CT-based nomogram for predicting survival after R0 resection in patients with gallbladder cancer: a retrospective multicenter analysis

Preoperative MRI-based nomogram to predict survival after curative resection in patients with gallbladder cancer: a retrospective multicenter analysis

PURPOSE

To use preoperative MRI data to construct a nomogram to predict survival in patients who have undergone R0 resection for gallbladder cancer.

METHODS

The present retrospective study included 143 patients (M:F, 76:67; 67.15 years) with gallbladder cancer who underwent preoperative MRI and subsequent R0 resection between 2013 and 2021 at two tertiary institutions. Clinical and radiological features were analyzed using univariate and multivariate Cox regression analysis to identify independent prognostic factors. Based on the multivariate analysis, we developed an MRI-based nomogram for determining prognoses after curative resections of gallbladder cancer. We also obtained calibration curves for 1-,3-, and 5-year survival probabilities.

RESULTS

The multivariate model consisted of the following independent predictors of poor overall survival (OS), which were used for constructing the nomogram: age (years; hazard ratio [HR] = 1.04; 95% confidence interval [CI], 1.04-1.07; p = 0.033); tumor size (cm; HR = 1.40; 95% CI, 1.09-1.79; p = 0.008); bile duct invasion (HR = 3.54; 95% CI, 1.66-7.58; p = 0.001); regional lymph node metastasis (HR = 2.47; 95% CI, 1.10-5.57; p = 0.029); and hepatic artery invasion (HR = 2.66; 95% CI, 1.04-6.83; p = 0.042). The nomogram showed good probabilities of survival on the calibration curves, and the concordance index of the model for predicting overall survival (OS) was 0.779.

CONCLUSION

Preoperative MRI findings could be used to determine the prognosis of gallbladder cancer, and the MRI-based nomogram accurately predicted OS in patients with gallbladder cancer who underwent curative resection.

Novel multifactor predictive model for postoperative survival in gallbladder cancer: a multi-center study

BACKGROUND

Gallbladder cancer (GBC) is a highly aggressive malignancy, with limited survival profiles after curative surgeries. This study aimed to develop a practical model for predicting the postoperative overall survival (OS) in GBC patients.

METHODS

Patients from three hospitals were included. Two centers (N = 102 and 100) were adopted for model development and internal validation, and the third center (N = 85) was used for external testing. Univariate and stepwise multivariate Cox regression were used for feature selection. A nomogram for 1-, 3-, and 5-year postoperative survival rates was constructed accordingly. Performance assessment included Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier curves were utilized to evaluate the risk stratification results of the nomogram. Decision curves were used to reflect the net benefit.

RESULTS

Eight factors, TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), body mass index (BMI), R0 resection, blood platelet count, and serum levels of albumin, CA125, CA199 were incorporated in the nomogram. The time-dependent C-index consistently exceeded 0.70 from 6 months to 5 years, and time-dependent ROC revealed an area under the curve (AUC) of over 75% for 1-, 3-, and 5-year survival. The calibration curves, Kaplan-Meier curves and decision curves also indicated good prognostic performance and clinical benefit, surpassing traditional indicators TNM staging and CA199 levels. The reliability of results was further proved in the independent external testing set.

CONCLUSIONS

The novel nomogram exhibited good prognostic efficacy and robust generalizability in GBC patients, which might be a promising tool for aiding clinical decision-making.

