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We report a case of metastatic melanoma of the gallbladder diagnosed by contrast-enhanced ultrasound and systematically review the characteristics of transabdominal ultrasound, clinical manifestations, and treatment methods of gallbladder metastatic melanoma in order to provide reference ideas for the diagnosis and treatment of metastatic melanoma of gallbladder., (© 2024 Wiley Periodicals LLC.)

Background: Surgical resection, the only potentially curative treatment for gallbladder cancer (GBC), entails an extended cholecystectomy with portal lymphadenectomy. Lymph node dissection is a key staging procedure, but its therapeutic value is unclear. Additionally, it is technically challenging and potentially harmful. Methods for better assessment of lymph node status are needed. This report presents a case of indocyanine green (ICG)-guided sentinel lymph node biopsy (SNLB) for a patient with a gallbladder mass suspicious for GBC., Methods: An 81-year-old woman consulted for abdominal discomfort. Abdominal ultrasound showed an intraluminal gallbladder mass suspicious for GBC. Staging imaging did not show liver invasion, lymphadenopathy, or distant metastasis. Given the woman's advanced age and limited extent of disease, a laparoscopic extended cholecystectomy with an ICG-guided SLNB was performed. Injection of 1 ml of ICG (0.125 mg/mL) into the gallbladder bed was performed using a 22-gauge needle, avoiding direct injection into the gallbladder wall., Results: A near-infrared camera was used to visualize real-time ICG flow through the lymphatic vessels of the gallbladder toward the cystic node. Then, a sentinel lymph node posterolateral to the bile duct (station 12b) was identified. The node was resected and sent for permanent section. The procedure continued with an extended cholecystectomy. Pathology showed an intracholecystic papillary neoplasm with high-grade dysplasia. Cystic and sentinel lymph nodes were negative for malignancy., Conclusion: For patients with gallbladder neoplasms, ICG-guided SLNB is a feasible technique that could allow for treatment de-escalation. Further evaluation in clinical trials is needed., (© 2024. Society of Surgical Oncology.)

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer., (© 2024. The Author(s).)

History: A 45-year-old female patient who was previously healthy presented after several weeks of fullness in the right upper quadrant of the abdomen. The patient did not experience pain, nausea, vomiting, or jaundice, and had no contributory past medical or surgical history, including no history of malignancy. Upon examination, vital signs were within normal limits and the patient appeared well, with soft palpable fullness in the right upper quadrant. The abdomen was nontender and nondistended. Laboratory investigation revealed no abnormalities, with a normal complete blood cell count and normal serum tumor markers that included α-fetoprotein (<2.0 ng/mL; reference, <8.3 ng/mL), cancer antigen 19-9 (21.6 U/mL; reference, <35 U/mL), and carcinoembryonic antigen (1.3 ng/mL; reference, <5 ng/mL). CT of the abdomen and pelvis was performed with intravenous contrast material in the emergency department. Subsequently, combined MRI and MR cholangiopancreatography of the abdomen was performed with and without intravenous contrast material for further evaluation. CT of the chest performed during the same encounter was unremarkable.

