Generative pre-trained transformer (GPT)-4 support for differential diagnosis in neuroradiology.

Background: Differential diagnosis in radiology relies on the accurate identification of imaging patterns. The use of large language models (LLMs) in radiology holds promise, with many potential applications that may enhance the efficiency of radiologists' workflow. The study aimed to evaluate the efficacy of generative pre-trained transformer (GPT)-4, a LLM, in providing differential diagnoses in neuroradiology, comparing its performance with board-certified neuroradiologists. Methods: Sixty neuroradiology reports with variable diagnoses were inserted into GPT-4, which was tasked with generating a top-3 differential diagnosis for each case. The results were compared to the true diagnoses and to the differential diagnoses provided by three blinded neuroradiologists. Diagnostic accuracy and agreement between readers were assessed. Results: Of the 60 patients (mean age 47.8 years, 65% female), GPT-4 correctly included the diagnoses in its differentials in 61.7% (37/60) of cases, while the neuroradiologists' accuracy ranged from 63.3% (38/60) to 73.3% (44/60). Agreement between GPT-4 and the neuroradiologists, and among the neuroradiologists was fair to moderate [Cohen's kappa (kw) 0.34-0.44 and kw 0.39-0.54, respectively]. Conclusions: GPT-4 shows potential as a support tool for differential diagnosis in neuroradiology, though it was outperformed by human experts. Radiologists should remain mindful to the limitations of LLMs, while harboring their potential to enhance educational and clinical work.

The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer.

Background: Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer. Materials and methods: A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy. Results: Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01). Conclusion: Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment.

Computer-aided diagnosis of eyelid skin tumors using machine learning.

OBJECTIVE: To develop an automated, new framework based on machine learning to diagnose malignant eyelid skin tumors. METHODS: This study used eyelid lesion images from Sheba Medical Center, a large tertiary center in Israel. Before model training, we pretrained our models on the International Skin Imaging Collaboration (ISIC) 2019 dataset consisting of 25,332 images. The proprietary eyelid data set was then used for fine-tuning. The data set contained multiple images per patient, aiming to classify malignant lesions in comparison to benign counterparts. RESULTS: The analyzed data set consisted of images representing both benign and malignant eyelid lesions. For the benign category, a total of 373 images were sourced. By comparison, for the malignant category, 186 images were sourced. For the final model, at sensitivity of 93.8% (95% CI 80.0-100.0%), the model has a corresponding specificity of 73.7% (95% CI 60.0-87.1%). To further understand the decision-making process of our model, we employed heatmap visualization techniques, specifically gradient-weighted Class Activation Mapping. DISCUSSION: This study introduces a dependable model-aided diagnostic technology for assessing eyelid skin lesions. The model demonstrated accuracy comparable to human evaluation, effectively determining whether a lesion raises a high suspicion of malignancy or is benign. Such a model has the potential to alleviate the burden on the health care system, particularly benefiting rural areas, and enhancing the efficiency of clinicians and overall health care.

Duration-Dependent Risk of Hypoxemia in Colonoscopy Procedures.

