Search

Recent advancements in generative artificial intelligence have enabled analysis of text with visual data, which could have important implications in healthcare. Diagnosis in ophthalmology is often based on a combination of ocular examination, and clinical context. The aim of this study was to evaluate the performance of multimodal GPT-4 (GPT-4 V) in an integrated analysis of ocular images and clinical text. This retrospective study included 40 patients seen in our institution with images of their ocular examinations. Cases were selected by a board-certified ophthalmologist, to represent various pathologies. We provided the model with each patient image, without and then with the clinical context. We also asked two non-ophthalmology physicians to write diagnoses for each image, without and then with the clinical context. Answers for both GPT-4 V and the non-ophthalmologists were evaluated by two board-certified ophthalmologists. Performance accuracies were calculated and compared. GPT-4 V provided the correct diagnosis in 19/40 (47.5%) cases based on images without clinical context, and in 27/40 (67.5%) cases when clinical context was provided. Non-ophthalmologist physicians provided the correct diagnoses in 24/40 (60.0%), and 23/40 (57.5%) of cases without clinical context, and in 29/40 (72.5%) and 27/40 (67.5%) with clinical context. For all study participants adding context improved accuracy (p = 0.033). GPT-4 V is currently able to simultaneously analyze and integrate visual and textual data, and arrive at accurate clinical diagnoses in the majority of cases. Multimodal large language models like GPT-4 V have significant potential to advance both patient care and research in ophthalmology. [ABSTRACT FROM AUTHOR]

Background: Septic encephalopathy is frequent but its pathophysiology is enigmatic. We studied expression of neurotransmitters, inflammation and integrity of the blood–brain barrier (BBB) in several brain regions during abdominal sepsis. We compared mice with either lethal or surviving phenotype in the first 4 sepsis days. Mature CD-1 females underwent cecal ligation and puncture (CLP). Body temperature (BT) was measured daily and predicted-to-die (within 24 h) mice (for P-DIE; BT < 28 °C) were sacrificed together (1:1 ratio) with mice predicted-to-survive (P-SUR; BT > 35 °C), and healthy controls (CON). Brains were dissected into neocortex, cerebellum, midbrain, medulla, striatum, hypothalamus and hippocampus. Results: CLP mice showed an up to threefold rise of serotonin in the hippocampus, 5-hydroxyindoleacetic and homovanillic acid (HVA) in nearly all regions vs. CON. Compared to P-SUR, P-DIE mice showed a 1.7 to twofold rise of HVA (386 ng/g of tissue), dopamine (265 ng/g) and 3,4-Dihydroxyphenylacetic acid (DOPAC; 140 ng/g) in the hippocampus, hypothalamus and medulla (174, 156, 82 ng/g of tissue, respectively). CLP increased expression of TNFα, IL-1β and IL-6 mRNA by several folds in the midbrain, cerebellum and hippocampus versus CON. The same cytokines were further elevated in P-DIE vs P-SUR in the midbrain and cerebellum. Activation of astrocytes and microglia was robust across regions but remained typically phenotype independent. There was a similar influx of sodium fluorescein across the BBB in both P-DIE and P-SUR mice. Conclusions: Compared to survivors, the lethal phenotype induced a stronger deregulation of amine metabolism and cytokine expression in selected brain regions, but the BBB permeability remained similar regardless of the predicted outcome. [ABSTRACT FROM AUTHOR]

Objective: The United States Medical Licensing Examination (USMLE) assesses physicians' competency. Passing this exam is required to practice medicine in the U.S. With the emergence of large language models (LLMs) like ChatGPT and GPT-4, understanding their performance on these exams illuminates their potential in medical education and healthcare. Materials and methods: A PubMed literature search following the 2020 PRISMA guidelines was conducted, focusing on studies using official USMLE questions and GPT models. Results: Six relevant studies were found out of 19 screened, with GPT-4 showcasing the highest accuracy rates of 80–100% on the USMLE. Open-ended prompts typically outperformed multiple-choice ones, with 5-shot prompting slightly edging out zero-shot. Conclusion: LLMs, especially GPT-4, display proficiency in tackling USMLE questions. As AI integrates further into healthcare, ongoing assessments against trusted benchmarks are essential.Article Highlights: GPT-4 showed accuracy rates of 80–90% on the USMLE, outperforming previous GPT models. Model performance is influenced more by its inherent capabilities than by prompting methods or media elements inclusion. Further assessments against trusted benchmarks are essential for the effective integration of LLM into healthcare. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness. Materials and methods: This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve. Results: Ten studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies. Conclusions: The current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting. Clinical relevance statement: Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries. Key Points: • Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) model designed to identify active bleeding in digital subtraction angiography images for upper gastrointestinal bleeding. Methods: Angiographic images were retrospectively collected from mesenteric and celiac artery embolization procedures performed between 2018 and 2022. This dataset included images showing both active bleeding and non-bleeding phases from the same patients. The images were labeled as normal versus images that contain active bleeding. A convolutional neural network was trained and validated to automatically classify the images. Algorithm performance was tested in terms of area under the curve, accuracy, sensitivity, specificity, F1 score, positive and negative predictive value. Results: The dataset included 587 pre-labeled images from 142 patients. Of these, 302 were labeled as normal angiogram and 285 as containing active bleeding. The model's performance on the validation cohort was area under the curve 85.0 ± 10.9% (standard deviation) and average classification accuracy 77.43 ± 4.9%. For Youden's index cutoff, sensitivity and specificity were 85.4 ± 9.4% and 81.2 ± 8.6%, respectively. Conclusion: In this study, we explored the application of AI in mesenteric and celiac artery angiography for detecting active bleeding. The results of this study show the potential of an AI-based algorithm to accurately classify images with active bleeding. Further studies using a larger dataset are needed to improve accuracy and allow segmentation of the bleeding. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To examine the ophthalmic data from a large database of people attending a general medical survey institute, and to investigate ophthalmic findings of the eye and its adnexa, including differences in age and sex. Methods: Retrospective analysis including medical data of all consecutive individuals whose ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects examined at a single general medical survey institute. Results: Data were derived from 184,589 visits of 3676 patients (mean age 52 years, 68% males). The prevalence of the following eye pathologies were extracted. Eyelids: blepharitis (n = 4885, 13.3%), dermatochalasis (n = 4666, 12.7%), ptosis (n = 677, 1.8%), ectropion (n = 73, 0.2%), and xanthelasma (n = 160, 0.4%). Anterior segment: pinguecula (n = 3368, 9.2%), pterygium (n = 852, 2.3%), and cataract or pseudophakia (n = 9381, 27.1%). Cataract type (percentage of all phakic patients): nuclear sclerosis (n = 8908, 24.2%), posterior subcapsular (n = 846, 2.3%), and capsular anterior (n = 781, 2.1%). Pseudophakia was recorded for 697 patients (4.6%), and posterior subcapsular opacification for 229 (0.6%) patients. Optic nerve head (ONH): peripapillary atrophy (n = 4947, 13.5%), tilted disc (n = 3344, 9.1%), temporal slope (n = 410, 1.1%), ONH notch (n = 61, 0.2%), myelinated nerve fiber layer (n = 94, 0.3%), ONH drusen (n = 37, 0.1%), optic pit (n = 3, 0.0%), and ON coloboma (n = 4, 0.0%). Most pathologies were more common in males except for ONH, and most pathologies demonstrated a higher prevalence with increasing age. Conclusions: Normal ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects seen at a single medical survey institute. [ABSTRACT FROM AUTHOR]