Background and Aims: It is difficult to differentiate between neoplastic and non-neoplastic gallbladder (GB) polyps before surgery. EUS-guided elastography (EUS-EG) is a noninvasive complementary diagnostic method. The utility of EUS-EG in the differential diagnosis of GB polyps has not been investigated. We investigated the diagnostic performance of EUS-EG for the differential diagnosis of GB polyps., Methods: Patients with GB polyps were prospectively enrolled from June 2020 until November 2022. EUS-EG and semiquantitative evaluation of the strain ratio (SR) were performed for differential diagnosis of GB polyps. Fifty-three eligible patients were divided into 2 groups based on the final diagnosis after surgery. Patient demographics, EUS characteristics, and SR values were compared. A receiver-operating characteristic curve analysis was performed to determine the optimal cutoff SR value that discriminates between neoplastic and non-neoplastic GB polyps., Results: The median SR value for neoplastic polyps (32.93 [interquartile range {IQR}, 22.37-69.02]) was significantly higher than for non-neoplastic polyps (5.40 [IQR, 2.36-14.44], P < .001). Significant differences were found in SR values between non-neoplastic, benign neoplastic (23.38 [IQR, 13.62-39.04]), and malignant polyps (49.25 [IQR, 27.90-82.00]). The optimal cutoff SR value to differentiate between neoplastic and non-neoplastic polyps was 18.4. In multivariable logistic regression, SR value >18.4 (odds ratio, 33.604; 95% confidence interval, 2.588-436.292) was an independent predictor of neoplastic polyps., Conclusions: EUS-EG and SR values can be used as a supplementary method for evaluating GB polyps. (Clinical trial registration number: NCT04416763.)., Competing Interests: Disclosure All authors disclosed no financial relationships., (Copyright © 2024 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Gallbladder (GB) polyps are a common incidental finding on sonography, but only a small fraction of polyps become GB cancer. The Society of Radiologists in Ultrasound (SRU) consensus committee recently performed an extensive literature review and published guidelines for GB polyp follow-up/management to provide clarity among the many heterogeneous recommendations that are available to clinicians. As these guidelines have become adopted into clinical practice, challenging clinical scenarios have arisen including GB polyps in primary sclerosing cholangitis (PSC), high risk geographic/genetic patient populations, shrinking polyps, pedunculated vs sessile polyps, thin vs thick stalked polyps, vascular polyps and multiple polyps. According to the SRU guidelines, clinicians should refer to gastroenterology guidelines when managing GB polyps in patients with known PSC. If patients at high geographic/genetic risk develop GB polyps, 'extremely low risk' polyps may be managed as 'low risk' and 10-14 mm 'extremely low risk' or '7-14 mm' low risk polyps that decrease in size by ≥ 4 mm require no follow-up. Thin-stalked or pedunculated polyps are 'extremely low risk' and thick-stalked pedunculated polyps are 'low risk'. Sessile polyps are 'low risk' but should receive immediate specialist referral if features suggestive of GB cancer are present. Neither polyp multiplicity nor vascularity impact risk of GB cancer and follow up should be based on morphology alone., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficult. This study aimed to develop a deep learning model based on contrast-enhanced computed tomography (CT) images to assist radiologists in identifying GBC., Methods: We retrospectively enrolled 278 patients with gallbladder lesions (> 10 mm) who underwent contrast-enhanced CT and cholecystectomy and divided them into the training (n = 194) and validation (n = 84) datasets. The deep learning model was developed based on ResNet50 network. Radiomics and clinical models were built based on support vector machine (SVM) method. We comprehensively compared the performance of deep learning, radiomics, clinical models, and three radiologists., Results: Three radiomics features including LoG&#95;3.0 gray-level size zone matrix zone variance, HHL first-order kurtosis, and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC, and were selected for developing radiomics model. Multivariate regression analysis revealed that age ≥ 65 years [odds ratios (OR) = 4.4, 95% confidence interval (CI): 2.1-9.1, P < 0.001], lesion size (OR = 2.6, 95% CI: 1.6-4.1, P < 0.001), and CA-19-9 > 37 U/mL (OR = 4.0, 95% CI: 1.6-10.0, P = 0.003) were significant clinical risk factors of GBC. The deep learning model achieved the area under the receiver operating characteristic curve (AUC) values of 0.864 (95% CI: 0.814-0.915) and 0.857 (95% CI: 0.773-0.942) in the training and validation datasets, which were comparable with radiomics, clinical models and three radiologists. The sensitivity of deep learning model was the highest both in the training [90% (95% CI: 82%-96%)] and validation [85% (95% CI: 68%-95%)] datasets., Conclusions: The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions., (Copyright © 2023 First Affiliated Hospital, Zhejiang University School of Medicine in China. Published by Elsevier B.V. All rights reserved.)

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis., (© 2024. Indian Society of Gastroenterology.)