Machine learning-based short-term DFS-associated characteristic factor screening and model construction for patients with gallbladder cancer after radical surgery

Gallbladder cancer (GBC) is a malignancy with a bleak prognosis, and radical surgery remains the primary treatment option. However, the high postoperative recurrence rate and the lack of individualized risk assessment tools limit the effectiveness of current treatment strategies. This study aims to identify risk factors affecting the short-term disease-free survival (DFS) of GBC patients using machine learning methods and to build a prediction model. A retrospective analysis was conducted on the clinical data from 328 GBC patients treated at the First Affiliated Hospital of Huzhou University from 2008 to 2021. Patients were randomly divided into a training set (n=230) and a validation set (n=98). Clinical data, laboratory indexes, and follow-up data were collected. Univariate Cox regression analysis identified age, tumor T-staging, lymph node metastasis, differentiation degree, and CA199 level as prognostic factors affecting DFS (all P<0.05). A prediction model constructed using the LASSO regression achieved AUCs of 0.827 and 0.801 for predicting 1-year and 3-year DFS, respectively. Notably, the XGBoost regression model showed higher prediction accuracy with AUCs of 0.922 and 0.947, respectively. The Delong test confirmed that the XGBoost model had significantly higher AUC values compared to the LASSO model (all P<0.001). In the validation set, the XGBoost model demonstrated AUCs of 0.764 and 0.761 for predicting 1-year and 3-year DFS, respectively. Overall, the XGBoost regression model demonstrates high accuracy and clinical value in predicting short-term DFS in GBC patients after radical surgery, offering a valuable tool for personalized treatment.

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer

RATIONALE AND OBJECTIVES

An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection.

METHODS

A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis.

RESULTS

The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05).

CONCLUSION

The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

Gastrointestinal involvement in gallbladder cancer: Computed tomography findings and proposal of a classification system

BACKGROUND

There is relatively scarce data on the computed tomography (CT) detection of gastrointestinal (GI) involvement in gallbladder cancer (GBC). We aim to assess the GI involvement in GBC on CT and propose a CT-based classification.

METHODS

This retrospective study comprized consecutive patients with GBC who underwent contrast-enhanced computed tomography (CECT) for staging between January 2019 and April 2022. Two radiologists evaluated the CT images independently for the morphological type of GBC and the presence of GI involvement. GI involvement was classified into probable involvement, definite involvement and GI fistulization. The incidence of GI involvement and the association of GI involvement with the morphological type of GBC was evaluated. In addition, the inter-observer agreement for GI involvement was assessed.

RESULTS

Over the study period, 260 patients with GBC were evaluated. Forty-three (16.5%) patients had GI involvement. Probable GI involvement, definite GI involvement and GI fistulization were seen in 18 (41.9%), 19 (44.2%) and six (13.9%) patients, respectively. Duodenum was the most common site of involvement (55.8%), followed by hepatic flexure (23.3%), antropyloric region (9.3%) and transverse colon (2.3%). There was no association between GI involvement and morphological type of GBC. There was substantial to near-perfect agreement between the two radiologists for the overall GI involvement (k = 0.790), definite GI involvement (k = 0.815) and GI fistulization (k = 0.943). There was moderate agreement (k = 0.567) for probable GI involvement.

CONCLUSION

GBC frequently involves the GI tract and CT can be used to categorize the GI involvement. However, the proposed CT classification needs validation.

CT features of divisional bile ducts in healthy Labrador Retrievers

Computed tomography (CT) is increasingly being used for the study of gallbladder and bile duct diseases. The first step in interpreting CT findings is understanding the cross-sectional anatomy of the structures involved, but there are no published studies describing the CT features of the divisional bile ducts. In dogs, anatomic studies report two common patterns including three or four divisional bile ducts. The aim of this retrospective, descriptive, anatomical study was to describe the size and pattern of the visible divisional bile ducts, based on their location and extension, using contrast-enhanced CT in a group of Labrador Retrievers without evidence of hepatobiliary diseases. The correlation between the biliary duct number and dimensions, and the visceral fat area percentage (VFA%) was also evaluated. The right lateral divisional duct (RLD) was visualized in four of 40 dogs, the left lateral divisional duct (LLD) in nine of 40 dogs, and in 17 of 40 dogs, both were simultaneously visualized. In 10 of 40 dogs, the RLD and LLD were not highlighted. When visible, the RLD has a median diameter of 0.23 cm and a median length of 0.82 cm. The LLD has a median diameter of 0.23 cm and a median length of 2.72 cm. The median diameter of the common bile duct before and after the insertion of divisional bile ducts was 0.23 and 0.25 cm, respectively. No correlation with the VFA% was found. At least one of the divisional bile ducts could be visualized using contrast-enhanced CT in the majority of sampled dogs (75%) .

