Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology

Digital pathology(DP)and its subsidiaries including artificial intelligence(AI)are rapidly making inroads into the area of diagnostic anatomic pathology(AP)including gastrointestinal(GI)pathology.It is poised to revolutionize the field of diagnostic AP.Historically,AP has been slow to adopt digital technology,but this is changing rapidly,with many centers worldwide transitioning to DP.Coupled with advanced techniques of AI such as deep learning and machine learning,DP is likely to transform histopathology from a subjective field to an objective,efficient,and transparent discipline.AI is increasingly integrated into GI pathology,offering numerous advancements and improvements in overall diagnostic accuracy,efficiency,and patient care.Specifically,AI in GI pathology enhances diagnostic accuracy,streamlines workflows,provides predictive insights,integrates multimodal data,supports research,and aids in education and training,ultimately improving patient care and outcomes.This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology.The main aim was to provide updates and create awareness among the pathology community.

NLRP3-mediated autophagy dysfunction links gut microbiota dysbiosis to tau pathology in chronic sleep deprivation

Emerging evidence indicates that sleep deprivation(SD)can lead to Alzheimer’s disease(AD)-related pathological changes and cognitive decline.However,the underlying mechanisms remain obscure.In the present study,we identified the existence of a microbiota-gut-brain axis in cognitive deficits resulting from chronic SD and revealed a potential pathway by which gut microbiota affects cognitive functioning in chronic SD.Our findings demonstrated that chronic SD in mice not only led to cognitive decline but also induced gut microbiota dysbiosis,elevated NLRP3 inflammasome expression,GSK-3βactivation,autophagy dysfunction,and tau hyperphosphorylation in the hippocampus.Colonization with the“SD microbiota”replicated the pathological and behavioral abnormalities observed in chronic sleep-deprived mice.Remarkably,both the deletion of NLRP3 in NLRP3-/-mice and specific knockdown of NLRP3 in the hippocampus restored autophagic flux,suppressed tau hyperphosphorylation,and ameliorated cognitive deficits induced by chronic SD,while GSK-3βactivity was not regulated by the NLRP3 inflammasome in chronic SD.Notably,deletion of NLRP3 reversed NLRP3 inflammasome activation,autophagy deficits,and tau hyperphosphorylation induced by GSK-3βactivation in primary hippocampal neurons,suggesting that GSK-3β,as a regulator of NLRP3-mediated autophagy dysfunction,plays a significant role in promoting tau hyperphosphorylation.Thus,gut microbiota dysbiosis was identified as a contributor to chronic SD-induced tau pathology via NLRP3-mediated autophagy dysfunction,ultimately leading to cognitive deficits.Overall,these findings highlight GSK-3βas a regulator of NLRP3-mediated autophagy dysfunction,playing a critical role in promoting tau hyperphosphorylation.

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst(OKC)is a common jaw cyst with a high recurrence rate.OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome.Moreover,OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts.Because of the different prognosis,differential diagnosis of several cysts can contribute to clinical management.We collected 519 cases,comprising a total of 2157 hematoxylin and eosinstained images,to develop digital pathology-based artificial intelligence(AI)models for the diagnosis and prognosis of OKC.The Inception_v3 neural network was utilized to train and test models developed from patch-level images.Finally,whole slide imagelevel AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms.The AI models showed great performance in the diagnosis(AUC=0.935,95%CI:0.898–0.973)and prognosis(AUC=0.840,95%CI:0.751–0.930)of OKC.The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model.Furthermore,the study investigates the correlation between AI features generated by deep learning and pathological findings,highlighting the interpretative potential of AI models in the pathology.Here,we have developed the robust diagnostic and prognostic models for OKC.The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

Going straight for the gut:gut-brain axis pathology and treatment of Parkinson's disease

This perspective focuses on the recent literature regarding the role of the gut-brain axis(GBA) in fecal microbiota transplantation(FMT) and stem cell therapy(SCT) in Parkinson's disease(PD).PD is the second most common neurodegenerative disease in the United States,yet therapies remain limited.Current research suggests that the GBA may play a role in the pathogenesis of PD.GBAbased FMT as well as SCT offer promising new avenues for PD treatment.Pro bing the interactions between FMT and SCT with the GBA may reveal novel therapeutics for PD.

