPO showed greater survival advantages in carefully selected patients with GCL. Registered at researchregistery.com: Trial registration number is researchregistry6915.

Clinical efficacy of adjuvant treatments for patients with resected biliary tract cancer: a systematic review and network meta-analysis

OBJECTIVE

This study aimed to determine the benefits of adjuvant therapy in patients with resected biliary tract cancer (BTC) and identify the optimal adjuvant treatment scheme.

DESIGN

Systematic review and network meta-analysis.

DATA SOURCES

Studies comparing different adjuvant therapies in patients with BTC were searched in PubMed, Embase, CINAHL, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov databases from inception to December 2021. Additionally, the references were manually searched for the related literature.

MATERIALS AND METHODS

Eligible studies were identified, and data were extracted independently by two authors. A random-effects network meta-analysis was performed using R software. The pooled outcomes of overall survival (OS) and disease-free survival (DFS) were measured using the combined HRs with 95% CIs.

RESULTS

Nineteen eligible studies reporting three types of adjuvant therapies were included in our network meta-analysis. Adjuvant radiotherapy (ART, HR 0.62; 95% CI 0.42 to 0.93), adjuvant chemoradiotherapy (ACRT; HR 0.71; 95% CI 0.54 to 0.83) and adjuvant chemotherapy (ACT; HR 0.84; 95% CI 0.68 to 0.98) were more effective in prolonging OS than that of observation, with no significant difference between the three adjuvant therapies. Moreover, the improvement in DFS was also found in ACRT and ACT compared with that of observation (HR 0.60; 95% CI 0.45 to 0.75; HR 0.82; 95% CI 0.68 to 0.97, respectively). Furthermore, ACRT obtained a slightly better DFS benefit compared with that of ACT (HR 0.73; 95% CI 0.53 to 0.95).

CONCLUSIONS

Our primary results demonstrated that, compared with that of observation, ACRT and ACT after radical resection could provide better OS and DFS benefits in patients with BTC. However, ART only showed improvement in OS, but not in DFS. Due to the lack of head-to-head studies of ACT, ACRT and ART, the above results need to be further verified by prospective randomised controlled trials.

Optimal Lymph Node Staging System in Evaluating Prognosis of Gallbladder Carcinoma: A Multi-institutional Study

BACKGROUND

Lymph node (LN) involvement is a critical prognostic factor in patients with gallbladder carcinoma (GBC). Controversy exists regarding optimal categorization of nodal metastasis status, including anatomical location of positive nodes (AJCC 7th N staging), number of metastatic lymph nodes (NMLN), log odds of metastatic LNs (LODDS), and lymph node ratio (LNR).

METHODS

Patients who underwent curative-intent resection for GBC from six Chinese tertiary hospitals between 2008 and 2013 were analyzed retrospectively. The relative discriminative abilities of the different LN staging systems were assessed by different models including the tree-augmented naïve Bayesian (TAN) model, Cox proportional hazards regression model, and binary logistic regression model.

RESULTS

A total of 226 patients were involved in this cohort. Based on the TAN model and composite importance measures, the most important factor affecting the prognosis in the different LN staging systems was NMLN. Among the four TAN models which were built with 4 metastatic LN markers and baseline variables, the accuracy of the NMLN-based prognostic model was 88.15%, higher than 7th N staging (86.44%), LNR (87.34%), and LODDS (85.19%). The Cox model based on NMLN (C-index: 0.763, AIC: 1371.62) had a higher fitness than the others (7th N staging C-index: 0.756, AIC: 1375.51; LNR C-index: 0.759, AIC: 1378.82; LODDS C-index 0.748, AIC: 1390.99). The AUCs of different staging binary logistic regression models were NMLN (0.872), LNR (0.872), 7th N staging (0.869) and LODDS (0.856), respectively.

CONCLUSIONS

NMLN was the optimal LN staging system in evaluating prognosis of GBC.

Factors that Minimize Curative Resection for Gallbladder Adenocarcinoma: an Analysis of Clinical Decision-Making and Survival

BACKGROUND

Gallbladder adenocarcinoma has a poor prognosis as it is often diagnosed incidentally, and patients have a high risk for residual and occult metastatic disease. Expert guidelines recommend definitive surgery for ≥T1b tumors; however, surgical management is inconsistent. This study evaluates the factors that affect the completion of radical resection with portal lymphadenectomy and its impact on survival.

METHODS

A retrospective review of patients who underwent surgery for gallbladder cancer from 2008 to 2017 at an academic institution was performed. Patients were analyzed based on whether they underwent definitive surgical resection. Patient factors and clinical decision-making were analyzed; overall survival was compared using Kaplan-Meier analysis.

