Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study.

BACKGROUND: The prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level. METHODS: According to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs. RESULTS: Independent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05). CONCLUSION: The linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.

Risk prediction and analysis of gallbladder polyps with deep neural network.

The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People's Hospital of Shenzhen between January 2017 and December 2022. The patients' clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI -0.237 to 0.061, p < 0.001), number of polyps (95% CI -0.214 to -0.052, p = 0.001), polyp size (95% CI 0.038 to 0.051, p < 0.001), wall thickness (95% CI 0.042 to 0.081, p < 0.001), and gallbladder size (95% CI 0.185 to 0.367, p < 0.001) emerged as independent predictors for gallbladder adenomatous polyps and malignant polyps. Based on these significant findings, we developed a predictive classification model for gallbladder polyps, represented as follows, Predictive classification model for GBPs = -0.149 * core antibody - 0.033 * number of polyps + 0.045 * polyp size + 0.061 * wall thickness + 0.276 * gallbladder size - 4.313. To assess the predictive efficiency of the model, we employed precision-recall (PR) and receiver operating characteristic (ROC) curves. The area under the curve (AUC) for the prediction model was 0.945 and 0.930, respectively, indicating excellent predictive capability. We determined that a polyp size of 10 mm served as the optimal cutoff value for diagnosing gallbladder adenoma, with a sensitivity of 81.5% and specificity of 60.0%. For the diagnosis of gallbladder cancer, the sensitivity and specificity were 81.5% and 92.5%, respectively. These findings highlight the potential of our predictive model and provide valuable insights into accurate diagnosis and risk assessment for gallbladder polyps. We identified several risk factors associated with the development of adenomatous and malignant polyps in the gallbladder, including hepatitis B core antibodies, polyp number, polyp size, wall thickness, and gallbladder size. To address the need for accurate prediction, we introduced a novel neural network learning algorithm. This algorithm utilizes the aforementioned risk factors to predict the nature of gallbladder polyps. By accurately identifying the nature of these polyps, our model can assist patients in making informed decisions regarding their treatment and management strategies. This innovative approach aims to improve patient outcomes and enhance the overall effectiveness of care.

Accuracy of preoperative T2 gallbladder tumor localization and the adequate surgical resection.

INTRODUCTION: The agreement between the radiologic and histopathologic tumor locations in T2 gallbladder cancer is critical. There is no consensus regarding the extent of curative resection by tumor locations. METHODS: Between January 2010 and December 2019, a consecutive series of 118 patients with pathological T2 gallbladder cancer who underwent surgery were retrospectively analyzed in terms of the accordance between radiologic and histopathologic tumor locations, the extents of hepatic resection and the numbers of harvested lymph nodes. Radical resection was defined as liver resection with harvesting of at least four lymph nodes. RESULTS: The accuracy of preoperative tumor localization was only 68%. After radical resection, the 5-year overall survival (OS) was 59.4%; after nonradical resection, the figure was 46.1% (p = 0.092). In subanalyses, the 5-year OS was marginally better for patients who underwent liver resection or from whom at least four lymph nodes were harvested than those who did not undergo liver resection or from whom three or fewer lymph nodes were harvested (58.2% vs. 39.4%, p = 0.072; 59.9% vs. 50.0%, p = 0.072, respectively). In patients with peritoneal side tumor, the 5-year OSs of those who did and did not undergo liver resection were 67% and 41.2%, respectively (p = 0.028). In multivariate analysis, perineural invasion and radical resection were independently prognostic of OS. CONCLUSION: The accuracy of preoperative tumor localization was 68%. Hepatic resection, lymph node dissection harvesting of at least four lymph nodes are required for curative resection for gallbladder cancer, regardless of tumor location.

Positive 18 F-FDG and Negative 68 Ga-FAPI-04 Findings in a Patient With Gallbladder Adenocarcinoma.

ABSTRACT: Radiolabeled fibroblast activation protein inhibitor (FAPI) is considered as a potential alternative agent to 18 F-FDG for tumor-specific imaging. We report 18 F-FDG and 68 Ga-FAPI-04 PET/MR findings in a 67-year-old woman with gallbladder adenocarcinoma. The lesions showed intense 18 F-FDG uptake but limited 68 Ga-FAPI-04 uptake in PET/MR. This case emphasizes the necessity for nuclear clinicians to exercise caution when assessing gallbladder lesions with limited 68 Ga-FAPI-04 uptake, underscoring the continued relevance of 18 F-FDG in this diagnostic domain.

Gallbladder carcinoma presenting with disseminated bony metastasis.