Using MsfNet to Predict the ISUP Grade of Renal Clear Cell Carcinoma in Digital Pathology Images

Clear cell renal cell carcinoma(ccRCC)represents the most frequent form of renal cell carcinoma(RCC),and accurate International Society of Urological Pathology(ISUP)grading is crucial for prognosis and treatment selection.This study presents a new deep network called Multi-scale Fusion Network(MsfNet),which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images.The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity.The model was trained and tested using 90 Hematoxylin and Eosin(H&E)stained whole slide images(WSIs),which were all cropped into 320×320-pixel patches at 40×magnification.MsfNet achieved a micro-averaged area under the curve(AUC)of 0.9807,a macro-averaged AUC of 0.9778 on the test dataset.The Gradient-weighted Class Activation Mapping(Grad-CAM)visually demonstrated MsfNet’s ability to distinguish and highlight abnormal areas more effectively than ResNet50.The t-Distributed Stochastic Neighbor Embedding(t-SNE)plot indicates our model can efficiently extract critical features from images,reducing the impact of noise and redundant information.The results suggest that MsfNet offers an accurate ISUP grade of ccRCC in digital images,emphasizing the potential of AI-assisted histopathological systems in clinical practice.

Amyloid-beta pathology-induced nanoscale synaptic disruption:the case of the GABA_B-GIRK assembly

Alzheimer's disease (AD) is characterized by an imbalance between excitatory and inhibitory brain networks,leading to aberrant homeostatic synaptic plasticity.AD has progressively been recognized as syna ptopathy and syna ptic dysfunction has been identified as a key component of its pathogenesis (Schirinzi et al.,2020).Syna ptic dysfunction is believed to precede synapse loss,a primary biological correlate of cognitive decline in AD,inevita bly associated with neuronal death.

Hysterectomies for Gynaecological Pathology: 56 Cases at the Segou Regional Hospital in Mali

Introduction: Hysterectomy is a surgical procedure involving partial or total removal of the uterus. It is the most common gynaecological surgery in the world. Objective: To describe the epidemio-clinical and prognostic aspects of gynaecological hysterectomies. Patients and methods: This was an 18-month retrospective prospective descriptive study with a six-month follow-up period from 1 December 2020 to 31 May 2022 carried out in the gynaecology department of the Segou regional hospital. Results: Fifty-six (56) hysterectomies were performed out of 118 gynaecological surgical procedures (47.45%). The mean age was 47 ± 11.77 years. Large multiparous women were the most common (50%), with an average parity of 4.58. The main indications were uterine fibroids (30.4%), precancerous lesions of the cervix (17.85%) and uterine prolapse (17.85%). The abdominal route was the most commonly used surgical route (82.14%). Hysterectomy was total in 100% of cases and associated with bilateral adnexectomy in 48.2% of cases. The intra- and post-operative prognosis was satisfactory in 94.6% of cases. No deaths were recorded. The average length of stay was 3.28 days, irrespective of the surgical approach. Three cases of dyspareunia were noted among those who had resumed sexual activity.

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

Microvascular structural changes in esophageal squamous cell carcinoma pathology according to intrapapillary capillary loop types under magnifying endoscopy

BACKGROUND The intrapapillary capillary loop(IPCL)characteristics,visualized using magnifying endoscopy,are commonly assessed for preoperative evaluation of the infiltration depth of esophageal squamous cell carcinoma(ESCC).Japan Esophageal Society(JES)classification is the most widely used classification.Microvascular structural changes are evaluated by magnifying endoscopy for the presence or absence of each morphological factor:tortuosity,dilatation,irregular caliber,and different shapes.However,the pathological characteristics of IPCLs have not been thoroughly investigated,especially the microvascular structures corresponding to the deepest parts of the lesions'infiltration.AIM To investigate differences in pathological microvascular structures of ESCC,which correspond to the deepest parts of the lesions'infiltration.METHODS Patients with ESCC and precancerous lesions diagnosed at Peking University Third Hospital were enrolled between January 2019 and April 2023.Patients first underwent magnified endoscopic examination,followed by endoscopic submucosal dissection or surgical treatment.Pathological images were scanned using a threedimensional slice scanner,and the pathological structural differences in different types,according to the JES classification,were analyzed using nonparametric tests and t-tests.RESULTS The 35 lesions were divided into four groups according to the JES classification:A,B1,B2,and B3.Statistical analyses revealed significant differences(aP<0.05)in the short and long calibers,area,location,and density between types A and B.Notably,there were no significant differences in these parameters between types B1 and B2 and between types B2 and B3(P>0.05).However,significant differences in the short calibers,long calibers,and area of IPCL were observed between types B1 and B3(aP<0.05);no significant differences were found in the density or location(P>0.05).CONCLUSION Pathological structures of IPCLs in the deepest infiltrating regions differ among various IPCL types classified by the JES classification under magnifying endoscopy,especially between the types A and B.

