Diagnostic accuracy of imaging modalities in differentiating xanthogranulomatous cholecystitis from gallbladder cancer

Xanthogranulomatous cholecystitis: Diagnostic dilemma and surgical solution in geriatric patients: A case report

INTRODUCTION

Xanthogranulomatous Cholecystitis (XGC) is a rare inflammatory condition characterized by the presence of xanthogranulomas within the gallbladder wall, often mimicking gallbladder carcinoma (GBC). Diagnosis is challenging and may require biopsy. Once GBC is excluded, an open cholecystectomy is recommended, although laparoscopic cholecystectomy is increasingly being performed with great caution. This case report aims to evaluate clinical and radiological features, surgical outcomes, and treatment approaches for XGC.

CASE PRESENTATION

A 70-year-old patient presented with right hypochondrial pain and a palpable gallbladder. A CT scan revealed a distended lithiasic gallbladder with a thickened irregular wall and hepatic nodules. A hepatic MRI suggested xanthogranulomatous cholecystitis. A CT-guided biopsy of the liver nodule showed no signs of malignancy. An open cholecystectomy with a trans-cystic drain was performed. Histological examination confirmed chronic xanthogranulomatous cholecystitis. The patient was discharged on postoperative day 10. A clinical and radiological follow-up at 6 months postoperatively showed no abnormalities.

CLINICAL DISCUSSION

XGC presents diagnostic challenges due to its resemblance to GBC. Imaging aids in diagnosis, but biopsy may be necessary. Open cholecystectomy is the recommended surgical treatment due to excessive local inflammation and the risk of concomitant malignancy.

CONCLUSION

Managing XGC demands a holistic approach that integrates all clinical insights and mandates close collaboration among a multidisciplinary team of surgeons, radiologists, and pathologists. Further research is needed to refine diagnostic and therapeutic strategies for this rare condition, especially in geriatric patients.

Should we suspect gallbladder cancer if which CT finding is observed in patients with localized gallbladder wall thickening?

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.

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.

Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound

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.

The utility of systemic immune inflammatory index in discriminating between gallbladder cancer and xanthogranulomatous cholecystitis: A single-tertiary center experience

Xanthogranulomatous cholecystitis (XGC) and gallbladder carcinoma (GBC) are rare diseases with several similarities. This study aimed to evaluate the utility of the systemic immune inflammatory index (SII), a novel index that more accurately depicts inflammatory and immunological balance, in distinguishing between XGC and GBC. This retrospective study included 33 XGC, 22 GBC patients diagnosed according to histopathological findings and 33 age-sex-matched healthy controls at Hacettepe University Faculty of Medicine, General Surgery Department. The demographic, clinical and laboratory findings were recorded. Neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, monocyte to lymphocyte ratio and SII were calculated from preoperative complete blood count parameters. The receiver operating characteristic curve was performed to evaluate the utility of SII in differentiating GBC and XGC. A P value < .05 was accepted as statistically significant. The preoperative neutrophil to lymphocyte ratio, monocyte to lymphocyte ratio, platelet to lymphocyte ratio and SII were significantly higher in patients with GBC compared to XGC patients and healthy controls (P < .001, P = .001, P = .001, P < .001, respectively). When receiver operating characteristic analysis was made, the optimal cutoff value of SII was 640 for differential diagnosis of XGC and GBC preoperatively with a sensitivity of 77.3% and a specificity of 66.7%, among which the positive likelihood ratio was 2.32, and Youden index was 0.44 (P = .006). The positive predictive value was 60.7%, the negative predictive value was 81.5%, and the diagnostic accuracy was 79.9%. SII may be a valuable, practical, and affordable method to differentiate between XGC and GBC, in addition to clinical and radiological signs, prior to surgery. When supported by prospective trials with a larger study population, distinguishing GBC from XGC using SII preoperatively may lead to a change in the management practice of GBC.

Preoperative Diagnosis of Xanthogranulomatous Cholecystitis

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Utility and limitations of intraoperative frozen section diagnosis to determine optimal surgical strategy in suspected gallbladder malignancy

BACKGROUND

Preoperative diagnosis of gallbladder cancer (GBC) remains a challenge. Unwarranted extensive surgery for benign disease and undertreatment for GBC pose challenges. We aimed to analyze the utility, diagnostic accuracy, and limitations of intraoperative frozen section (FS), for primary diagnosis of suspected gallbladder malignancy.