Gallbladder cancer (GBC) is the 23rd most common cancer worldwide and one of the three leading cancers in North and Northeast India. GBC has inferior outcomes due to its advanced presentation and poor response to chemotherapy. The approximate 5-year survival rate for metastatic GBC is less than 5%, with a median survival of around 6 months. Distant metastases from GBC to the bones happen in the later part of the natural history of the disease. Presentation with bony metastasis is infrequent, and less than 25 cases have been reported. Our case was an elderly man in his 70s who presented with back pain and, on workup, was detected to have adenocarcinoma of the gall bladder with disseminated lytic bony metastasis without any visceral metastasis. This case describes the natural history of such cases and discusses the role of bone scan or fluorodeoxyglucose positron emission tomography in the workup for GBC.

Value of high frame rate contrast enhanced ultrasound in gallbladder wall thickening in non-acute setting.

BACKGROUND: Ultrasound (US) has been widely used in screening and differential diagnosis of gallbladder wall thickening (GWT). However, the sensitivity and specificity for diagnosing wall-thickening type gallbladder cancer are limited, leading to delayed treatment or overtreatment. We aim to explore the value of high frame rate contrast enhanced ultrasound (H-CEUS) in distinguishing wall-thickening type gallbladder cancer (malignant) from GWT mimicking malignancy (benign). METHODS: This retrospective study enrolled consecutive patients with non-acute GWT who underwent US and H-CEUS examination before cholecystectomy. Clinical information, US image and H-CEUS image characteristics between malignant and benign GWT were compared. The independent risk factors for malignant GWT on H-CEUS images were selected by multivariate logistic regression analysis. The diagnostic performance of H-CEUS in determining malignant GWT was compared with that of the gallbladder reporting and data system (GB-RADS) score. RESULTS: Forty-six patients included 30 benign GWTs and 16 malignant GWTs. Only mural layering and interface with liver on US images were significantly different between malignant and benign GWT (P < 0.05). Differences in enhancement direction, vascular morphology, serous layer continuity, wash-out time and mural layering in the venous phase of GWT on H-CEUS images were significant between malignant and benign GWT (P < 0.05). The sensitivity, specificity and accuracy of H-CEUS based on enhancement direction, vascular morphology and wash-out time in the diagnosis of malignant GWT were 93.75%, 90.00%, and 91.30%, respectively. However, the sensitivity, specificity and accuracy of the GB-RADS score were only 68.75%, 73.33% and 71.74%, respectively. The area under ROC curve (AUC) of H-CEUS was significantly higher than that of the GB-RADS score (AUC = 0.965 vs. 0.756). CONCLUSIONS: H-CEUS can accurately detect enhancement direction, vascular morphology and wash-out time of GWT, with a higher diagnostic performance than the GB-RADS score in determining wall-thickening type gallbladder cancer. This study provides a novel imaging means with high accuracy for the diagnosis of wall-thickening type gallbladder cancer, thus may be better avoiding delayed treatment or overtreatment.

Rare Findings of Gallbladder Metastasis From Cutaneous Melanoma on 18 F-FDG PET/CT Imaging.

ABSTRACT: We present the imaging findings of a 77-year-old man with a history of malignant cutis melanoma that metastasized to the gallbladder. A restaging 18 F-FDG PET/CT scan showed uneven thickening and elevated 18 F-FDG uptake in the gallbladder wall. Subsequently, the patient underwent laparoscopic cholecystectomy, and histopathologic findings confirmed the diagnosis of metastatic melanoma of the gallbladder.

Mixed adenoneuroendocrine carcinoma of the gallbladder.

A 58-year-old woman presented with a 1-day history of abdominal pain. Abdominal CT showed an oval soft tissue density mass in the fundus of the gallbladder (red arrow), approximately 4.0 cm × 3.0 cm in size. The level of cancer antigen 199 was elevated (275.80 U/mL; normal level, 0.0-27.0 U/mL). Other tumor markers were normal including alpha fetoprotein, carcinoembryonic antigen. Abdominal magnetic resonance imaging demonstrated the mass with characteristic of mixed signals, containing marked enhanced ingredient (yellow arrow) and poor blood supply ingredient (blue arrow). Radical cholecystectomy, partial liver resection, and regional lymphadenectomy were performed. Pathological examination indicated mixed adenoneuroendocrine carcinoma, with the following immunohistochemistry results: CD56 (+) (Figure 1F), Syn (+) (Figure 1G), CK19 (+) (Figure 1H), CgA (+), MLHL (+), PMS2 (+), MSH2 (+), MSH6 (+), Ki-67 (60%+).

Percutaneous cholecystostomy: an unusual route of dissemination of gallbladder carcinoma.