Axial length,vitreoretinal pathology,and anterior chamber depth can predict postoperative refractive outcomes in phacovitrectomy/silicone oil removal

AIM:To evaluate the postoperative refractive prediction error(PE)and determine the factors that af fect the refractive outcomes of combined pars plana vitrectomy(PPV)or silicone oil removal(SOR)with cataract surgery.METHODS:The study is a retrospective,case-series study.Totally 301 eyes of 301 patients undergoing combined PPV/SOR with cataract surgery were enrolled.Eligible individuals were separated into four groups according to their preoperative diagnoses:silicone oil-filled eyes after PPV(group 1),epiretinal membrane(group 2),macular hole(group 3),and primary retinal detachment(RD;group 4).The variables af fecting postoperative refractive outcomes were analyzed,including age,gender,preoperative best-corrected visual acuity(BCVA),axial length(AL),keratometry average,anterior chamber depth(ACD),intraocular tamponade,and vitreoretinal pathology.The outcome measurements include the mean refractive PE and the proportions of eyes with a PE within±0.50 diopter(D)and±1.00 D.RESULTS:For all patients,the mean PE was-0.04±1.17 D,and 50.17%of patients(eyes)had a PE within±0.50 D.There was a significant difference in refractive outcomes among the four groups(P=0.028),with RD(group 4)showing the least favorable refractive outcome.In multivariate regression analysis,only AL,vitreoretinal pathology,and ACD were strongly associated with PE(all P<0.01).Univariate analysis revealed that longer eyes(AL>26 mm)and a deeper ACD were correlated with hyperopic PE,and shorter eyes(AL<26 mm)and a shallower ACD were correlated with myopic PE.CONCLUSION:RD patients have the least favorable refractive outcome.AL,vitreoretinal pathology,and ACD are strongly associated with PE in the combined surgery.These three factors affect refractive outcomes and thus can be used to predict a better postoperative refractive outcome in clinical practice.

Aquaporin 5 in Alzheimer’s disease:a link between oral and brain pathology?

The involvement of aquaporins(AQPs)in the development of diseases has been widely described(Azad et al.,2021).AQP5 has been described in astrocytes changing after traumatic brain injuries(Chai et al.,2013),but the precise role of AQP5 in Alzheimer’s disease(AD)pathology is yet to be understood.We have recently reported that AQP5 expression changes during the development of AD(Antequera et al.,2022).The AQP5 expression in salivary glands is decreased in 6-month-old APP/PS1 mice and AD patients.This decrease in AQP5 expression could be involved in the mechanism of salivary gland dysfunction described in a previous study(Antequera et al.,2021).Now,we propose a new indirect role of AQP5 in the connection between infection-induced oral dysbiosis and AD(Sureda et al.,2020).Here,we suggest that the proinflammatory response induced by oral pathogen infection results in the downregulation of AQP5 contributing to the salivary gland secretory dysfunction.All these alterations destabilize the peripheral immune-inflammatory balance and exacerbate neuroinflammation and neurodegeneration leading to AD pathology.

Effects of alendronate on bone mass and organ pathology in ovariectomized mice

Objective:To investigate the effect of alendronate on bone mass and organ pathology of ovariectomized mice.Methods:Thirty SPF grade C57 female mice were randomly divided into three groups(n=10):Sham operation group(Sham),ovariectomized group(OVX)and ovariectomized+alendronate group(ALN).The sodium alendronate was injected subcutaneously at 400μg/kg twice a week in the ALN group.The equal volume of normal saline was injected subcutaneously twice a week in the SHAM group and OVX group.After 12 weeks of drug administration,the samples were taken.The organ coefficients,main organ pathological sections,and bone histopathological sections were observed,and the micro CT,L4 biomechanics and serum biochemical indicators were analyzed.Results:The uterine coefficient of Sham group was(0.0054±0.0007)significantly higher than that of OVX group(0.0026±0.0009)and ALN group(0.0025±0.0007),and the difference was statistically significant(P<0.05).No obvious lesions or toxic or side effects were observed in the main organs.Compared with the OVX group,the ALN group with decalcified sections of bone tissue had compact trabecular structure and fewer adipocytes.Micro-CT results showed that the Tb.BMD,Tb.N,Tb.Th and Tb.BV/TV values of the ALN group were significantly increased compared with those of the OVX group,but the Tb.Sp value was significantly decreased,and the difference was statistically significant(P<0.05).In L4 vertebral body biomechanics,the elastic modulus(50.29±13.43)and maximum load number(29.83±4.92)of ALN group were significantly higher than those of OVX group(14.77±3.12)and maximum load number(11.57±3.18),and the difference was statistically significant(P<0.05).Compared with the OVX group,the serum OCN and PINP indicators of bone formation in the ALN group were increased,while the bone resorption indicators TRACP-5b and CTX-I were decreased,with statistical significance(P<0.05).Conclusion:Alendronate sodium improves bone quality by increasing bone density,improving bone microstructure,increasing bone strength,promoting bone formation and inhibiting bone resorption,without obvious toxic and side effects on organs.