Background and Aims: Colonoscopy is a critical diagnostic and therapeutic procedure in gastroenterology. However, it carries risks, including hypoxemia, which can impact patient safety. Understanding the factors that contribute to the incidence of severe hypoxemia, specifically the role of procedure duration, is essential for improving patient outcomes. This study aims to elucidate the relationship between the length of colonoscopy procedures and the occurrence of severe hypoxemia. Methods: We conducted a retrospective cohort study at Sheba Medical Center, Israel, including 21,524 adult patients who underwent colonoscopy from January 2020 to January 2024. The study focused on the incidence of severe hypoxemia, defined as a drop in oxygen saturation below 90%. Sedation protocols, involving a combination of Fentanyl, Midazolam, and Propofol were personalized based on the endoscopist's discretion. Data were collected from electronic health records, covering patient demographics, clinical scores, sedation and procedure details, and outcomes. Statistical analyses, including logistic regression, were used to examine the association between procedure duration and hypoxemia, adjusting for various patient and procedural factors. Results: We initially collected records of 26,569 patients who underwent colonoscopy, excluding 5045 due to incomplete data, resulting in a final cohort of 21,524 patients. Procedures under 20 min comprised 48.9% of the total, while those lasting 20-40 min made up 50.7%. Only 8.5% lasted 40-60 min, and 2.9% exceeded 60 min. Longer procedures correlated with higher hypoxemia risk: 17.3% for <20 min, 24.2% for 20-40 min, 32.4% for 40-60 min, and 36.1% for ≥60 min. Patients aged 60-80 and ≥80 had increased hypoxemia odds (aOR 1.1, 95% CI 1.0-1.2 and aOR 1.2, 95% CI 1.0-1.4, respectively). Procedure durations of 20-40 min, 40-60 min, and over 60 min had aORs of 1.5 (95% CI 1.4-1.6), 2.1 (95% CI 1.9-2.4), and 2.4 (95% CI 2.0-3.0), respectively. Conclusions: The duration of colonoscopy procedures significantly impacts the risk of severe hypoxemia, with longer durations associated with higher risks. This study underscores the importance of optimizing procedural efficiency and tailoring sedation protocols to individual patient risk profiles to enhance the safety of colonoscopy. Further research is needed to develop strategies that minimize procedure duration without compromising the quality of care, thereby reducing the risk of hypoxemia and improving patient safety.

Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review.

Crohn's disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning in improving MRE for CD assessment. We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity. The study was conducted according to the PRISMA guidelines. The risk of bias was evaluated using the QUADAS-2 tool. Five eligible studies, encompassing 468 subjects, were identified. Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation for disease burden quantification, and 3D reconstruction for surgical planning are useful and promising for CD assessment. However, most of the studies are preliminary, retrospective studies, and have a high risk of bias in at least one category. Future research is needed to assess how deep learning can impact CD patient diagnostics, particularly when considering the increasing integration of such models into hospital systems.

ChatGPT's adherence to otolaryngology clinical practice guidelines.

OBJECTIVES: Large language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines. METHODS: We presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses. RESULTS: The study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests. CONCLUSIONS: Using a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use.

Utilizing large language models in breast cancer management: systematic review.

PURPOSE: Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field. METHODS: We searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". The risk bias was evaluated using the QUADAS-2 tool. RESULTS: Six studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information. CONCLUSION: LLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.

Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.

OBJECTIVES: With smartphones and wearable devices becoming ubiquitous, they offer an opportunity for large-scale voice sampling. This systematic review explores the application of deep learning models for the automated analysis of voice samples to detect vocal cord pathologies. METHODS: We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We searched MEDLINE and Embase databases for original publications on deep learning applications for diagnosing vocal cord pathologies between 2002 and 2022. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS: Out of the 14 studies that met the inclusion criteria, data from a total of 3037 patients were analyzed. All studies were retrospective. Deep learning applications targeted Reinke's edema, nodules, polyps, cysts, unilateral cord paralysis, and vocal fold cancer detection. Most pathologies had detection accuracy above 90%. Thirteen studies (93%) exhibited a high risk of bias and concerns about applicability. CONCLUSIONS: Technology holds promise for enhancing the screening and diagnosis of vocal cord pathologies. While current research is limited, the presented studies offer proof of concept for developing larger-scale solutions.

Glaucoma Prediction Models Based on Ocular and Systemic Findings.