Anti-glial fibrillary acidic protein (GFAP) meningoencephalomyelitis (autoimmune GFAP astrocytopathy) is a new autoimmune central nervous system (CNS) disease diagnosable by the presence of anti-GFAP autoantibodies in the cerebrospinal fluid and presents as meningoencephalomyelitis in the majority of patients. Only few neuropathological reports are available and little is known about the pathogenic mechanisms. We performed a histopathological study of two autopsies and nine CNS biopsies of patients with anti-GFAP autoantibodies and found predominantly a lymphocytic and in one autopsy case a granulomatous inflammatory phenotype. Inflammatory infiltrates were composed of B and T cells, including tissue-resident memory T cells. Although obvious astrocytic damage was absent in the GFAP-staining, we found cytotoxic T cell-mediated reactions reflected by the presence of CD8+/perforin+/granzyme A/B+ cells, polarized towards astrocytes. MHC-class-I was upregulated in reactive astrocytes of all biopsies and two autopsies but not in healthy controls. Importantly, we observed a prominent immunoreactivity of astrocytes with the complement factor C4d. Finally, we provided insight into an early phase of GFAP autoimmunity in an autopsy of a pug dog encephalitis that was characterized by marked meningoencephalitis with selective astrocytic damage with loss of GFAP and AQP4 in the lesions. Our histopathological findings indicate that a cytotoxic T cell-mediated immune reaction is present in GFAP autoimmunity. Complement C4d deposition on astrocytes could either represent the cause or consequence of astrocytic reactivity. Selective astrocytic damage is prominent in the early phase of GFAP autoimmunity in a canine autopsy case, but mild or absent in subacute and chronic stages in human disease, probably due to the high regeneration potential of astrocytes. The lymphocytic and granulomatous phenotypes might reflect different stages of lesion development or patient-specific modifications of the immune response. Future studies will be necessary to investigate possible implications of pathological subtypes for clinical disease course and therapeutic strategies. [ABSTRACT FROM AUTHOR]

The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models' consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT's 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Large language models such as ChatGPT have gained public and scientific attention. These models may support oncologists in their work. Oncologists should be familiar with large language models to harness their potential while being aware of potential dangers and limitations. [ABSTRACT FROM AUTHOR]

Purpose: Mortality prediction in elderly femoral neck fracture patients is valuable in treatment decision-making. A previously developed and internally validated clinical prediction model shows promise in identifying patients at risk of 90-day and 2-year mortality. Validation in an independent cohort is required to assess the generalizability; especially in geographically distinct regions. Therefore we questioned, is the SORG Orthopaedic Research Group (SORG) femoral neck fracture mortality algorithm externally valid in an Israeli cohort to predict 90-day and 2-year mortality? Methods: We previously developed a prediction model in 2022 for estimating the risk of mortality in femoral neck fracture patients using a multicenter institutional cohort of 2,478 patients from the USA. The model included the following input variables that are available on clinical admission: age, male gender, creatinine level, absolute neutrophil, hemoglobin level, international normalized ratio (INR), congestive heart failure (CHF), displaced fracture, hemiplegia, chronic obstructive pulmonary disease (COPD), history of cerebrovascular accident (CVA) and beta-blocker use. To assess the generalizability, we used an intercontinental institutional cohort from the Sheba Medical Center in Israel (level I trauma center), queried between June 2008 and February 2022. Generalizability of the model was assessed using discrimination, calibration, Brier score, and decision curve analysis. Results: The validation cohort included 2,033 patients, aged 65 years or above, that underwent femoral neck fracture surgery. Most patients were female 64.8% (n = 1317), the median age was 81 years (interquartile range = 75–86), and 80.4% (n = 1635) patients sustained a displaced fracture (Garden III/IV). The 90-day mortality was 9.4% (n = 190) and 2-year mortality was 30.0% (n = 610). Despite numerous baseline differences, the model performed acceptably to the validation cohort on discrimination (c-statistic 0.67 for 90-day, 0.67 for 2-year), calibration, Brier score, and decision curve analysis. Conclusions: The previously developed SORG femoral neck fracture mortality algorithm demonstrated good performance in an independent intercontinental population. Current iteration should not be relied on for patient care, though suggesting potential utility in assessing patients at low risk for 90-day or 2-year mortality. Further studies should evaluate this tool in a prospective setting and evaluate its feasibility and efficacy in clinical practice. The algorithm can be freely accessed: https://sorg-apps.shinyapps.io/hipfracturemortality/. Level of evidence: Level III, Prognostic study. [ABSTRACT FROM AUTHOR]