Development and validation of prognostic dynamic nomograms for hepatitis B Virus-related hepatocellular carcinoma with microvascular invasion after curative resection

Background and Aim

The prediction models of postoperative survival for hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) have not been well established. The study objective was the development of nomograms to predict disease recurrence and overall survival (OS) in these patients.

Methods

Data were obtained from 1046 HBV-related MVI-positive HCC patients who had undergone curative resection from January 2014 to December 2017. The study was approved by the Eastern Hepatobiliary Surgery Hospital and Jinling Hospital ethics committee, and patients provided informed consent for the use of their data. Nomograms for recurrence and OS were created by Cox regression model in the training cohort (n=530). The modes were verified in an internal validation cohort (n= 265) and an external validation cohort (n= 251).

Results

The nomograms of recurrence and OS based on preoperative serological indicators (HBV-DNA, neutrophil-lymphocyte ratio, a-fetoprotein), tumor clinicopathologic features (diameter, number), surgical margin and postoperative adjuvant TACE achieved high C-indexes of 0.722 (95% confidence interval [CI], 0.711-0.732) and 0.759 (95% CI, 0.747-0.771) in the training cohort, respectively, which were significantly higher than conventional HCC staging systems (BCLC, CNLC, HKLC).The nomograms were validated in the internal validation cohort (0.747 for recurrence, 0.758 for OS) and external validation cohort(0.719 for recurrence, 0.714 for OS) had well-fitted calibration curves. Our nomograms accurately stratified patients with HBV-HCC with MVI into low-, intermediate- and high-risk groups of postsurgical recurrence and mortality. Prediction models for recurrence-free survival (https://baishileiehbh.shinyapps.io/HBV-MVI-HCC-RFS/) and OS (https://baishileiehbh.shinyapps.io/HBV-MVI-HCC-OS/) were constructed.

Conclusions

The two nomograms showed good predictive performance and accurately distinguished different recurrence and OS by the nomograms scores for HBV-HCC patients with MVI after resection.

Prognostic Value of the Albumin-to-γ-glutamyltransferase Ratio for Gallbladder Cancer Patients and Establishing a Nomogram for Overall Survival

Purpose: The albumin-to-γ-glutamyltransferase ratio (AGR), a novel inflammation-related index, has been reported to have prognostic importance in several malignancies but not yet in gallbladder cancer (GBC). This study intended to assess the prognostic value of AGR in GBC and to develop a nomogram based on AGR for predicting overall survival (OS) in GBC patients after surgery.

Methods: Medical records of 140 qualified GBC patients between July 2003 and June 2017 were retrospectively analyzed. The function “surv_cutpoint” in the R package “survminer” was implemented to discover the optimal cut-off value of AGR. A nomogram on the fundamental of Cox model was established in the training cohort and was internally validated using calibration curves, Harrell's concordance index, time-dependent AUC plots and decisive curve analyses.

Results: The optimal AGR cut-off value concerning overall survival was 2.050. Univariate and multivariate analyses demonstrated that AGR (HR=0.354, P=0.004), T stage (HR=3.114, P=0.004), R0 resection (HR=0.448, P=0.003), BMI (HR=0.470, P=0.002) and CA19-9 (HR=1.704, P=0.048) were independent predictors for OS. The nomogram combining these prognostic factors showed considerable prognostic performance in term of consistency, discrimination and net benefit.

Conclusion: AGR has independent prognostic value for OS in GBC patients receiving surgery. A nomogram incorporating AGR, T stage, R0 resection, CA19-9 and BMI achieved enhanced prognostic ability.