METHODS

Patients with suspected GBC underwent a cystic-plate cholecystectomy and FS for primary diagnosis. The procedure was considered adequate if FS suggested a benign pathology. A radical cholecystectomy was performed if FS favoured GBC, or in patients with high intra-operative suspicion of malignancy. All FS records were compared with final histopathology.

RESULTS

FS guided the surgical strategy in 491 of 575 resections (85.4%). FS had a sensitivity of 88.3%, specificity of 99.6%, a positive predictive value of 99.4% and a negative predictive value of 92.7%. The diagnostic accuracy of FS was 95.1%. With routine use of intraoperative FS, only 10 out of 491 patients (2%) required a revised surgical strategy.

CONCLUSIONS

For radiologically suspected GBC it is prudent to confirm the histological diagnosis by use of intraoperative FS before undertaking radical resections. This study emphasizes the safety and accuracy of FS as an adjunct for directing optimal surgical strategy in suspected GBC.

Multiparameter diagnostic model based on 18F-FDG PET metabolic parameters and clinical variables can differentiate nonmetastatic gallbladder cancer and cholecystitis

OBJECTIVE

To evaluate the diagnostic value of a multiparameter model based on ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) metabolic parameters and clinical variables in differentiating nonmetastatic gallbladder cancer (GBC) from cholecystitis.

PATIENTS AND METHODS

In total, 122 patients (88 GBC nonmetastatic patients and 34 cholecystitis patients) with gallbladder space-occupying lesions who underwent ¹⁸F-FDG PET/CT were included. All patients received surgery and pathology, and baseline characteristics and clinical data were also collected. The metabolic parameters of ¹⁸F-FDG PET, including SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), SUVpeak (peak standard uptake value), MTV (metabolic tumour volume), TLG (total lesion glycolysis) and SUVR (tumour-to-normal liver standard uptake value ratio), were evaluated. The differential diagnostic efficacy of each independent parameter and multiparameter combination model was evaluated using the receiver operating characteristic (ROC) curve. The improvement in diagnostic efficacy using a combination of the above multiple parameters was evaluated by integrated discriminatory improvement (IDI), net reclassification improvement (NRI) and bootstrap test. Decision curve analysis (DCA) was used to evaluate clinical efficacy.

RESULTS

The ROC curve showed that SUVR had the highest diagnostic ability among the ¹⁸F-FDG PET metabolic parameters (area under the curve [AUC] = 0.698; sensitivity = 0.341; specificity = 0.971; positive predictive value [PPV] = 0.968; negative predictive value [NPV] = 0.363). The combined diagnostic model of cholecystolithiasis, fever, CEA > 5 ng/ml and SUVR showed an AUC of 0.899 (sensitivity = 0.909, specificity = 0.735, PPV = 0.899, NPV = 0.758). The diagnostic efficiency of the model was improved significantly compared with SUVR. The clinical efficacy of the model was confirmed by DCA.

CONCLUSIONS

The multiparameter diagnostic model composed of ¹⁸F-FDG PET metabolic parameters (SUVR) and clinical variables, including patient signs (fever), medical history (cholecystolithiasis) and laboratory examination (CEA > 5 ng/ml), has good diagnostic efficacy in the differential diagnosis of nonmetastatic GBC and cholecystitis.

RadFormer: Transformers with global-local attention for interpretable and accurate Gallbladder Cancer detection

We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architecture for highly accurate Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our experiments indicate that the detection accuracy of our model beats even human radiologists, and advocates its use as the second reader for GBC diagnosis. Bag of words embeddings allow our model to be probed for generating interpretable explanations for GBC detection consistent with the ones reported in medical literature. We show that the proposed model not only helps understand decisions of neural network models but also aids in discovery of new visual features relevant to the diagnosis of GBC. Source-code is available at https://github.com/sbasu276/RadFormer.

Computed diffusion-weighted magnetic resonance imaging with high b-values in the diagnosis of gallbladder lesions

PURPOSE

The diagnosis of gallbladder lesions remains challenging. The efficacy of computed diffusion-weighted imaging (DWI) with high b-values and apparent diffusion coefficient (ADC) for the diagnosis of gallbladder cancer remains unknown. We aimed to investigate the usefulness of computed DWI with high b-values and the combination of computed DWI and ADC in differentiating malignant and benign gallbladder lesions.