Pulmonary embolism (PE) is a common, life threatening cardiovascular emergency. Risk stratification is one of the core principles of acute PE management and determines the choice of diagnostic and therapeutic strategies. In routine clinical practice, clinicians rely on the patient's electronic health record (EHR) to provide a context for their medical imaging interpretation. Most deep learning models for radiology applications only consider pixel-value information without the clinical context. Only a few integrate both clinical and imaging data. In this work, we develop and compare multimodal fusion models that can utilize multimodal data by combining both volumetric pixel data and clinical patient data for automatic risk stratification of PE. Our best performing model is an intermediate fusion model that incorporates both bilinear attention and TabNet, and can be trained in an end-to-end manner. The results show that multimodality boosts performance by up to 14% with an area under the curve (AUC) of 0.96 for assessing PE severity, with a sensitivity of 90% and specificity of 94%, thus pointing to the value of using multimodal data to automatically assess PE severity. [ABSTRACT FROM AUTHOR]

The field of gastroenterology (GI) is constantly evolving. It is essential to pinpoint the most pressing and important research questions. To evaluate the potential of chatGPT for identifying research priorities in GI and provide a starting point for further investigation. We queried chatGPT on four key topics in GI: inflammatory bowel disease, microbiome, Artificial Intelligence in GI, and advanced endoscopy in GI. A panel of experienced gastroenterologists separately reviewed and rated the generated research questions on a scale of 1–5, with 5 being the most important and relevant to current research in GI. chatGPT generated relevant and clear research questions. Yet, the questions were not considered original by the panel of gastroenterologists. On average, the questions were rated 3.6 ± 1.4, with inter-rater reliability ranging from 0.80 to 0.98 (p < 0.001). The mean grades for relevance, clarity, specificity, and originality were 4.9 ± 0.1, 4.6 ± 0.4, 3.1 ± 0.2, 1.5 ± 0.4, respectively. Our study suggests that Large Language Models (LLMs) may be a useful tool for identifying research priorities in the field of GI, but more work is needed to improve the novelty of the generated research questions. [ABSTRACT FROM AUTHOR]

Background: The association between diverticular disease and atherosclerotic cardiovascular disease (ASCVD) has been demonstrated previously, mainly in symptomatic subjects. Aims: To evaluate 10 years cardiovascular risk, exercise performance and association to ASCVD among subjects with asymptomatic diverticulosis. Methods: A retrospective cross-sectional cohort of self-referred participants in a medical screening program, who underwent a screening colonoscopy. Demographics, clinical and laboratory variables, ASCVD score, and metabolic equivalents (METs) during treadmill stress test were compared between subjects with and without diverticulosis as diagnosed on screening colonoscopy. Results: 4586 participants underwent screening colonoscopy; 799 (17.4%) had diverticulosis. Among 50–69 yo participants, diverticulosis subjects had a higher ASCVD score compared to non-diverticulosis subjects. Exercise performance was comparable between the groups, across all age groups. Using logistic regression analysis, advanced age group (50–59 yo Adjusted odds ratio (AOR) [95% confidence interval (CI)] 2.57 (1.52–4.34), p < 0.001; 60–69 yo, AOR 2.87 (2.09–3.95), p < 0.001; ≥ 70 yo AOR 4.81 (3.23–7.15), p < 0.001; compared to < 50 yo age group), smoking [AOR 1.27 (1.05–1.55), p = 0.016], HTN [AOR 1.27 (1.03–1.56), p = 0.022], obesity [AOR 1.36 (1.06–1.74), p = 0.014] and male sex [AOR 1.29 (1.02–1.64), p = 0.036] were associated with diverticular detection during screening colonoscopy. Among males, achieving METs score ≥ 10 was inversely associated with diverticular detection during screening colonoscopy [AOR 0.64 (0.43–0.95), p = 0.027]. Conclusions: Ten years probability for ASCVD estimated by the ASCVD score is higher among subjects with asymptomatic diverticulosis compared to subjects without diverticulosis. Improved exercise performance is demonstrated for the first time to correlate with decreased probability for diverticular disease in screening colonoscopy. [ABSTRACT FROM AUTHOR]