INTRODUCTION: Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma. METHODS: Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included. Univariate and multivariate analyses followed by CatBoost and Light gradient-boosting machine prediction models were performed. Main outcome measures were systemic and ocular features associated with progression to glaucoma. RESULTS: A total of 7,880 patients (mean age 54.7 ± 12.6 years, 5,520 males [70.1%]) were included in a 3-year prediction model, and 314 patients (3.98%) had a final diagnosis of glaucoma. The combined model included 185 systemic and 42 ocular findings, and reached an ROC AUC of 0.84. The associated features were intraocular pressure (48.6%), cup-to-disk ratio (22.7%), age (8.6%), mean corpuscular volume (MCV) of red blood cell trend (5.2%), urinary system disease (3.3%), MCV (2.6%), creatinine level trend (2.1%), monocyte count trend (1.7%), ergometry metabolic equivalent task score (1.7%), dyslipidemia duration (1.6%), prostate-specific antigen level (1.2%), and musculoskeletal disease duration (0.5%). The ocular prediction model reached an ROC AUC of 0.86. Additional features included were age-related macular degeneration (10.0%), anterior capsular cataract (3.3%), visual acuity (2.0%), and peripapillary atrophy (1.3%). CONCLUSIONS: Ocular and combined systemic-ocular models can strongly predict the development of glaucoma in the forthcoming 3 years. Novel progression indicators may include anterior subcapsular cataracts, urinary disorders, and complete blood test results (mainly increased MCV and monocyte count).

Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.

BACKGROUND: Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS: A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS: Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS: The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.

A Five-Decade Text Mining Analysis of Cochlear Implant Research: Where We Started and Where We Are Heading.

Background and Objectives: Since its invention in the 1970s, the cochlear implant (CI) has been substantially developed. We aimed to assess the trends in the published literature to characterize CI. Materials and Methods: We queried PubMed for all CI-related entries published during 1970-2022. The following data were extracted: year of publication, publishing journal, title, keywords, and abstract text. Search terms belonged to the patient's age group, etiology for hearing loss, indications for CI, and surgical methodological advancement. Annual trends of publications were plotted. The slopes of publication trends were calculated by fitting regression lines to the yearly number of publications. Results: Overall, 19,428 CIs articles were identified. Pediatric-related CI was the most dominant sub-population among the age groups, with the highest rate and slope during the years (slope 5.2 ± 0.3, p < 0.001), while elderly-related CIs had significantly fewer publications. Entries concerning hearing preservation showed the sharpest rise among the methods, from no entries in 1980 to 46 entries in 2021 (slope 1.7 ± 0.2, p < 0.001). Entries concerning robotic surgery emerged in 2000, with a sharp increase in recent years (slope 0.5 ± 0.1, p < 0.001). Drug-eluting electrodes and CI under local-anesthesia have been reported only in the past five years, with a gradual rise. Conclusions: Publications regarding CI among pediatrics outnumbered all other indications, supporting the rising, pivotal role of CI in the rehabilitation of children with sensorineural hearing loss. Hearing-preservation publications have recently rapidly risen, identified as the primary trend of the current era, followed by a sharp rise of robotic surgery that is evolving and could define the next revolution.

Undetected Pancreatic Adenocarcinoma on Computed Tomography: Frequency According to Scan Protocol.

BACKGROUND: Computed tomography (CT) is the main diagnostic modality for detecting pancreatic adenocarcinoma. OBJECTIVES: To assess the frequency of missed pancreatic adenocarcinoma on CT scans according to different CT protocols. METHODS: The medical records of consecutive pancreatic adenocarcinoma patients were retrospectively collected (12/2011-12/2015). Patients with abdominal CT scans performed up to a year prior to cancer diagnosis were included. Two radiologists registered the presence of radiological signs of missed cancers. The frequency of missed cancers was compared between portal and pancreatic/triphasic CT protocols. RESULTS: Overall, 180 CT scans of pancreatic adenocarcinoma patients performed prior to cancer diagnosis were retrieved; 126/180 (70.0%) were conducted using pancreatic/triphasic protocols and 54/180 (30.0%) used portal protocols. The overall frequency of missed cancers was 6/180 (3.3%) in our study population. The frequency of missed cancers was higher with the portal CT protocols compared to the pancreatic/triphasic protocols: 5/54 (9.3%) vs. 1/126 (0.8%), P = 0.01. CT signs of missed cancers included small hypodense lesions, peri-pancreatic fat stranding, and dilated pancreatic duct with a cut-off sign. CONCLUSIONS: The frequency of missed pancreatic adenocarcinoma is higher on portal CT protocols. Physicians should consider the cancer miss rate on different CT protocols.