Evolution of human kidney allograft pathology diagnostics through 30 years of the Banff classification process

The second half of the previous century witnessed a tremendous rise in the number of clinical kidney transplants worldwide.This activity was,however,accompanied by many issues and challenges.An accurate diagnosis and appropriate management of causes of graft dysfunction were and still are,a big challenge.Kidney allograft biopsy played a vital role in addressing the above challenge.However,its interpretation was not standardized for many years until,in 1991,the Banff process was started to fill this void.Thereafter,regular Banff meetings took place every 2 years for the past 30 years.Marked changes have taken place in the interpretation of kidney allograft biopsies,diagnosis,and classification of rejection and other non-rejection pathologies from the original Banff 93 classification.This review attempts to summarize those changes for increasing the awareness and understanding of kidney allograft pathology through the eyes of the Banff process.It will interest the transplant surgeons,physicians,pathologists,and allied professionals associated with the care of kidney transplant patients.

Transitioning of renal transplant pathology from allograft to xenograft and tissue engineering pathology:Are we prepared?

Currently,the most feasible and widely practiced option for patients with endstage organ failure is the transplantation of part of or whole organs,either from deceased or living donors.However,organ shortage has posed and is still posing a big challenge in this field.Newer options being explored are xenografts and engineered/bioengineered tissues/organs.Already small steps have been taken in this direction and sooner or later,these will become a norm in this field.However,these developments will pose different challenges for the diagnosis and management of problems as compared with traditional allografts.The approach to pathologic diagnosis of dysfunction in these settings will likely be significantly different.Thus,there is a need to increase awareness and prepare transplant diagnosticians to meet this future challenge in the field of xenotransplantation/regenerative medicine.This review will focus on the current status of transplant pathology and how it will be changed in the future with the emerging scenario of routine xenotransplantation.

Preliminary Exploration of All-English Teaching in Pathology for International Students

With the development of medical education in China, the education of international students has gradually become an important part of China’s higher education. Pathology is a bridge course connecting basic medicine and clinical medicine, and is a compulsory course for international medical students. In order to improve the quality of all-English teaching in pathology, according to the characteristics of international students and the discipline characteristics of pathology, this paper discussed the problems existing in the pathology teaching of international students, such as language communication, teacher training, textbook selection, teaching content arrangement, etc., aiming at exploring ways to solve these problems and improve the learning effect of international students from teaching practice.

Collision tumor of primary malignant lymphoma and adenocarcinoma in the colon diagnosed by molecular pathology:A case report and literature review

BACKGROUND Collision tumors of primary malignant lymphoma and adenocarcinoma in the colon are rare.Primary diffuse large B-cell lymphoma(DLBCL)–adenocarcinoma collision tumors are especially rare.CASE SUMMARY A 74-year-old woman presented with abdominal pain of 1 mo duration.Biopsy under colonoscopy revealed adenocarcinoma of the ascending colon.Subsequently,the patient underwent laparoscopic radical resection of right colon cancer with lymph node dissection.A collision tumor was found incidentally through postoperative pathological sampling.Genetic analysis showed a collision tumor of DLBCL with germinal center B-cell subtype and TP53 mutation,and adenocarcinoma arising in a tubulovillous adenoma in the colon,with BRAF mutation and mutL homolog 1 promoter methylation.The patient died 3 mo after surgery.To our knowledge,this is the 23rd reported case of collision tumor of colorectal adenocarcinoma and lymphoma.The mean age of the 23 patients was 73 years.The most common site was the cecum.There were 15 cases with followup data including 11 living and four dead with a 3-year overall survival rate of 71.5%.CONCLUSION Based on pathological and genetic analysis,surgery combined with chemotherapy or chemoradiotherapy may have good therapeutic effects for collision tumor.