Purpose: Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative surgical decisions. However, IOUS is highly operator dependent and has a steep learning curve. We describe the design and assessment of an artificial intelligence (AI) system to identify focal liver lesions in IOUS. Methods: IOUS images were collected during liver resections performed between November 2020 and November 2021. The images were labeled by radiologists and surgeons as normal liver tissue versus images that contain liver lesions. A convolutional neural network (CNN) was trained and tested to classify images based on the labeling. Algorithm performance was tested in terms of area under the curves (AUCs), accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value. Results: Overall, the dataset included 5043 IOUS images from 16 patients. Of these, 2576 were labeled as normal liver tissue and 2467 as containing focal liver lesions. Training and testing image sets were taken from different patients. Network performance area under the curve (AUC) was 80.2 ± 2.9%, and the overall classification accuracy was 74.6% ± 3.1%. For maximal sensitivity of 99%, the classification specificity is 36.4 ± 9.4%. Conclusions: This study provides for the first time a proof of concept for the use of AI in IOUS and show that high accuracy can be achieved. Further studies using high volume data are warranted to increase accuracy and differentiate between lesion types. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine–related axillary lymphadenopathy. Materials and methods: We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes. Analysis was performed on PET, CT, and combined PET/CT inputs. Lymph nodes were randomly assigned to a training (n = 132) and validation cohort (n = 33) by 5-fold cross-validation. K-nearest neighbors (KNN) and random forest (RF) machine learning models were used. Performance was evaluated using an area under the receiver-operator characteristic curve (AUC-ROC) score. Results: Axillary lymph nodes from breast cancer patients (n = 85) and COVID-19-vaccinated individuals (n = 80) were analyzed. Analysis of first-order features showed statistically significant differences (p < 0.05) in all combined PET/CT features, most PET features, and half of the CT features. The KNN model showed the best performance score for combined PET/CT and PET input with 0.98 (± 0.03) and 0.88 (± 0.07) validation AUC, and 96% (± 4%) and 85% (± 9%) validation accuracy, respectively. The RF model showed the best result for CT input with 0.96 (± 0.04) validation AUC and 90% (± 6%) validation accuracy. Conclusion: Radiomics features can differentiate between FDG-avid breast cancer metastatic and FDG-avid COVID-19 vaccine–related axillary lymphadenopathy. Such a model may have a role in differentiating benign nodes from malignant ones. Key Points: • Patients who were vaccinated with the COVID-19 mRNA vaccine have shown FDG-avid reactive axillary lymph nodes in PET-CT scans. • We evaluated if radiomics and machine learning can distinguish between FDG-avid metastatic axillary lymphadenopathy in breast cancer patients and FDG-avid reactive axillary lymph nodes. • Combined PET and CT radiomics data showed good test AUC (0.98) for distinguishing between metastatic axillary lymphadenopathy and post-COVID-19 vaccine–associated axillary lymphadenopathy. Therefore, the use of radiomics may have a role in differentiating between benign from malignant FDG-avid nodes. [ABSTRACT FROM AUTHOR]

Purpose: Air monitoring has been the accepted exposure assessment of toxic metals from, e.g., welding, but a method characterizing the actual dose delivered to the lungs would be preferable. Sampling of particles in exhaled breath can be used for the biomonitoring of both endogenous biomarkers and markers of exposure. We have explored a new method for the sampling of metals in exhaled breath from the small airways in a study on welders. Methods: Our method for particle sampling, Particles in Exhaled Air (PExA®), is based on particle counting and inertial impaction. We applied it on 19 stainless steel welders before and after a workday. In parallel, air monitoring of chromium, manganese and nickel was performed as well as blood sampling after work. Results: Despite substantial exposure to welding fumes, we were unable to show any significant change in the metal content of exhaled particles after, compared with before, exposure. However, the significance might be obscured by a substantial analytical background noise, due to metal background in the sampling media and possible contamination during sampling, as an increase in the median metal contents were indicated. Conclusions: If efforts to reduce background and contamination are successful, the PExA® method could be an important tool in the investigations of metals in exhaled breath, as the method collects particles from the small airways in contrast to other methods. In this paper, we discuss the discrepancy between our findings and results from studies, using the exhaled breath condensate (EBC) methodology. [ABSTRACT FROM AUTHOR]

Only one paper, "Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial", published in I Radiology i , was a randomized controlled trial. Keywords: Artificial intelligence; Randomized controlled trial; Radiology EN Artificial intelligence Randomized controlled trial Radiology 4223 4225 3 05/17/23 20230601 NES 230601 A rapid growth in artificial intelligence (AI) applications in radiology has encompassed many aspects, from medical imaging interpretation to workflow management and decision-making. [[5]] As AI becomes an integral part of radiology workflow, critical evaluation of AI through methodologically strong tools is crucial. [Extracted from the article]

One of the solutions to increase children's energy intake is the use of amylase-rich flours. This led us to optimize the fluidification capacity of two paddy rice varieties. For that, the Doehlert plane-type response surface methodology was used to optimize the germinating ability of paddy rice flour (varieties Nerica 3 and Nerica L56) to fluidize cassava-based gruels by using two factors: soaking and germination time. The evaluated response was the flow velocity of the gruels. The optimization result showed that, the optimal soaking time was 48 h for the variety Nerica L56 and 12 h for Nerica 3. Concerning the optimal germination time, it was 135 h for both varieties. These conditions have been found to have the best fluidifying capacities for cassava flour-based gruels. The incorporation of 3 g germinated paddy rice flour (Nerica L56 and Nerica 3) and at optimal conditions, allowed us to obtain high flow velocities of the gruels. They were 100.00 mm/30 s and 210.66 mm/30 s respectively for the varieties Nerica L56 and Nerica 3. These flow velocities were similar to the flow velocities predicted by the model for desirabilities of 0.99 and 1.00. In view of all these results, we could therefore recommend the use of these two varieties of rice produced under these conditions to improving flow velocities, dry matter concentrations and energy bulk of the gruels. [ABSTRACT FROM AUTHOR]