Adversarial attacks in radiology - A systematic review.

PURPOSE: The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology. METHODS: We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases. RESULTS: A total of 22 studies published between March 2018 and April 2023 were included, primarily focused on image classification algorithms. Fourteen studies evaluated white-box attacks, three assessed black-box attacks and five investigated both. Eleven of the 22 studies targeted chest X-ray classification algorithms, while others involved chest CT (6/22), brain MRI (4/22), mammography (2/22), abdominal CT (1/22), hepatic US (1/22), and thyroid US (1/22). Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100 %. CONCLUSIONS: Adversarial attacks are a growing concern. Although currently the threats are more theoretical than practical, they still represent a potential risk. It is important to be alert to such attacks, reinforce cybersecurity measures, and influence the formulation of ethical and legal guidelines. This will ensure the safe use of deep learning technology in medicine.

Current Applications of Artificial Intelligence in Sarcoidosis.

PURPOSE: Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis. METHODS: Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded. CONCLUSIONS: Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

Jejunal Inflammation in Crohn's Disease: Comparison between Diffusion Weighted Magnetic Resonance Imaging and Video Capsule Endoscopy.

BACKGROUND: Jejunal disease is associated with worse prognosis in Crohn's disease. The added value of diffusion weighted imaging for evaluating jejunal inflammation related to Crohn's Disease is scarce. OBJECTIVES: To compare diffusion weighted imaging, video capsule endoscopy, and inflammatory biomarkers in the assessment of Crohn's disease involving the jejunum. METHODS: Crohn's disease patients in clinical remission were prospectively recruited and underwent magnetic resonance (MR)-enterography and video capsule endoscopy. C-reactive protein and fecal-calprotectin levels were obtained. MR-enterography images were evaluated for restricted diffusion, and apparent diffusion coefficient values were measured. The video capsule endoscopy-based Lewis score was calculated. Associations between diffusion weighted imaging, apparent diffusion coefficient, Lewis score, and inflammatory biomarkers were evaluated. RESULTS: The study included 51 patients, and 27/51 (52.9%) with video capsule endoscopies showed jejunal mucosal inflammation. Sensitivity and specificity of restricted diffusion for video capsule endoscopy mucosal inflammation were 59.3% and 37.5% for the first reader, and 66.7% and 37.5% for the second reader, respectively. Diffusion weighted imaging was not statistically associated with jejunal video capsule endoscopy inflammation (P = 0.813). CONCLUSIONS: Diffusion weighted imaging was not an effective test for evaluation of jejunal inflammation as seen by video capsule endoscopy in patients with quiescent Crohn's disease.

MRI features of intraductal papillary mucinous neoplasm of the bile ducts, "The myth about the cyst": A systematic review.

Rationale and objectives: Intraductal papillary mucinous neoplasm of the bile ducts (IPMN-B) is a true pre-cancerous lesion, which shares common features with pancreatic IPMN (IPMN-P). While IPMN-P is a well described entity for which guidelines were formulated and revised, IPMN-B is a poorly described entity.We carried out a systematic review to evaluate the existing literature, emphasizing the role of MRI in IPMN-B depiction. Materials and methods: PubMed database was used to identify original studies and case series that reported MR Imaging features of IPMN-B. The search keywords were "IPMN OR intraductal papillary mucinous neoplasm OR IPNB OR intraductal papillary neoplasm of the bile duct AND Biliary OR biliary cancer OR hepatic cystic lesions". Risk of bias and applicability were evaluated using the QUADAS-2 tool. Results: 884 Records were Identified through database searching. 12 studies satisfied the inclusion criteria, resulting in MR features of 288 patients. All the studies were retrospective. Classic features of IPMN-B are under-described. Few studies note worrisome features, concerning for an underlying malignancy. 50 % of the studies had a high risk of bias and concerns regarding applicability. Conclusions: The MRI features of IPMN-B are not well elaborated and need to be further studied. Worrisome features and guidelines regarding reporting the imaging findings should be established and published. Radiologists should be aware of IPMN-B, since malignancy diagnosis in an early stage will yield improved prognosis.