A 78-year-old male with high-risk surgical presented severe acute cholecystitis and required cholecystostomy. The patient was referred later for assessment of the surgical treatment. A cholangio-MRI revealed a lesion on the gallbladder fundus with hepatic lesions suggestive of metastatic gallbladder carcinoma, which was confirmed in the histological analysis. The tumor progressed despite the chemotherapy through the cholecystostomy tract and developed peritoneal carcinomatosis. The patient did not respond to chemotherapy and he died 12 months later.

Proposal for a new morphological "combined type" of gallbladder cancer: description of radiopathological characteristics and comparison with other morphological types.

OBJECTIVE: To describe the radiopathological characteristics of a new morphological "combined type" of gallbladder cancer (GBC) and compare it with the mass replacing gallbladder and thickening types of GBC. MATERIALS AND METHODS: The imaging and pathological details of consecutive patients with GBC between August 2020 and December 2022 were retrospectively reviewed. Two radiologists reviewed computed tomography/magnetic resonance imaging in consensus for the morphological type of GBC. The radiologists classified GBC as mass replacing gallbladder, wall thickening, and combined type. The combined type was defined as a mass arising from the thickened wall of an adequately distended gallbladder that extended exophytically into the adjacent liver parenchyma. The presence of calculi, site, and size of lesion, biliary/portal vein involvement, liver, lymph node, and omental metastases was compared among the various types. The pathological characteristics were also compared. RESULTS: Of the 481 patients (median age 55 years, 63.2% females) included in the study, mass replacing gallbladder, wall thickening, and combined-type GBC were seen in 42.8% (206/481), 40.5% (195/481), and 16.6% (80/481) of patients, respectively. In the combined type of GBC, biliary/portal vein involvement was seen in 63.7% (51/80) and 7.5% (6/80) of patients. Liver, lymph node, and omental metastases were seen in 67.5% (54/80), 40% (32/80), and 41.2% (33/80) patients, respectively. Liver metastases were significantly more common in the combined type (p = 0.002). There were no significant differences in pathological characteristics among the various types. CONCLUSION: Combined-type GBC is less common than the mass replacing gallbladder and thickening types and is associated with a higher risk of liver metastases.

Distinguishing characteristics of xanthogranulomatous cholecystitis and gallbladder adenocarcinoma: a persistent diagnostic dilemma.

BACKGROUND: Xanthogranulomatous cholecystitis (XGC) is an uncommon variant of chronic cholecystitis which can resemble gallbladder adenocarcinoma (GAC) on preoperative imaging and present technical challenges in the performance of cholecystectomy. We examined our experience with each pathology to identify distinguishing characteristics that may guide patient counseling and surgical management. METHODS: A retrospective review of all pathologically confirmed cases of XGC and GAC following cholecystectomy between 2015 and 2021 at a single institution was performed. Clinical, biochemical, radiographic, and intraoperative features were compared. RESULTS: There were 37 cases of XGC and 20 cases of GAC. Patients with GAC were older (mean 70.3 years vs 58.0, p = 0.01) and exclusively female (100% vs 45.9%, p < 0.0001). There were no significant differences in accompanying symptoms between groups (nausea/vomiting, fevers, or jaundice). The mean maximum white blood cell count was elevated for XGC compared to GAC (16.4 vs 8.6 respectively, p = 0.044); however, there were no differences in the remainder of the biochemical profile, including bilirubin, liver transaminases, CEA, and CA 19-9. The presence of an intraluminal mass (61.1% vs 9.1%, p = 0.0001) and lymphadenopathy (18.8%. vs 0.0%, p = 0.045) were associated with malignancy, whereas gallbladder wall thickening as reported on imaging (87.9% vs 38.9%, p = 0.0008) and gallstones (76.5% vs. 50.0%, p = 0.053) were more often present with XGC. Cases of XGC more often had significant adhesions/inflammation (83.8% vs 55.0%, p = 0.03). CONCLUSION: Clinical features that may favor benign chronic cholecystitis over gallbladder adenocarcinoma include younger age, male gender, current or prior leukocytosis, and the absence of an intraluminal mass or lymphadenopathy. Laparoscopic cholecystectomy is a safe surgical option for equivocal presentations. Intraoperative frozen section or intentional staging of more extensive procedures based upon final histopathology are valuable surgical strategies.

Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.

OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients. MATERIALS AND METHODS: A total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Through the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801. CONCLUSION: Radiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision.