Objective: To evaluate uterine arteries (UA) of potential living donors for uterus transplantation (UTx) by comparison of CT angiography (CTA), digital subtraction angiography (DSA), and MR angiography (MRA) with care taken to minimize radiation doses. Methods: Prospective donors for a clinical UTx trial were included. CTA, DSA, and MRA measurements in three predefined segments of the UAs were evaluated. Radiation doses were estimated and 1-year graft survival was recorded. Results: Twelve potential donors (age 37–62 years) were investigated. There was no difference in visualized average UA lumen diameter when comparing CTA (mean 2.0 mm, SD 0.4), DSA (mean 2.1 mm, SD 0.6), and MRA (mean 2.0 mm, SD 0.3). MRA was not able to fully evaluate 10 (43%) out of 23 UA that proved to be patent on DSA. One UA was not identified by any of the modalities, and three MRA-absent UAs were identified by both CTA and DSA. The estimated mean effective dose was lower for DSA (5.1 mSv, SD 2.8) than CTA (7.1 mSv, SD 2.0), but not significantly (p value = 0.06). Three potential donors were excluded due to UA pathology and one due to adenomyosis. Eight donors underwent hysterectomy, with 1-year graft survival in six women. Conclusion: MRI including MRA should be the initial modality to examine potential UTx donors to acquire valuable details of uterine anatomy, and if UAs are fully visualized, there is no need for further angiographic methods with radiation. If UAs are not visualized by MRA, CTA may be performed and in selective cases with addition of the invasive modality DSA. Key Points: • For uterine transplantation, pelvic MRI with MRA provides information of the uterine structure and of the diameters of uterine arteries in living donors. • Failure of MRA to demonstrate uterine arteries could be followed by CTA which will visualize the uterine arteries in a majority of cases. If MRA and additional CTA provide inconclusive results, the uterine arteries should be further evaluated by DSA. • Information of CTA can be used in the angio-system for DSA settings to minimize the radiation and contrast media doses. [ABSTRACT FROM AUTHOR]

Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms. [ABSTRACT FROM AUTHOR]

Background: In the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic procedures. Methods: Medline, Embase, IEEE Xplore, and the Web of science databases were searched from January 2012 to May 5, 2020. Selected studies tested a deep learning model, specifically convolutional neural networks, for video analysis of laparoscopic surgery. Study characteristics including the dataset source, type of operation, number of videos, and prediction application were compared. A random effects model was used for estimating pooled sensitivity and specificity of the computer algorithms. Summary receiver operating characteristic curves were calculated by the bivariate model of Reitsma. Results: Thirty-two out of 508 studies identified met inclusion criteria. Applications included instrument recognition and detection (45%), phase recognition (20%), anatomy recognition and detection (15%), action recognition (13%), surgery time prediction (5%), and gauze recognition (3%). The most common tested procedures were cholecystectomy (51%) and gynecological—mainly hysterectomy and myomectomy (26%). A total of 3004 videos were analyzed. Publications in clinical journals increased in 2020 compared to bio-computational ones. Four studies provided enough data to construct 8 contingency tables, enabling calculation of test accuracy with a pooled sensitivity of 0.93 (95% CI 0.85–0.97) and specificity of 0.96 (95% CI 0.84–0.99). Yet, the majority of papers had a high risk of bias. Conclusions: Deep learning research holds potential in laparoscopic surgery, but is limited in methodologies. Clinicians may advance AI in surgery, specifically by offering standardized visual databases and reporting. [ABSTRACT FROM AUTHOR]

Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings. [ABSTRACT FROM AUTHOR]