Background: The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We aimed at utilizing the deep learning (DL)-based approach for differentiating XGC and GBC on ultrasound (US)., Methods: This single-center study comprised consecutive patients with XGC and GBC from a prospectively acquired database who underwent pre-operative US evaluation of the gallbladder lesions. The performance of state-of-the-art (SOTA) DL models (GBCNet-convolutional neural network [CNN] and RadFormer, transformer) for XGC vs. GBC classification in US images was tested and compared with popular DL models and a radiologist., Results: Twenty-five patients with XGC (mean age, 57 ± 12.3, 17 females) and 55 patients with GBC (mean age, 54.6 ± 11.9, 38 females) were included. The performance of GBCNet and RadFormer was comparable (sensitivity 89.1% vs. 87.3%, p = 0.738; specificity 72% vs. 84%, p = 0.563; and AUC 0.744 vs. 0.751, p = 0.514). The AUCs of DenseNet-121, vision transformer (ViT) and data-efficient image transformer (DeiT) were significantly smaller than of GBCNet (p = 0.015, 0.046, 0.013, respectively) and RadFormer (p = 0.012, 0.027, 0.007, respectively). The radiologist labeled US images of 24 (30%) patients non-diagnostic. In the remaining patients, the sensitivity, specificity and AUC for GBC detection were 92.7%, 35.7% and 0.642, respectively. The specificity of the radiologist was significantly lower than of GBCNet and RadFormer (p = 0.001)., Conclusion: SOTA DL models have a better performance than radiologists in differentiating XGC and GBC on the US., (© 2023. Indian Society of Gastroenterology.)

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.

Intracholecystic papillary neoplasm (ICPN) of the gallbladder is a rare tumor described as a mucosal exophytic neoplastic lesion that projects into the gallbladder lumen. In regards to the size, lesions that did not make the arbitrary 1cm cutoff are described as "incipient" ICPN. Not much is known about these incipient ICPNs, as they are often excluded in ICPN studies, given the attempted adherence to the traditional 1cm cutoff. We present the youngest reported case of incipient, non-mucinous gastric-pylorus type ICPN who underwent cholecystectomy. Resection with negative margin for ICPN appears to be sufficient treatment and post resection imaging surveillance could be of value but further studies are required., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr Jeyarajah is a consultant for AngioDynamics, Ethicon, and Sirtex.

Background: There is little information regarding the size measurement differences in gallbladder (GB) polyps performed by different radiologists on abdominal ultrasonography (US)., Aim: To reveal the differences in GB polyp size measurements performed by different radiologists on abdominal US., Methods: From June to September 2022, the maximum diameter of 228 GB polyps was measured twice on abdominal US by one of three radiologists (a third-year radiology resident [reader A], a radiologist with 7 years of experience in abdominal US [reader B], and an abdominal radiologist with 8 years of experience in abdominal US [reader C]). Intra-reader agreements for polyp size measurements were assessed by intraclass correlation coefficient (ICC). A Bland-Altman plot was used to visualize the differences between the first and second size measurements in each reader., Results: Reader A, reader B, and reader C evaluated 65, 77, and 86 polyps, respectively. The mean size of measured 228 GB polyps was 5.0 ± 1.9 mm. Except for the case where reader A showed moderate intra-reader agreement (0.726) for polyps with size ≤ 5 mm, all readers showed an overall high intra-reader reliability (reader A, ICC = 0.859; reader B, ICC = 0.947, reader C, ICC = 0.948), indicative of good and excellent intra-reader agreements. The 95% limit of agreement of reader A, B, and C was 1.9 mm of the mean in all three readers., Conclusions: GB polyp size measurement on abdominal US showed good or excellent intra-reader agreements. However, size changes of approximately less than 1.9 mm should be interpreted carefully because these may be within the measurement error.

Abstract: A 64-year-old woman presented with chest pain while eating and was referred to our hospital. Physical examination revealed abdominal distension, tenderness, and lower-extremity edema. Imaging revealed a large gallbladder tumor infiltrating the liver, with ascites and pleural effusion. A biopsy confirmed a poorly differentiated adenocarcinoma with SMARCA4 deficiency (cT3N2M1, cStage IV). Chemotherapy was ineffective and led to tumor progression. The patient died 9 months later. Recently, attention has been paid to SMARCA4 deficiency, which is a genetic mutation found in tumors. Here, we report on poorly differentiated adenocarcinomas of the gallbladder based on imaging findings, including FDG PET., Competing Interests: Conflicts of interest and sources of funding: The present study was financially supported by JSPS KAKENHI (grant number 22K15879). There are no potential conflicts of interest to disclose about this manuscript., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Gallbladder wall thickening is relatively common in clinical settings, and for appropriate diagnosis, the size, shape, internal structure, surface contour, and vascularity of the gallbladder wall must be evaluated. Morphological evaluation is the most important; however, some gallbladder lesions resemble gallbladder cancer in imaging studies, making differential diagnosis challenging. Vascular evaluation is indispensable for a precise diagnosis in these cases. In this review, we present the current status of vascular evaluation using US and diagnosis using vascular imaging for gallbladder lesions, including those presenting with wall thickening. To date, several ultrasound imaging techniques have been developed to assess vascularity, including Doppler imaging with high sensitivity, use of contrast agents, and microvascular imaging using a novel filter for Doppler imaging. Although conventional color Doppler imaging is rarely used for the diagnosis of gallbladder lesions, the efficacy of contrast-enhanced ultrasound in assessing the vascularity, enhancement pattern, or timing of enhancement/washout has been reported. Presence of multiple irregular microvessels has been speculated to indicate malignancy. However, few reports on microvessels have been published, and further studies are required for the precise diagnosis of gallbladder lesions with microvascular evaluation., (© 2024. The Author(s), under exclusive licence to The Japan Society of Ultrasonics in Medicine.)