RESULTS

Seventy-five patients with ≥T1b tumors were identified, of who 32 (42.7%) underwent definitive resection. Fifty-four (72%) patients had gallbladder cancer identified as an incidental diagnosis following laparoscopic cholecystectomy. Among patients who did not undergo definitive resection, the underlying factors were varied. Only 24 (55.8%) patients in the non-definitive resection group were seen by surgical oncology. Among patients who underwent re-operation for definitive resection, 12 (38.7%) were upstaged on final pathology. Of the 43 patients who did not undergo definitive resection, 4 (9.3%) had metastatic disease identified during attempted re-resection. Patients who underwent definitive resection had a significantly longer median overall survival compared to those who did not (4.3 v. 1.9 years, p = 0.02).

CONCLUSIONS

Patients undergoing definitive resection have a significantly improved survival, including as part of a re-operative strategy. Universal referral to a surgical specialist is a modifiable factor resulting in increased definitive resection rates.

Development and Validation of a Prognostic Nomogram Based on the Systemic Immune-Inflammation Index for Resectable Gallbladder Cancer to Predict Survival and Chemotherapy Benefit

OBJECTIVES

To investigate the prognostic significance of the systemic immune-inflammation index (SII) in patients after radical cholecystectomy for gallbladder cancer (GBC) using overall survival (OS) as the primary outcome measure.

METHODS

Based on data from a multi-institutional registry of patients with GBC, significant prognostic factors after radical cholecystectomy were identified by multivariate Cox proportional hazards model. A novel staging system was established, visualized as a nomogram. The response to adjuvant chemotherapy was compared between patients in different subgroups according to the novel staging system.

RESULTS

Of the 1072 GBC patients enrolled, 691 was randomly selected in the discovery cohort and 381 in the validation cohort. SII>510 was found to be an independent predictor of OS (hazard ratio [HR] 1.90, 95% confidence interval [CI] 1.42-2.54). Carbohydrate antigen 199(CA19-9), tumor differentiation, T stage, N stage, margin status and SII were involved in the nomogram. The nomogram showed a superior prediction compared with models without SII (1-, 3-, 5-year integrated discrimination improvement (IDI):2.4%, 4.1%, 5.4%, P<0.001), and compared to TNM staging system (1-, 3-, 5-year integrated discrimination improvement (IDI):5.9%, 10.4%, 12.2%, P<0.001). The C-index of the nomogram in predicting OS was 0.735 (95% CI 0.683-0.766). The novel staging system based on the nomogram showed good discriminative ability for patients with T2 or T3 staging and with negative lymph nodes after R0 resection. Adjuvant chemotherapy offered significant survival benefits to these patients with poor prognosis.

CONCLUSIONS

SII was an independent predictor of OS in patients after radical cholecystectomy for GBC. The new staging system identified subgroups of patients with T2 or T3 GBC with negative lymph nodes who benefited from adjuvant chemotherapy.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, identifier (NCT04140552).

Clinicopathological characteristics and prognosis of signet ring cell carcinoma of the gallbladder

Background

Signet ring cell carcinoma (SRC) is a rare histological subtype of gallbladder adenocarcinoma. The current study evaluates the clinicopathologic features and prognosis of SRC.

Methods

Patients with adenocarcinoma of the gallbladder were identified in the Surveillance, Epidemiology, and End Results database from 1973 to 2016. Overall survival (OS) and cancer-specific survival (CSS) of patients who had SRC were compared with those of patients who had non-SRC using Cox regression and propensity score methods.

Results

Of 22,781 gallbladder adenocarcinomas retrieved, 377 (1.7%) were SRC and the other 22,404 were non-SRC. SRC was more significantly associated with older age, female gender, poor differentiation, advanced tumor stage, lymph node metastasis, distant metastasis, and advanced AJCC stage. The 5-year OS and CSS in the SRC group were 7.2 and 6.5%, respectively, both of which were significantly worse than the 13.2 and 13.3% seen in the SRC group (P = 0.002 and P = 0.012, respectively). This survival disadvantage persisted in multivariable analyses [hazard ratio (HR) = 1.256, P = 0.021 and HR = 1.211, P = 0.036] and after propensity score matching (OS: HR = 1.341, P = 0.012 and CSS: HR = 1.625, P = 0.005). Surgery in combination with chemotherapy improved OS of gallbladder SRC patients compared with surgery alone (HR = 0.726, P = 0.036) or chemotherapy alone (HR = 0.433, P < 0.001).

Conclusion

Patients with SRC of the gallbladder have distinct clinicopathological features with poor prognosis. Surgery in combination with chemotherapy can improve survival.

Identification of patients who may benefit the most from adjuvant chemotherapy following resection of incidental gallbladder carcinoma

BACKGROUND

To develop a scoring system to identify the subset of patients who may benefit the most from adjuvant chemotherapy following curative-intent resection for incidental gallbladder cancer (IGBC).