METHODS

Sixty patients (comprising 30 malignant and 30 benign lesions) who underwent magnetic resonance imaging for gallbladder lesions were included in this retrospective study. Qualitative evaluations were performed using conventional DWI with b1000, computed DWI with b1500, b1000 DWI/ADC, and computed b1500 DWI/ADC, and their diagnostic performances were compared.

RESULTS

The sensitivity, specificity, and accuracy of computed b1500 DWI/ADC were 90% (27/30), 80% (24/30), and 85% (51/60), respectively. The accuracy of computed b1500 DWI/ADC was higher than that of conventional b1000 DWI (52%, 31/60, p < 0.001), computed b1500 DWI (72%, 43/60, p = 0.008), and b1000 DWI/ADC (78%, 47/60, p = 0.125). The specificity of computed b1500 DWI/ADC was also higher than that of conventional b1000 DWI (7%, 2/30, p < 0.001), computed b1500 DWI (47%, 14/30, p = 0.002), and b1000 DWI/ADC (67%, 20/30, p = 0.125). No significant difference was observed in the sensitivity between the groups.

CONCLUSION

This study shows that computed DWI with high b-values combined with ADC can improve diagnostic performance when differentiating malignant and benign gallbladder lesions. Computed diffusion-weighted magnetic resonance imaging with high b-values in the diagnosis of gallbladder lesions. *Computed DWI with b1500 combined with ADC can improve diagnostic performance when differentiating gallbladder lesions compared with conventional methods (b1000 DWI).

Differential diagnoses of gallbladder tumors using CT‐based deep learning

Background

The differential diagnosis between gallbladder cancer (GBC) and xanthogranulomatous cholecystitis (XGC) remains quite challenging, and can possibly lead to improper surgery. This study aimed to distinguish between XGC and GBC by combining computed tomography (CT) images and deep learning (DL) to maximize the therapeutic success of surgery.

Methods

We collected a dataset, including preoperative CT images, from 28 cases of GBC and 21 XGC patients undergoing surgery at our facility. It was subdivided into training and validation (n = 40), and test (n = 9) datasets. We built a CT patch‐based discriminating model using a residual convolutional neural network and employed 5‐fold cross‐validation. The discriminating performance of the model was analyzed in the test dataset.

Results

Of the 40 patients in the training dataset, GBC and XGC were observed in 21 (52.5%), and 19 (47.5%) patients, respectively. A total of 61 126 patches were extracted from the 40 patients. In the validation dataset, the average sensitivity, specificity, and accuracy were 98.8%, 98.0%, and 98.5%, respectively. Furthermore, the area under the receiver operating characteristic curve (AUC) was 0.9985. In the test dataset, which included 11 738 patches, the discriminating accuracy for GBC patients after neoadjuvant chemotherapy (NAC) (n = 3) was insufficient (61.8%). However, the discriminating model demonstrated high accuracy (98.2%) and AUC (0.9893) for cases other than those receiving NAC.

Conclusion

Our CT‐based DL model exhibited high discriminating performance in patients with GBC and XGC. Our study proposes a novel concept for selecting the appropriate procedure and avoiding unnecessary invasive measures.

Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis

Background

Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making.

Methods

We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model.

Results

Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC.

Conclusions

The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.

Asymptomatic chronic suppurative cholecystitis and peritonitis mimicking metastasis by 18F-FDG PET/CT scan during sigmoid colon cancer surveillance

The study describes an unusual case that a patient with previous history of adenocarcinoma of sigmoid colon who has developed chronic suppurative cholecystitis and peritonitis was misdiagnosed as metastasis. This case is presented to illustrate the importance of considering benign etiologies that may mimic metastatic disease when interpreting positron emmision tomography (PET)/CT scans.

Contrast enhanced ultrasound versus multiphasic contrast enhanced computed tomography in evaluation of gallbladder lesions

AIM

To compare the diagnostic performance of contrast enhanced ultrasound (CEUS) and multiphasic contrast enhanced computed tomography (CECT) in differentiating benign and malignant gallbladder (GB) lesions.