Purpose: To identify high-risk computed tomography (CT) features for predicting gallbladder (GB) cancer in patients presenting with localized GB wall thickening., Methods: This retrospective analysis included 120 patients (mean age: 63.9 ± 10.0 years; 51 men) exhibiting localized GB wall thickening on CT scans obtained between January 2008 and May 2017. Two radiologists independently evaluated CT imaging features for predicting GB cancer. The diagnostic performance of significant imaging features and their combinations was evaluated. High-risk CT features ranked by accuracy were delineated for predicting GB cancer., Results: This study included 55 patients with GB cancer and 65 with benign GB conditions. The top-four most accurate CT imaging features for predicting GB cancer were identified: heterogeneously enhancing single layer or strongly enhancing thick inner layer; GB wall thickness > 6.5 mm; hyperenhancement on arterial phase; and absence of intramural small cystic lesions (accuracies of 90.0 %, 88.3 %, 85.0 %, and 85.0 %, respectively). The combination of any three high-risk features exhibited the highest accuracy (94.2 %). The presence of any high-risk feature yielded a sensitivity of 100 %, whereas that of all high-risk features indicated a specificity of 100 %., Conclusion: CT imaging features, whether alone or in combination, could effectively and accurately predict GB cancer among patients with localized GB wall thickening. This finding holds significance in guiding decisions regarding further diagnostic tests and treatment planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

SummarySquamous cell carcinoma (SCC) is an uncommon and frequently aggressive subtype of gallbladder cancer known for its poor outcomes compared with other gallbladder tumours. Gallbladder SCC typically presents as higher grade and more advanced than adenocarcinoma, resulting in lower estimated survival. Early recognition of these tumours is ideal, but infrequently achieved. Herein is a case of a male patient in his 80s with new onset abdominal pain who was initially diagnosed with cholecystitis, but diagnostic imaging revealed a gallbladder mass. Surgical resection and pathology revealed pure SCC of the gallbladder without local organ invasion or metastatic disease. Pure SCC histology of the gallbladder is rare, with limited studies on clinical presentation, natural history, and optimal treatment., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Nonocclusive mesenteric ischemia (NOMI) is a life-threatening disorder. Early diagnosis is challenging because NOMI lacks specific symptoms. A 52-year-old man who received extended cholecystectomy with Roux-en-Y hepaticojejunostomy for gallbladder cancer (GBC) presented to our hospital with nausea and vomiting. Neither tender nor peritoneal irritation sign was present on abdominal examination. Blood test exhibited marked leukocytosis (WBC:19,800/mm3). A contrast-enhanced abdominal computed tomography (CT) scan revealed remarkable wall thickening and lower contrast enhancement effect localized to Roux limb. On hospital day 2, abdominal arterial angiography revealed angio-spasm at marginal artery and arterial recta between 2nd jejunal artery and 3rd jejunal artery, leading us to the diagnosis of NOMI. We then administered continuous catheter-directed infusion of papaverine hydrochloride until hospital day 7. Furthermore, the patient was anticoagulated with intravenous unfractionated heparin and antithrombin agents for increasing D-dimer level and decreasing antithrombin III level. On hospital day 8, diluted oral nutrition diet was initiated and gradually advanced as tolerated. On hospital day 21, the patient was confirmed of improved laboratory test data and discharged with eating a regular diet. We experienced a rare case of NOMI on Roux limb after 2 years of extended cholecystectomy with hepaticojejunostomy for GBC, promptly diagnosed and successfully treated by interventional radiology (IVR)., (© 2024. Japanese Society of Gastroenterology.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Conflicts of interest and sources of funding: The study was supported by Key Discipline Construction Project of Shanghai Pudong New Area Health Commission (grant PWZxk2022-12), Youth Science and Technology Project of Shanghai Pudong New Area Health Commission (grant PW2023-B08). None declared to all authors., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery., Purpose: To investigate the potential of various machine learning models, incorporating radiomics and deep transfer learning, in predicting the nature of cholesterol and adenomatous gallbladder polyps., Material and Methods: A retrospective analysis was conducted on clinical and imaging data from 100 patients with cholesterol or adenomatous polyps confirmed by surgery and pathology at our hospital between September 2015 and February 2023. Preoperative contrast-enhanced CT radiomics combined with deep learning features were utilized, and t -tests and least absolute shrinkage and selection operator (LASSO) cross-validation were employed for feature selection. Subsequently, 11 machine learning algorithms were utilized to construct prediction models, and the area under the ROC curve (AUC), accuracy, and F1 measure were used to assess model performance, which was validated in a validation group., Results: The Logistic algorithm demonstrated the most effective prediction in identifying polyp properties based on 10 radiomics combined with deep learning features, achieving the highest AUC (0.85 in the validation group, 95% confidence interval = 0.68-1.0). In addition, the accuracy (0.83 in the validation group) and F1 measure (0.76 in the validation group) also indicated strong performance., Conclusion: The machine learning radiomics combined with deep learning model based on enhanced CT proves valuable in predicting the characteristics of cholesterol and adenomatous gallbladder polyps. This approach provides a more reliable basis for preoperative diagnosis and treatment of these conditions., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes., Purpose: To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging., Material and Methods: All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves., Results: In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features ( P  <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features ( P  >0.05) or between the clinical features and radiomics features ( P  >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups ( P  >0.05)., Conclusion: It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., (© 2024. The Author(s).)