METHODS

A novel scoring system was utilized to stratify patients relative to overall survival (OS), as well as potential benefit from adjuvant chemotherapy following curative resection for IGBC.

RESULTS

Among 266 patients with IGBC, a total of 99 (37.2%) patients received adjuvant chemotherapy. Five risk factors were used to develop an integer-based score to predict OS. Risk of death at 5-years incrementally increased among patients in the low (n = 42, 69.0%), medium (n = 64, 56.3%) and high-risk groups (n = 40, 30.0%) (median OS, 99.4 vs. 33.5 vs. 15.6 months, all p < .001). Use of adjuvant chemotherapy did not provide a survival benefit among patients in the low-risk group (median survival, 99.4 vs. 60.7 months, p = .56). In contrast, utilization of adjuvant chemotherapy was associated with an improvement in survival among medium- (median survival, 21.7 vs. 59.5 months, p = .04) and high-risk patients (median survival, 11.6 vs. 20.1 months, p = .01).

CONCLUSIONS

While low-risk patients did not benefit from adjuvant chemotherapy, individuals with medium or high-risk scores had an improved survival with the utilization of adjuvant chemotherapy.

Validation of the 8th Edition American Joint Commission on Cancer (AJCC) Gallbladder Cancer Staging System: Prognostic Discrimination and Identification of Key Predictive Factors

The scope of our study was to compare the predictive ability of American Joint Committee on Cancer (AJCC) 7th and 8th edition in gallbladder carcinoma (GBC) patients, investigate the effect of AJCC 8th nodal status on the survival, and identify risk factors associated with the survival after N reclassification using the National Cancer Database (NCDB) in the period 2005-2015. The cohort consisted of 7743 patients diagnosed with GBC; 202 patients met the criteria for reclassification and were denoted as stage ≥III by AJCC 7th and 8th edition criteria. Overall survival concordance indices were similar for patients when classified by AJCC 8th (OS c-index: 0.665) versus AJCC 7th edition (OS c-index: 0.663). Relative mortality was higher within strata of T1, T2, and T3 patients with N2 compared with N1 stage (T1 HR: 2.258, p < 0.001; T2 HR: 1.607, p < 0.001; Τ3 HR: 1.306, p < 0.001). The risk of death was higher in T1-T3 patients with Nx compared with N1 stage (T1 HR: 1.281, p = 0.043, T2 HR: 2.221, p < 0.001, T3 HR: 2.194, p < 0.001). In patients with AJCC 8th edition stage ≥IIIB GBC and an available grade, univariate analysis showed that higher stage, Charlson-Deyo score ≥ 2, higher tumor grade, and unknown nodal status were associated with an increased risk of death, while year of diagnosis after 2013, academic center, chemotherapy. and radiation therapy were associated with decreased risk of death. Chemotherapy and radiation therapy were associated with decreased risk of death in patients with T3-T4 and T2-T4 GBC, respectively. In conclusion, the updated AJCC 8th GBC staging system was comparable to the 7th edition, with the recently implemented changes in N classification assessment failing to improve the prognostic performance of the staging system. Further prospective studies are needed to validate the T2 stage subclassification as well as to clarify the association, if any is actually present, between advanced N staging and increased risk of death in patients of the same T stage.

Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models

BACKGROUND

In this study, we developed a nomogram and a Bayesian network (BN) model for prediction of survival in gallbladder carcinoma (GBC) patients following surgery and compared the performance of the two models.

METHODS

Survival prediction models were established and validated using data from 698 patients with GBC who underwent curative-intent resection between 2008 and 2017 at one of six Chinese tertiary hospitals. Model construction and internal validation were performed using data from 381 patients at one hepatobiliary center, and external validation was then performed using data from 317 patients at the other five centers. A BN model and a nomogram model were constructed based on the independent prognostic variables. Performance of the BN and nomogram models was compared based on area under receiver operating characteristic curves (AUC), model accuracy, and a confusion matrix.

RESULTS

Independent prognostic variables included age, pathological grade, liver infiltration, T stage, N stage, and margin. In internal validation, AUC was 84.14% and 78.22% for the BN and nomogram, respectively, and model accuracy was 75.65% and 72.17%, respectively. In external validation, AUC was 76.46% and 70.19% for the BN and nomogram, respectively, with model accuracy of 66.88% and 60.25%, respectively. Based on the confusion matrix, the nomogram had a higher true positive rate but a substantially lower true negative rate compared to the BN.

CONCLUSION

A BN model was more accurate than a Cox regression-based nomogram for prediction of survival in GBC patients undergoing curative-intent resection.