METHODS

This prospective ethical review board approved study comprised consecutive patients with GB lesions who underwent CEUS and multiphasic CECT at a tertiary care referral center. The enhancement patterns at CEUS and CT were compared. The quantitative CEUS parameters including arrival time (AT), AT in liver, time to peak enhancement, and washout time (WT) were assessed. The diagnostic performance of CEUS and CT features was calculated using receiver operating characteristic analysis. A subgroup analysis was performed for patients with GB wall thickening. Multivariate analysis was performed to identify features significantly associated with malignancy.

RESULTS

Over the study period, 30 patients (mean age, 52.8 ± 12.2 years, 17 females) with GB lesions were evaluated. Benign and malignant diseases were present in 13 and 17 patients, respectively. There was excellent agreement between CEUS and CT findings. Among the quantitative CEUS features, only WT was significantly associated with malignancy in the overall group (p < 0.001) and wall thickening subgroup (p = 0.007). WT within 53.5 s and 51.5 s had sensitivity of 88.2% and 81.8% and specificity of 84.5% and 100% in diagnosing malignant lesions in the overall group (AUC 0.900) and the wall thickening subgroup (area under curve, AUC 0.927), respectively. At multivariate analysis, features that were significantly associated with malignant lesions in the overall group were disruption of GB wall (CEUS), intralesional non-enhancing areas (CEUS), liver involvement (CEUS or CT), and arterial phase hyperenhancement (CT) in the overall group and disruption of GB wall (CEUS), WT (CEUS), and liver involvement (CEUS or CT) in the wall thickening subgroup.

CONCLUSION

CEUS is a useful adjunct to CT in evaluation of GB lesions. Its utilization in patients with GB wall thickening may improve detection of malignancy.

Grayscale ultrasonography findings for characterization of gallbladder wall thickening in non-acute setting: a systematic review and meta-analysis

BACKGROUND

The accurate characterization of gallbladder wall thickening (GWT) into benign or malignant on ultrasound (US) is a significant challenge.

METHODS

We searched the MEDLINE and EMBASE databases for studies reporting two-dimensional grayscale US in benign and malignant GWT. The pooled prevalence was calculated using a generalized linear mixed method with a random-effects model. The pooled sensitivity and specificity were calculated using a bivariate random-effects model.

RESULTS

Of the 7309 studies screened by titles, 73 studies with 18,008 patients were included. The most common findings in xanthogranulomatous cholecystitis (XGC) were lack of wall disruption and intramural hypoechoic nodules while adenomyomatosis (ADM) was frequently associated with intramural cysts and intramural echogenic foci. Echogenic foci, lack of gallbladder wall disruption, and hypoechoic nodules had a sensitivity of 89%, 77%, and 66% and specificity of 86%, 51%, and 80%, respectively for the diagnosis of benign GWT. Focal thickening and indistinct liver interface had a sensitivity of 75% and 55% and specificity of 64% and 69%, respectively for the diagnosis of malignant GWT.

CONCLUSION

intramural features (echogenic foci, hypoechoic nodules), gallbladder wall disruption, and liver interface are useful US features for the characterization of GWT.

PET/CT Variants and Pitfalls in Liver, Biliary Tract, Gallbladder and Pancreas

A wide variety of pathological anomalies may occur in the liver, biliary system, and pancreas. It is a necessity to use many different imaging techniques in order to distinguish such varied pathologies, especially those from malignant processes. Positron Emission Tomography/Computed Tomography (PET/CT) is an imaging method that has proven its diagnostic value in oncology and can be used for different clinical purposes. Fluoro-18 fluoro-2-deoxy-D-glucose has a wide range of uses as a dominant radiopharmaceutical in routine molecular imaging, however, molecular imaging has started to play a more important role in personalized cancer treatment in recent years with new Fluoro-18 and Gallium-68 labeled tracers. Although molecular imaging has a strong diagnostic effect, the surprises and pitfalls of molecular imaging can lead us to unexpected and misleading results. Prior to PET/CT analysis and reporting, information about possible technical and physiological pitfalls, normal histological features of tissues, inflammatory pathologies, specific clinical features of the case, treatment-related complications and past treatments should be evaluated in advance to avoid misinterpretation. In this review, the physiological and pathophysiological variants as well as pitfalls encountered in PET/CT imaging of the liver, biliary tract, gallbladder, and pancreas will be examined. Other benign and malignant pathologies that have been reported to date and that have led to incorrect evaluation will be listed. It is expected that the devices, software, and artificial intelligence applications that will be developed in the near future will enable much more effective and faster imaging that will reduce the potential causes of error. However, as a result of the dynamic and evolving structure of the information obtained by molecular imaging, the inclusion of the newly developed radiopharmaceuticals in routine practice will continue to carry new potentials as well as new troubles. Although molecular imaging will be the flagship of diagnostic oncology in the 21st century, the correct analysis and interpretation by the physician will continue to form the basis of achieving optimal performance.