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., (© 2024 Published by Elsevier Ltd.)

Endoscopic ultrasonography (EUS) provides high spatial resolution and more detailed images than other diagnostic modalities. Furthermore, EUS-guided tissue acquisition (EUS-TA), such as EUS-guided fine needle aspiration or biopsy (EUS-FNA/FNB), is an indispensable tool in pancreaticobiliary disease diagnostics, supporting a conclusive pathological diagnosis. In this review, we evaluate the current status and the usefulness of EUS-TA for the diagnostics of the following biliary tract diseases: (A) biliary stricture diagnostics, (B) biliary tract cancer (BTC) itself, and (C) staging of advanced BTC. Previous reports have shown that EUS-FNA for biliary lesions is a safe procedure that is useful in differentiating biliary cancer from benign lesions and in the staging of BTC. On the other hand, the diagnostic performance of EUS-TA for bile duct lesions is reported to be similar to that of transpapillary biopsy. Overall, EUS-TA for biliary lesions may be a safe and effective method, but it should be performed with an understanding of the risk of serious adverse events such as bile leakage and peritoneal dissemination of cancer. It is recommended for distal biliary stricture lesions for which endoscopic retrograde cholangiopancreatography cannot confirm the diagnosis or gallbladder lesions if they do not require the needle to pass through the biliary lumen., (© 2024. The Author(s), under exclusive licence to The Japan Society of Ultrasonics in Medicine.)

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

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.

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., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Aim: To evaluate apparent diffusion coefficient (ADC) and its standard deviation (SD ADC ) 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/mm 2 with a 3 T magnetic resonance imaging scanner before surgery. ADC and SD ADC 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/mm 2 (AUCs of ADC and SD ADC were 0.697 and 0.714, respectively). In patients with liver invasion, mean ADC and SD ADC of tumour-adjacent liver tissue were lower than of tumour-distant liver tissue when b = 0, 800 s/mm 2 , and = 0, 1,000 s/mm 2 (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/mm 2 ) and 0.680 (b = 0, 1,000 s/mm 2 ), and AUCs of SD ADC were 0.673 (b = 0, 800 s/mm 2 ) and 0.731 (b = 0, 1,000 s/mm 2 )., Conclusions: DWI could have potential value in preoperative predicting liver invasion by T3-staged GBC., (Copyright © 2023. Published by Elsevier Ltd.)