Purpose: Natural language processing (NLP) can be used for automatic flagging of radiology reports. We assessed deep learning models for classifying non-English head CT reports. Methods: We retrospectively collected head CT reports (2011–2018). Reports were signed in Hebrew. Emergency department (ED) reports of adult patients from January to February for each year (2013–2018) were manually labeled. All other reports were used to pre-train an embedding layer. We explored two use cases: (1) general labeling use case, in which reports were labeled as normal vs. pathological; (2) specific labeling use case, in which reports were labeled as with and without intra-cranial hemorrhage. We tested long short-term memory (LSTM) and LSTM-attention (LSTM-ATN) networks for classifying reports. We also evaluated the improvement of adding Word2Vec word embedding. Deep learning models were compared with a bag-of-words (BOW) model. Results: We retrieved 176,988 head CT reports for pre-training. We manually labeled 7784 reports as normal (46.3%) or pathological (53.7%), and 7.1% with intra-cranial hemorrhage. For the general labeling, LSTM-ATN-Word2Vec showed the best results (AUC = 0.967 ± 0.006, accuracy 90.8% ± 0.01). For the specific labeling, all methods showed similar accuracies between 95.0 and 95.9%. Both LSTM-ATN-Word2Vec and BOW had the highest AUC (0.970). Conclusion: For a general use case, word embedding using a large cohort of non-English head CT reports and ATN improves NLP performance. For a more specific task, BOW and deep learning showed similar results. Models should be explored and tailored to the NLP task. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether sarcoidosis patients infected with SARS-CoV-2 are at risk for adverse disease outcomes. Study Design and Methods: This retrospective study was conducted in five hospitals within the Mount Sinai Health System during March 1, 2020 to July 29, 2020. All patients diagnosed with COVID-19 were included in the study. We identified sarcoidosis patients who met diagnostic criteria for sarcoidosis according to accepted guidelines. An adverse disease outcome was defined as the presence of intubation and mechanical ventilation or in-hospital mortality. In sarcoidosis patients, we reported (when available) the results of pulmonary function testing measured within 3 years prior to the time of SARS‑CoV‑2 infection. A multivariable logistic regression model was used to generate an adjusted odds ratio (aOR) to evaluate sarcoidosis as a risk factor for an adverse outcome. The same model was used to analyze sarcoidosis patients with moderate and/or severe impairment in pulmonary function. Results: The study included 7337 patients, 37 of whom (0.5%) had sarcoidosis. The crude rate of developing an adverse outcome was significantly higher in patients with moderately and/or severely impaired pulmonary function (9/14 vs. 3/23, p = 0.003). While the diagnosis of sarcoidosis was not independently associated with risk of an adverse event, (aOR 1.8, 95% CI 0.9–3.6), the diagnosis of sarcoidosis in patients with moderately and/or severely impaired pulmonary function was associated with an adverse outcome (aOR 7.8, 95% CI 2.4–25.8). Conclusion: Moderate or severe impairment in pulmonary function is associated with mortality in sarcoidosis patients infected with SARS‑CoV‑2. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage. Methods: We collected data of all adult ED visits in our institution for five consecutive years (1/2013–12/2017). Retrieved variables included the following: demographics, mode of arrival to the ED, comorbidities, home medications, structured and unstructured chief complaints, vital signs, pain scale score, emergency severity index, ED wing assignment, documentation of previous ED visits, hospitalizations and CTs, and current visit non-contrast head CT usage. A machine learning gradient boosting model was trained on data from the years 2013–2016 and tested on data from 2017. Area under the curve (AUC) was used as metrics. Single-variable AUCs were also determined. Youden's index evaluated optimal sensitivity and specificity of the models. Results: The final cohort included 595,561 ED visits. Non-contrast head CT usage rate was 11.8%. Each visit was coded into an input vector of 171 variables. Single-variable analysis showed that chief complaint had the best single predictive analysis (AUC = 0.87). The best model showed an AUC of 0.93 (95% CI 0.931–0.936) for predicting non-contrast head CT usage at triage level. The model had a sensitivity of 88.1% and specificity of 85.7% for non-contrast head CT utilization. Conclusion: The developed model can identify patients that need to undergo head CT exam already in the ED triage level and by that allow faster diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.Objective: Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED.Design: An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality.Key Results: Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality.Conclusion: The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED. [ABSTRACT FROM AUTHOR]

The medial olivocochlear reflex has been hypothesized to improve the detection and discrimination of dynamic signals in noisy backgrounds. This hypothesis was tested here by comparing behavioral outcomes with otoacoustic emissions. The effects of a precursor on amplitude-modulation (AM) detection were measured for a 1- and 6-kHz carrier at levels of 40, 60, and 80 dB SPL in a two-octave-wide noise masker with a level designed to produce poor, but above-chance, performance. Three types of precursor were used: a two-octave noise band, an inharmonic complex tone, and a pure tone. Precursors had the same overall level as the simultaneous noise masker that immediately followed the precursor. The noise precursor produced a large improvement in AM detection for both carrier frequencies and at all three levels. The complex tone produced a similarly large improvement in AM detection at the highest level but had a smaller effect for the two lower carrier levels. The tonal precursor did not significantly affect AM detection in noise. Comparisons of behavioral thresholds and medial olivocochlear efferent effects on stimulus frequency otoacoustic emissions measured with similar stimuli did not support the hypothesis that efferent-based reduction of cochlear responses contributes to the precursor effects on AM detection. [ABSTRACT FROM AUTHOR]

Purpose: Laparoscopic adjustable gastric banding (LAGB) used to be a common procedure at the turn of the century and is still frequently encountered on CT scans in common clinical practice. Our aim is to present the frequency and spectrum of complication associated with LAGB, as observed in CT.Materials and Methods: After approval of our institutional review board, a retrospective search for LAGB in CT interpretations using the term "band" between December 2011 and April 2017 was conducted. CT scans were reviewed to identify complications. The findings were divided into two groups: symptomatic, in which the complications caused acute symptoms for which CT scans were conducted, and incidental, in which complications were incidentally identified. The frequency of complications was calculated.Results: We identified 160 patients who underwent LAGB and performed a CT scan. Complications were identified in 69/160 (43.1%) patients, with a total of 83 findings: 47/160 (29.4%) esophageal dilatation, 13/160 (8.2%) pulmonary complications, 6/160 (3.8%) abdominal abscesses, 5/160 (3.1%) small bowel obstructions, 4/160 (2.5%) intragastric band erosions, 4/160 (2.5%) tube disconnections, 3/160 (1.9%) port site and tube course infections, and 1/160 (0.6%) small pouch bezoars. When compared with patients' referral notes, 38/83 (45.8%) of the findings were associated with acute symptoms, whereas 45/83 (54.2%) of the findings were incidental. Eighteen percent of the incidental complications were clinically important.Conclusion: Complications were found in 43% of CT scans of patients who underwent LAGB; less than half of the findings were symptomatic. Some of the incidentally identified complications had substantial clinical importance. [ABSTRACT FROM AUTHOR]

