Resectable and borderline resectable pancreatic ductal adenocarcinoma: Role of the radiologist and oncologist in the era of precision medicine

The incidence and mortality of pancreatic ductal adenocarcinoma are growing over time. The management of patients with pancreatic ductal adenocarcinoma involves a multidisciplinary team, ideally involving experts from surgery, diagnostic imaging, interventional endoscopy, medical oncology, radiation oncology, pathology, geriatric medicine, and palliative care. An adequate staging of pancreatic ductal adenocarcinoma and re-assessment of the tumor after neoadjuvant therapy allows the multidisciplinary team to choose the most appropriate treatment for the patient. This review article discusses advancement in the molecular basis of pancreatic ductal adenocarcinoma, diagnostic tools available for staging and tumor response assessment, and management of resectable or borderline resectable pancreatic cancer

Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images

Background and objective Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease prevalence, absence of a cure, and low diagnosis rates make this disease a public health problem. The diagnosis of CD predominantly relies on recognizing characteristic mucosal alterations of the small intestine, such as villous atrophy, crypt hyperplasia, and intraepithelial lymphocytosis. However, these changes are not entirely specific to CD and overlap with Non-Celiac Duodenitis (NCD) due to various etiologies. We investigated whether Artificial Intelligence (AI) models could assist in distinguishing normal, CD, and NCD (and unaffected individuals) based on the characteristics of small intestinal lamina propria (LP). Methods Our method was developed using a dataset comprising high magnification biopsy images of the duodenal LP compartment of CD patients with different clinical stages of CD, those with NCD, and individuals lacking an intestinal inflammatory disorder (controls). A pre-processing step was used to standardize and enhance the acquired images. Results For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all the tested classifiers by achieving 98.55% ACC. Conclusions To the best of our knowledge, this is the first study that documents automated differentiation between normal, NCD, and CD biopsy images. These findings are a stepping stone toward automated biopsy image analysis that can significantly benefit patients and healthcare providers

Diagnosis and Treatment of Small Bowel Disorders

Over the last few decades, remarkable progress has been made in understanding the aetiology and pathophysiology of diseases and many new theories emphasize the importance of the small bowel ‘ecosystem’ in the pathogenesis of acute and chronic illness. Emerging factors such as microbiome, stem cells, innate intestinal immunity and the enteric nervous system along with mucosal and endothelial barriers have key role in the development of gastrointestinal and extra-intestinal diseases. Therefore, the small intestine is considered key player in metabolic disease development, including diabetes mellitus, and other diet-related disorders such as celiac and non-celiac enteropathies. Another major field is drug metabolism and its interaction with microbiota. Moreover, the emergence of gut-brain, gut-liver and gut-blood barriers points toward the important role of small intestine in the pathogenesis of common disorders, such as liver disease, hypertension and neurodegenerative disease. However, the small bowel remains an organ that is difficult to fully access and assess and accurate diagnosis often poses a clinical challenge. Eventually, the therapeutic potential remains untapped. Therefore, it is due time to direct our interest towards the small intestine and unravel the interplay between small-bowel and other gastrointestinal (GI) and non-GI related maladies

INVESTIGATING THE IMPACT OF EXPLANATION ON REPAIRING TRUST IN AI DIAGNOSTIC SYSTEMS FOR RE-DIAGNOSIS

AI systems are increasingly being fielded to support diagnoses and healthcare advice for patients. One promise of AI application is that they might serve as the first point of contact for patients, replacing routine tasks, and allowing health care professionals to focus on more challenging and critical aspects of healthcare. For AI systems to succeed, they must be designed based on a good understanding of how physicians explain diagnoses to patients, and how prospective patients understand and trust the systems providing the diagnosis, as well as the explanations they expect. In this thesis, I examine this problem across three studies. In the first study, I interviewed physicians to explore their explanation strategies in re-diagnosis scenarios. I identified five broad categories of explanation strategies and I developed a generic diagnostic timeline of explanations from the interviews. For the second study, I tested an AI diagnosis scenario and found that explanation helps improve patient satisfaction measures for re-diagnosis. Finally, in a third study I implemented different forms of explanation in a similar diagnosis scenario and found that visual and example-based explanation integrated with rationales had a significantly better impact on patient satisfaction and trust than no explanations, or with text-based rationales alone. Based on these studies and the review of the literature, I provide some design recommendations for the explanations offered for AI systems in the healthcare domain

Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics

Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and clinically applicable diagnostics. Here, we argue that a causal perspective improves the identification of these challenges, and formalizes their relation to the robustness and generalization of machine learning-based diagnostics. To make for a concrete discussion, we focus on a specific, recently established high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs). We discuss how the main biological and experimental factors of the AIRR domain may influence the learned biomarkers and provide easily adjustable simulations of such effects. In conclusion, we find that causal modeling improves machine learning-based biomarker robustness by identifying stable relations between variables and by guiding the adjustment of the relations and variables that vary between populations

Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children

University of Virginia Center for Engineering in Medicine GrantBill and Melinda Gates Foundation grants OPP1066203 and OPP1066118University of Virginia THRIV Scholar Career Development Award awarded to Dr Syed