Xanthogranulomatous cholecystitis mimicking gallbladder cancer on 18F-fluorodeoxyglucose positron emission tomography/computed tomography scan

The abnormal thickening of the gallbladder (GB) wall can be caused by a malignant condition like gallbladder carcinoma or by benign lesions such as chronic cholecystitis or xanthogranulomatous cholecystitis (XGC). Mural thickening is a common finding between them as fluorodeoxyglucose (FDG) can be taken up by inflammatory cells also. Here, we present a patient with irregular thickening of the GB wall which was suspected to of GB carcinoma since FDG positron emission tomography/computed tomography scan showed increased tracer uptake in the lesion. However, after surgery the histopathological report was suggestive of XGC.

Imaging-based algorithmic approach to gallbladder wall thickening

Gallbladder (GB) wall thickening is a frequent finding caused by a spectrum of conditions. It is observed in many extracholecystic as well as intrinsic GB conditions. GB wall thickening can either be diffuse or focal. Diffuse wall thickening is a secondary occurrence in both extrinsic and intrinsic pathologies of GB, whereas, focal wall thickening is mostly associated with intrinsic GB pathologies. In the absence of specific clinical features, accurate etiological diagnosis can be challenging. The survival rate in GB carcinoma (GBC) can be improved if it is diagnosed at an early stage, especially when the tumor is confined to the wall. The pattern of wall thickening in GBC is often confused with benign diseases, especially chronic cholecystitis, xanthogranulomatous cholecystitis, and adenomyomatosis. Early recognition and differentiation of these conditions can improve the prognosis. In this minireview, the authors describe the patterns of abnormalities on various imaging modalities (conventional as well as advanced) for the diagnosis of GB wall thickening. This paper also illustrates an algorithmic approach for the etiological diagnosis of GB wall thickening and suggests a formatted reporting for GB wall abnormalities.

Xanthogranulomatous cholecystitis: experience in 100 cases

Background

Xanthogranulomatous cholecystitis (XGC) is a rare presentation of chronic cholecystitis, characterized by xanthogranuloma, severe foam cells and fibrosis, and can be an inducement of difficulty in cholecystectomy. The purpose of this study was to review the clinical findings and imageology features of XGC and to optimize the treatment option.

Methods

This retrospective study collected clinical symptoms, demographics, imageology, operation records, histopathological findings, and postoperative complications of 100 patients with XGC after evaluating 50005 cholecystectomy specimens between 2009 and 2018 in a single institute. heir clinical symptoms, demographics, imageology, operation records, histopathological findings, and postoperative complications were collected and analyzed.

Results

Patients showed various clinical symptoms, ultrasonography was performed in all patients, CT and MRI were further arranged selectively before the operation, but none of the patients were prediagnosed. Fifty-two patients received open cholecystectomy. Laparoscopic cholecystectomy (LC) was planned in 48 patients within whom 8 cases were converted to open cholecystectomy. No partial cholecystectomy was performed. The intraoperative findings included cholecystolithiasis, choledocholithiasis, thickened gallbladder wall, lesions infiltrating into adjacent tissues, disordered Calot’s triangle anatomy, enlarged regional lymph nodes, internal gallbladder fistula, and hepatic abscesses. Frozen-section analysis was performed in 48 patients under the suspicion of gallbladder carcinoma (GBCa), but only 2 cases were finally confirmed.

Conclusions

The preoperative diagnosis of XGC was challenging. Open cholecystectomy was the most preferred treatment, and conversion to open was often necessary after LC.