Cavitary Pulmonary Metastases: CT Features and Their Correlation with the Pathology of the Primary Malignancy

Objective: To study CT features of cavitary pulmonary metastases and to investigate the pos- sible relationship between CT features and the pathology of the primary lesions. Methods: CT ?ndings of 131 cavitary metastatic nodules in 40 patients with pathologically-proved pulmonary metastases were retrospectively analyzed. A comparison between CT signs and the pathologic types of the primary tumors was made. Results: Cavitary metastases and multiple solid nodules coexisted in all patients. Cavitary metastases presented as bubble (n=41), irregular (n=33), cystic (n=26) or small circular (n=31) cavities, with even (n=61) or uneven (n=70) thickness of the cavity wall. Of 131 cavitary nodules, diameter less than 15 mm was seen in 44, between 15–25 mm in 66, 25–40 mm in 17 and larger than 40 mm in 4 respectively. And the wall thickness of the cavity below 4 mm, between 4–15 mm and over 15 mm was respectively seen in 69, 44 and 18 metastatic nodules. Cavitary pulmonary metastases mainly occurred in patients whose primary malignancy was squamous cell carcinoma (n=13) or adenocarcinoma (n=22). Both squamous cell carcinoma and adenocarcinoma had its own CT characteristics. The occurrence of cavity bore no relationship to its site in the lung. Conclusion: Cavitary pulmonary metastases carries certain CT features and its occurrence is related to the pathologic type of the primary malignancy.

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

BACKGROUND Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin(H&E) sections in diverse tumors including colorectal cancers(CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time-and cost-effective complementary method for personalized treatment.AIM To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images(WSIs) with deep learning-based classifiers.METHODS A total of 629 CRC patients from The Cancer Genome Atlas(TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital(SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC, KRAS, PIK3CA, SMAD4, and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic(ROC) curves and area under the curves(AUCs) for all the classifiers were presented.RESULTS The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs.The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved.CONCLUSION APC, KRAS, PIK3CA, SMAD4, and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.

Biobanking in the digital pathology era

Digital Pathology is becoming more and more important to achieve the goal of precision medicine.Advances in whole-slide imaging,software integration,and the accessibility of storage solutions have changed the pathologists’clinical practice,not only in terms of laboratory workflow but also for diagnosis and biomarkers analysis.In parallel with the pathology setting advancement,translational medicine is approaching the unprecedented opportunities unrevealed by artificial intelligence(AI).Indeed,the increased usage of biobanks’datasets in research provided new challenges for AI applications,such as advanced algorithms,and computer-aided techniques.In this scenario,machine learning-based approaches are being propose in order to improve biobanks from biospecimens collection repositories to computational datasets.To date,evidence on how to implement digital biobanks in translational medicine is still lacking.This viewpoint article summarizes the currently available literature that supports the biobanks’role in the digital pathology era,and to provide possible practical applications of digital biobanks.

Management of hilar cholangiocarcinoma in the North of England: Pathology, treatment, and outcome

AIM: To assess the management and outcome of hilar cholangiocarcinoma (Klatskin tumor) in a single tertiary referral center.METHODS: The notes of all patients with a diagnosis of hilar cholangiocarcinoma referred to our unit for over an 8-year period were identified and retrospectively reviewed. Presentation, management and outcome were assessed.RESULTS: Seventy-five patients were identified. The median age was 64 years (range 34-84 years). Male to female ratio was 1:1. Eighty-nine percent of patients presented with jaundice. Most patients referred were under Bismuth classification 3a, 3b or 4. Seventy patients required biliary drainage, 65 patients required 152percutaneous drainage procedures, and 25 had other complications. Forty-one patients had 51 endoscopic drainage procedures performed (15 failed). Of these,36 subsequently required percutaneous drainage. The median number of drainage procedures for all patients was three, 18 patients underwent resection (24%), nine had major complications and three died post-operatively.The 5-year survival rate was 4.2% for all patients, 21%for resected patients and 0% for those who did not undergo resection (P = 0.0021). The median number of admissions after diagnosis in resected patients was two and three in non-resected patients (P＜0.05).Twelve patients had external-beam radiotherapy, seven brachytherapy, and eight chemotherapy. There was no significant benefit in terms of survival (P = 0.46) or hospital admissions.CONCLUSION: Resection increases survival but carries the risk of significant morbidity and mortality.Percutaneous biliary drainage is almost always necessary and endoscopic drainage should be avoided if possible.