Background and aims: Distinguishing between fibrotic and inflammatory strictures in Crohn’s disease (CD) is still challenging. The capacity of diffusion-weighted (DWI) magnetic resonance (MRE) to identify intestinal fibrosis was recently demonstrated; however, the therapeutic implications of this association have never been evaluated. The aim of the current study was to identify imaging features, including DWI, which can predict response to anti-inflammatory treatment in patients with stricturing CD.Methods: Consecutive CD patients with intestinal strictures that initiated treatment with anti-tumor necrosis alpha (anti-TNF) between June 2012 and April 2017 with MRE adjacent to treatment onset were retrospectively collected. The primary outcome was treatment failure, defined as drug discontinuation, CD-related surgery, or endoscopic dilatation of the stricture. Clinical, demographic, and imaging data were compared between patients who did and did not develop treatment failure within 12 months of anti-TNF treatment initiation.Results: A total of 21 patients were included in the study; 9/21 (42.8%) developed treatment failure. None of the clinical/demographic parameters were associated with the risk of treatment failure. Among imaging parameters, only ADC value (< 1 × 10−3 mm2/s) was significantly associated with the risk of treatment failure (AUC = 0.81, 66% vs. 0%, p = 0.015).Conclusions: Our results suggest that ADC value on DWI MRE may predict the risk of treatment failure in stricturing CD. If replicated in larger studies, these results may guide therapeutic decisions and suggest avoiding anti-TNF treatment. [ABSTRACT FROM AUTHOR]

Purpose: Simple renal cysts are a common benign finding in abdominal CT scans. However, since they may evolve in time, simple cysts need to be reported. With an ever-growing number of slices per CT scan, cysts are easily overlooked by the overloaded radiologist. In this paper, we address the detection of simple renal cysts as an incidental finding in a real clinical setting.Methods: We propose a fully automatic framework for renal cyst detection, supported by a robust segmentation of the kidneys performed by a fully convolutional neural network. A combined 3D distance map of the kidneys and surrounding fluids provides initial candidates for cysts. Eventually, a second convolutional neural network classifies the candidates as cysts or non-cyst objects.Results: Performance was evaluated on 52 abdominal CT scans selected at random in a real radiological workflow and containing over 70 cysts annotated by an experienced radiologist. Setting the minimal cyst diameter to 10 mm, the algorithm detected 59/70 cysts (true-positive rate = 84.3%) while producing an average of 1.6 false-positive per case.Conclusions: The obtained results suggest the proposed framework is a promising approach for the automatic detection of renal cysts as incidental findings of abdominal CT scans. [ABSTRACT FROM AUTHOR]

Objective: We describe FDG-PET/CT findings of postoperative fat necrosis in patients following abdominal surgery, and evaluate their changes in size and FDG uptake over time.Methods: FDG-PET/CT scans from January 2007-January 2016 containing the term 'fat necrosis' were reviewed. Lesions meeting radiological criteria of fat necrosis in patients with prior abdominal surgery were included.Results: Forty-four patients, 30 males, mean age 68.4 ± 11.0 years. Surgeries: laparotomy (n=37; 84.1 %), laparoscopy (n=3; 6.8 %), unknown (n=4; 9.1 %). CTs of all lesions included hyperdense well-defined rims surrounding a heterogeneous fatty core. Sites: peritoneum (n=34; 77 %), omental fat (n=19; 43 %), subcutaneous fat (n=8; 18 %), retroperitoneum (n=2; 5 %). Mean lesion long axis: 33.6±24.9 mm (range: 13.0-140.0). Mean SUVmax: 2.6±1.1 (range: 0.6-5.1). On serial CTs (n=34), lesions decreased in size (p=0.022). Serial FDG-PET/CT (n=24) showed no significant change in FDG-avidity (p=0.110). Mean SUVmax did not correlate with time from surgery (p=0.558) or lesion size (p=0.259).Conclusion: Postsurgical fat necrosis demonstrated characteristic CT features and may demonstrate increased FDG uptake. However, follow-up of subsequent imaging scans showed no increases in size or FDG-avidity. Awareness of this entity is important to avoid misinterpretation of findings as recurrent cancer.Key Points: • Postsurgical fat necrosis may mimic cancer in FDG-PET/CT. • Follow-up of fat necrosis showed no increase in FDG intensity. • CT follow-up showed a decrease in lesion size. • FDG uptake did not correlate with time lapsed from surgery. [ABSTRACT FROM AUTHOR]

Objectives: Evaluate the ability of MR diffusion-weighted imaging (DWI) to predict patency capsule retention in Crohn's disease (CD).Methods: Clinical and imaging data were prospectively reviewed for 80 CD patients following patency capsule administration and MR-DWI under institutional review board (IRB) approval with informed consent. Two radiologists separately assessed the presence/absence of restricted diffusion in the distal ileum. Apparent diffusion coefficients (ADC) from three regions of interest on the ileal wall were averaged. The association between restricted diffusion and retention, and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Ability of ADC to predict retention was assessed with receiver operating characteristic (ROC) curve analysis.Results: Restricted diffusion in the distal ileum was associated with capsule retention (p = 0.001, p < 0.0001). Sensitivity, specificity, PPV and NPV of restricted diffusion for capsule retention were 100.0%, 46.2%, 30.0%, 100% and 100.0%, 56.9%, 34.9%, 100%, respectively, for two radiologists. Accuracy of ADC to predict retention was high (area under the curve = 0.851, p < 0.0001). An ADC of 1.47 mm2/s showed 90.0% sensitivity and 50.0% specificity for retention.Conclusions: Sensitivity and NPV of restricted diffusion for patency capsule retention were 100%, suggesting that DWI may predict gastrointestinal tract capability to pass video camera endoscopy.Key Points: • Capsule endoscopy enables assessment of the gastrointestinal mucosa in Crohn's disease • Prior patency capsule administration is recommended to evaluate gastrointestinal tract patency • MR diffusion-weighted imaging may detect pathological constriction of the ileum • Restricted diffusion in the distal ileum was associated with capsule retention • MR-DWI may predict gastrointestinal tract capability to pass capsule endoscopy. [ABSTRACT FROM AUTHOR]