Background: Gallbladder adenoma represents a precancerous lesion of gallbladder cancer. However, distinguishing it from cholesteryl polyps of the gallbladder before surgery is challenging. Thus, we aimed to comprehensively explore various risk factors contributing to the formation of gallbladder adenoma to facilitate an informed diagnosis and treatment by clinicians., Methods: We conducted a retrospective analysis of patients who had undergone cholecystectomy at the Affiliated Hospital of Qingdao University between January 2015 and December 2022. Following postoperative pathological examination, patients were categorized into cholesterol polyp and adenoma groups. We analyzed their baseline characteristics, ultrasound imaging variables, and biochemical data using logistic, lasso, and stepwise regression. Subsequently, we constructed a preoperative prediction model based on the independent risk factors., Results: Regression analysis of 520 gallbladder polyps and 288 gallbladder adenomas in the model group revealed that age, gallbladder wall thickness, polyp size, echogenicity, pedunculation, and adenosine deaminase (ADA) levels were independent predictors of gallbladder adenoma, all with P < 0.05. Using these indicators, we established a regression equation: Logistic (P) = -5.615 + 0.018 ∗ age - 4.64 ∗ gallbladder wall thickness + 1.811 ∗ polyp size + 2.855 ∗ polyp echo + 0.97∗ pedunculation + 0.092 ∗ ADA. The resulting area under the curve (AUC) value was 0.894 (95 % CI: 0.872-0.917, P < 0.01), with a sensitivity of 89.20 %, specificity of 79.40 %, and overall accuracy of 84.41 % for adenoma detection., Conclusion: Age, polyp size, gallbladder wall thickness, polyp echogenicity, pedunculation, and ADA levels emerge as independent risk factors for gallbladder adenoma., Competing Interests: Declaration of competing interest The authors have no conflict of interests related to this publication., (© 2023 Published by Elsevier Ltd.)

A 50-year-old man with a history of total gastrectomy, distal pancreatectomy, splenectomy, and Roux-en-Y reconstruction was admitted to our hospital with a gallbladder tumor that had infiltrated the liver and abdominal wall. Because malignant cells were not collected during the percutaneous biopsy, we planned to perform an endoscopic ultrasound-guided fine-needle biopsy with a 22-G Franseen needle using a forward-viewing echoendoscope. Using intermittent manual compression, the forward-viewing echoendoscope reached the duodenum under fluoroscopic guidance. Endoscopic ultrasound-guided fine-needle biopsy was performed using a 22-G needle and 20-mL syringe and yielded a sufficient specimen with a single puncture. Malignant cells were promptly identified during on-site evaluation. The composition of the specimen (> 20% cancer cells and tissue area exceeding 25 mm 2 ) enabled comprehensive genomic profiling. Subsequently, high-tumor mutational burden was diagnosed based on comprehensive genomic profiling, and pembrolizumab was initiated as a second-line therapy. Even in cases involving Roux-en-Y reconstruction, endoscopic ultrasound-guided fine-needle biopsy using a forward-viewing echoendoscope can result in collection of a high-quality specimen., (© 2023. Japanese Society of Gastroenterology.)

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., (© 2024. The Author(s).)

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., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Radiologists across many imaging modalities commonly encounter gallbladder adenomyomatosis. The classic imaging appearances of gallbladder adenomyomatosis are well described and confirm benignity. However, in clinical practice, adenomyomatosis can be challenging to differentiate from other gallbladder pathologies that require cholecystectomy. In this article, we describe the common and uncommon appearances of gallbladder adenomyomatosis on multimodality imaging, helping differentiate adenomyomatosis from non-benign gallbladder abnormalities. Accurately differentiating adenomyomatosis from its mimics provides the surgical team with important clinical and surgical management information, improving patient outcomes., Competing Interests: Declaration of competing interest There are no financial or personal interests to report on behalf of all authors regarding this publication., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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%+).

A 68-year-old female patient was referred to our hospital with acute cholangitis. Computed tomography revealed common bile duct dilatation, gallbladder fundal tumor, and gallbladder wall thickening attached to the tumor. Cholangiography revealed pancreaticobiliary maljunction with biliary dilation. The patient was diagnosed with pancreaticobiliary maljunction with biliary dilation and gallbladder cancer and underwent liver S4b+5 and bile duct resection and reconstruction. Pathological results revealed that the gallbladder fundal tumor included sarcoma, and the gallbladder wall thickening had adenocarcinoma;thus, the patient was diagnosed with gallbladder carcinosarcoma.

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., Competing Interests: Declaration of competing interest The authors of this study declare no conflict of interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)