Perivascular Cuffing as the Sole Imaging Finding of Pancreatic Cancer.

BACKGROUND: Perivascular cuffing as the sole imaging manifestation of pancreatic ductal adenocarcinoma (PDAC) is an under-recognized entity. OBJECTIVES: To present this rare finding and differentiate it from retroperitoneal fibrosis and vasculitis. METHODS: Patients with abdominal vasculature cuffing were retrospectively collected (January 2011 to September 2017). We evaluated vessels involved, wall thickness, length of involvement and extra-vascular manifestations. RESULTS: Fourteen patients with perivascular cuffing were retrieved: three with celiac and superior mesenteric artery (SMA) perivascular cuffing as the only manifestation of surgically proven PDAC, seven with abdominal vasculitis, and four with retroperitoneal fibrosis. PDAC patients exhibited perivascular cuffing of either or both celiac and SMA (3/3). Vasculitis patients showed aortitis with or without iliac or SMA cuffing (3/7) or cuffing of either or both celiac and SMA (4/7). Retroperitoneal fibrosis involved the aorta (4/4), common iliac (4/4), and renal arteries (2/4). Hydronephrosis was present in 3/4 of retroperitoneal fibrosis patients. PDAC and vasculitis demonstrated reduced wall thickness in comparison to retroperitoneal fibrosis (PDAC: 1.0 ± 0.2 cm, vasculitis: 1.2 ± 0.5 cm, retroperitoneal fibrosis: 2.4 ± 0.4 cm; P = 0.002). There was no significant difference in length of vascular involvement (PDAC: 6.3 ± 2.1 cm, vasculitis: 7.1 ± 2.6 cm, retroperitoneal fibrosis: 8.7 ± 0.5 cm). CONCLUSIONS: Celiac and SMA perivascular cuffing can be the sole finding in PDAC and may be indistinguishable from vasculitis. This entity may differ from retroperitoneal fibrosis as it spares the aorta, iliac, and renal arteries and demonstrates thinner walls and no hydronephrosis.

Outcome of inflammatory bowel disease patients with prior malignancy.

Background: Inflammatory bowel disease (IBD) treatment options, such as anti-tumor necrosis factor (TNF) agents and thiopurines, are associated with an increased risk of certain malignancies. However, the management of IBD patients with prior malignancy is not well defined and the literature is scarce. The main aim of this study was to describe the outcome of IBD patients with prior malignancy, or malignancy before first exposure to IBD-related biologic or immunosuppressive treatment. Methods: The study cohort included adult IBD patients followed in a tertiary academic center, with at least one malignancy diagnosed before IBD diagnosis or before initiation of IBD-related treatment. The main outcome of interest was a relapse of the previous malignancy or development of a second malignancy. Results: Our database included 1112 patients with both IBD and malignancy. Of these, 86 (9%) who had their malignancy diagnosed before IBD-related treatment initiation were identified, while 10/86 patients (9%) were further diagnosed with a second primary malignancy. Twenty patients, (20/86, 23%) had recurrence of a previous malignancy, most commonly non-melanoma skin cancer (NMSC), found in 9/20 patients (45%). Treatment with infliximab was found to be significantly associated with recurrence of NMSC (P=0.003). Conclusions: Anti-TNF treatment may be associated with an increased risk of NMSC recurrence. This underscores the importance of rigorous dermatological follow up in IBD patients with previous NMSC treated with anti-TNFs.

Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence.

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn's disease (CD). In 2017, the panenteric capsule (PillCam Crohn's system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.