Objective: A number of case reports have demonstrated FDG uptake around mesh prostheses after hernia repair surgery. This study characterizes FDG PET-CT findings after hernia repair with synthetic mesh in a series of cancer patients.Materials and Methods: FDG PET-CT studies were reviewed for increased FDG uptake consistent with CT appearances of post-surgical hernia repair in cancer patients. The findings were correlated with clinical data and follow-up studies.Results: 53 PET-CT studies in 22 patients (18 males, 31-79 years) were identified. Surgery for repair of inguinal (n = 14), ventral (n = 5) or umbilical (n = 3) hernia was performed, 4-204 months prior to PET-CT. FDG avidity was focal or linear in the region of the anterior abdominal or pelvic wall (mean SUV max 4.0 ± 2.3). Corresponding nonspecific CT findings included soft tissue thickening (n = 18), fat infiltration (n = 20) and fluid collection (n = 19) in the region of the omentum, adjacent to or in the inner abdominal or pelvic wall at the surgical site. Linear hyper-dense structures (n = 9) or metallic clips (n = 8) seen on CT suggested benign postoperative changes. In 10/12 (83.3%) patients with repeat PET-CT, FDG uptake remained unchanged, one showed more diffuse uptake and another showed reduced uptake on follow-up. There was neither significant change in CT appearance at the surgical site in these 12 patients, nor in 3 additional patients with only CT follow-up. Another 3 patients had previous CT demonstrating hernia at the same location.Conclusion: With increasing use of synthetic mesh, awareness of variations in FDG PET-CT appearance is important to avoid false interpretation in cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the associations between breast glandular tissues diameters as determined by CT and b-hCG levels, histological types, tumour spread and prognosis in patients with testicular germ cell tumour.Methods: Ninety-four patients with pre-treatment CT scan and markers (b-hCG, AFP, LDH) were retrospectively collected. A radiologist measured diameters in all CT examinations and correlation between diameters and log (b-hCG) was assessed (Pearson's coefficient). The ability of measured diameters to predict lymphatic and distant haematogenous metastatic spread was evaluated (ROC curves). The associations between measured diameter cut-off values of 20 and 25 mm and International Germ Cell Cancer Collaborative Group (IGCCCG) classification, lymphatic and distant haematogenous metastatic spread and histological subtypes were evaluated (chi squared test).Results: Breast glandular diameters correlated to log(b-hCG) (r = 0.579) and predicted distant haematogenous metastatic spread (AUC = 0.78). Worse prognosis (intermediate or poor IGCCCG) was shown for 20 mm (27.3 vs. 4.2 %, p = 0.005) and 25 mm (33.3 vs. 6.1 %, p = 0.014). A diameter of 25 mm was associated with non-seminoma (91.7 vs. 48.8 %, p = 0.005).Conclusion: Breast glandular tissue diameters correlated with log(b-hCG) and predicted distant haematogenous metastases. Twenty and 25 mm were associated with worse prognosis and 25 mm was able to distinguish between seminoma and non-seminoma.Key Points: • CT breast glandular tissue diameter correlates with log(b-HCG) • Gynaecomastia in CT is associated with worse prognosis • Gynaecomastia in CT is associated with non-seminoma histological subtype. [ABSTRACT FROM AUTHOR]

The spleen is a rare site of abdominal ectopic pregnancy. In a review of the literature, we found 16 published cases of primary splenic pregnancies. Of the cases identified, all received surgical intervention, with one case successfully treated with laparoscopic methotrexate injection, and the rest underwent splenectomy. We would like to present a case of primary splenic pregnancy in a 35-year-old woman successfully treated with percutaneous image-guided injection of methotrexate and KCl. [ABSTRACT FROM AUTHOR]

Objectives:Mucosal healing (MH) and deep remission (DR) are associated with improved outcomes in Crohn's disease (CD). However, most of the current data pertain to colonic MH and DR, whereas the evidence regarding the prevalence and impact of small bowel (SB) MH is scarce. The aim of this study was to to evaluate the prevalence of SBMH and DR in quiescent SBCD.Methods:Patients with known SBCD in clinical remission (CDAI<150) or with mild symptoms (CDAI<220) were prospectively recruited and underwent video capsule endoscopy after verification of SB patency. Inflammation was quantified using the Lewis score (LS). SBMH was defined as LS<135, whereas a significant inflammation was defined as LS>790. Clinico-biomarker remission was defined as a combination of clinical remission and normal biomarkers. DR was defined as a combination of clinico-biomarker remission and MH.Results:Fifty-six patients with proven SB patency were enrolled; 52 (92.9%) patients were in clinical remission and 21 (40.4%) in clinico-biomarker remission. SBMH was demonstrated in 8/52 (15.4%) of patients in clinical remission. Moderate-to-severe SB inflammation was demonstrated in 11/52 (21.1%) of patients in clinical remission and in 1/21 (4.7%) of patients in clinical and biomarker remission. Only 7/52 (13.5%) patients were in DR.Conclusions:SB inflammation is detected in the majority of CD patients in clinical and biomarker remission. SBMH and DR were rare and were independent of treatment modality. Our findings represent the true inflammatory burden in quiescent patients with SBCD. [ABSTRACT FROM AUTHOR]