The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built from contrast enhanced computed tomography (CT) scans. 297 patients were included with confirmed XGC (94) and GBC (203) as the training and internal validation cohort from 2017 to 2021. The deep learning model Resnet-18 with Fourier transformation named FCovResnet18, shows most impressive potential in distinguishing XGC from GBC using 3-phase merged images. The accuracy, precision and area under the curve (AUC) of the model were then calculated. An additional cohort of 74 patients consisting of 22 XGC and 52 GBC patients was enrolled from two subsidiary hospitals as the external validation cohort. The accuracy, precision and AUC achieve 0.98, 0.99, 1.00 in the internal validation cohort and 0.89, 0.92, 0.92 in external validation cohort. A nomogram model combining clinical characteristics and deep learning prediction score showed improved predicting value. Altogether, FCovResnet18 nomogram has demonstrated its ability to effectively differentiate XGC from GBC preoperatively, which significantly aid surgeons in making informed and accurate surgical decisions for XGC and GBC patients.

Risk stratification model for incidentally detected gallbladder polyps: A multicentre study.

PURPOSE: We aimed to develop a 4-level risk stratification model using a scoring system based on conventional ultrasound to improve the diagnosis of gallbladder polyp. METHOD: Patients with histopathologically confirmed gallbladder polyps were consecutively recruited from three medical centres. Conventional ultrasound findings and clinical characteristics were acquired prior to cholecystectomy. Risk factors for neoplastic and malignant polyps were used to build a risk stratification system via interobserver agreement and multivariate logistic regression analysis. The model was retrospectively trained using 264 pre-surgical samples and prospectively validated using 106 pre-surgical samples. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and malignant polyp rate. RESULTS: In total, 370 patients (mean age, 51.68 ± 14.41 years, 156 men) were enrolled in this study. Size (≥12 mm), shape (oblate or round), single, vascularity, gallbladder stone or sludge were considered risk factors for neoplastic polyps. Size (≥14 mm), shape (oblate), single, disrupted gallbladder wall, and gallbladder stone or sludge were risk factors for malignant polyps (all p < 0.05). In the scoring system, the sensitivity, specificity, and AUC of score ≥ 9 in diagnosing neoplastic polyps were 0.766, 0.788, and 0.876 respectively; and the sensitivity, specificity, and AUC of score ≥ 15 in diagnosing malignant polyps were 0.844, 0.926, and 0.949 respectively. In our model, the malignancy rates at the four levels were 0 % (0/24), 1.28 % (2/156), 9.26 % (5/54), and 70.37 % (38/54), respectively. CONCLUSIONS: The 4-level risk stratification model based on conventional ultrasound imaging showed excellent performance in classifying gallbladder polyps.

Apparent diffusion coefficient and its standard deviation from diffusion-weighted imaging in preoperative predicting liver invasion by T3-staged resectable gallbladder carcinoma.

AIM: To evaluate apparent diffusion coefficient (ADC) and its standard deviation (SDADC) in preoperative predicting liver invasion by T3-staged gallbladder carcinoma (GBC). MATERIALS AND METHODS: Forty-one consecutive patients with T3-staged resectable GBC were included and divided into two sets with (n=27) and without (n=14) liver invasion. All patients underwent DWI at b-values of 0, 20, 50, 80, 100, 200, 400, 600, 800, and 1,000 s/mm2 with a 3 T magnetic resonance imaging scanner before surgery. ADC and SDADC of tumour-adjacent and tumour-distant liver tissues were measured on DWI, and were compared by Mann-Whitney U-tests. If there was a significant difference in any derived parameter, the area under the receiver operating characteristic curve (AUC) was used to assess performance of this parameter to predict liver invasion. RESULTS: DWI could differentiate between patients with and without liver invasion when b = 0, 1,000 s/mm2 (AUCs of ADC and SDADC were 0.697 and 0.714, respectively). In patients with liver invasion, mean ADC and SDADC of tumour-adjacent liver tissue were lower than of tumour-distant liver tissue when b = 0, 800 s/mm2, and = 0, 1,000 s/mm2 (all p-values <0.05). To differentiate tumour-adjacent from tumour-distant liver tissues in patients with liver invasion, AUCs of ADC were 0.687 (b = 0, 800 s/mm2) and 0.680 (b = 0, 1,000 s/mm2), and AUCs of SDADC were 0.673 (b = 0, 800 s/mm2) and 0.731 (b = 0, 1,000 s/mm2). CONCLUSIONS: DWI could have potential value in preoperative predicting liver invasion by T3-staged GBC.

Utilization of Radiomics Features Extracted From Preoperative Medical Images to Detect Metastatic Lymph Nodes in Cholangiocarcinoma and Gallbladder Cancer Patients: A Systemic Review and Meta-analysis.

OBJECTIVES: This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer. METHODS: Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability. RESULTS: Overall sensitivity was 0.748 (95% CI, 0.703-0.789). Overall specificity was 0.795 (95% CI, 0.742-0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266-0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850-4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477-17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80-0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80-0.89] and 0.85 [0.71-0.93], respectively). CONCLUSIONS: The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.