Artificial Intelligence Program to Predict p53 Mutations in Ulcerative Colitis-Associated Cancer or Dysplasia

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.

Artificial intelligence: A new tool in the pathologist's armamentarium for the diagnosis of IBD

Inflammatory bowel diseases (IBD) are classified into two entities, namely Crohn's disease (CD) and ulcerative colitis (UC), which differ in disease trajectories, genetics, epidemiological, clinical, endoscopic, and histopathological aspects. As no single golden standard modality for diagnosing IBD exists, the differential diagnosis among UC, CD, and non-IBD involves a multidisciplinary approach, considering professional groups that include gastroenterologists, endoscopists, radiologists, and pathologists. In this context, histological examination of endoscopic or surgical specimens plays a fundamental role. Nevertheless, in differentiating IBD from non-IBD colitis, the histopathological evaluation of the morphological lesions is limited by sampling and subjective human judgment, leading to potential diagnostic discrepancies. To overcome these limitations, artificial intelligence (AI) techniques are emerging to enable automated analysis of medical images with advantages in accuracy, precision, and speed of investigation, increasing interest in the histological analysis of gastrointestinal inflammation. This review aims to provide an overview of the most recent knowledge and advances in AI methods, summarizing its applications in the histopathological analysis of endoscopic biopsies from IBD patients, and discussing its strengths and limitations in daily clinical practice.

Artificial intelligence and machine learning technologies in ulcerative colitis

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.

The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis

BACKGROUND

Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27 M mutation carry a poor prognosis. In recent years, machine learning (ML)-based radiomics have shown promising results in predicting gene mutation status non-invasively. Therefore, this study aims to comprehensively evaluate the diagnostic performance of ML-based magnetic resonance imaging radiomics in predicting H3K27 M mutation status in DMG patients.

METHODS

A systematic search was conducted using relevant keywords in PubMed/Medline, Scopus, Embase, and Web of Science from inception to May 2023. Original studies evaluating the diagnostic performance of ML models in predicting H3K27 M mutation status in DMGs were enrolled. Quality assessment of the enrolled studies was conducted using QUADAS-2. Data were analyzed using STATA version 17.0 to calculate pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic score, and diagnostic odds ratio.

RESULTS

A total of 13 studies, including 12 retrospectives and 1 both retrospective and prospective study, enrolled 1510 (male = 777) DMG patients. Six studies underwent meta-analysis which showed a pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic score, and diagnostic odds ratio of 0.91 (95% CI 0.77-0.97), 0.81 (95% CI 0.73-0.88), 4.86 (95% CI 3.25-7.24), 0.11 (95% CI 0.04-0.29), 3.75 (95% CI 2.62-4.88), and 42.61 (95% CI 13.77-131.87), respectively.

CONCLUSIONS

Non-invasive prediction of H3K27 M mutation status in patients with DMGs using magnetic resonance imaging radiomics is a promising tool with good diagnostic performance. However, the pooled metrics had a wide confidence interval, which required further studies to enhance ML algorithms' accuracy and facilitate their integration into daily clinical practice.

Emerging insights into ethnic-specific TP53 germline variants

The recent expansion of human genomics repositories has facilitated the discovery of novel TP53 variants in populations of different ethnic origins. Interpreting TP53 variants is a major clinical challenge because they are functionally diverse, confer highly variable predisposition to cancer (including elusive low-penetrance alleles), and interact with genetic modifiers that alter tumor susceptibility. Here, we discuss how a cancer risk continuum may relate to germline TP53 mutations on the basis of our current review of genotype-phenotype studies and an integrative analysis combining functional and sequencing datasets. Our study reveals that each ancestry contains a distinct TP53 variant landscape defined by enriched ethnic-specific alleles. In particular, the discovery and characterization of suspected low-penetrance ethnic-specific variants with unique functional consequences, including P47S (African), G334R (Ashkenazi Jewish), and rs78378222 (Icelandic), may provide new insights in terms of managing cancer risk and the efficacy of therapy. Additionally, our analysis highlights infrequent variants linked to milder cancer phenotypes in various published reports that may be underdiagnosed and require further investigation, including D49H in East Asians and R181H in Europeans. Overall, the sequencing and projected functions of TP53 variants arising within ethnic populations and their interplay with modifiers, as well as the emergence of CRISPR screens and AI tools, are now rapidly improving our understanding of the cancer susceptibility spectrum, leading toward more accurate and personalized cancer risk assessments.

The pathobiology of follicular lymphoma

Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6, the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.

New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients

Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.

Evaluating the potential of artificial intelligence in ulcerative colitis

INTRODUCTION

Diagnosis and therapeutic management in ulcerative colitis (UC) relies on a combination of endoscopic and histological scorings which are difficult to objectively quantify. Artificial intelligence (AI) might overcome the current issues of inter-observer variability, repetitive need for biopsies and estimation of disease activity medicine currently encourages.

AREAS COVERED

With this narrative literature review we aim to provide a clear and critical overview of the recent evolutions in the field of AI and UC, based on a literature search performed on Pubmed, Embase and Cochrane Library. The major focus of this review is the use of AI for endoscopic assessment of disease activity and the correlation with histology and long-term outcome. Moreover, we elucidate on the more recent developments in the field of AI as support in histological disease assessment, surveillance, therapy monitoring and natural language processing.

EXPERT OPINION

UC management is evolving with AI impacting nearly every aspect of it. The immediate future influence of AI in UC management will be focussed on the collection, extraction and organization of particular clinical information. Expect is the transformation toward a real-time standardized, reproducible, objective and high-reliable disease grading, especially in endoscopy, histology and eventually radiology applications for UC.