Three-Dimensional Nanoscale Nuclear Architecture Mapping of Rectal Biopsies Detects Colorectal Neoplasia in Patients with Inflammatory Bowel Disease

Perspective on quantitative phase imaging to improve precision cancer medicine

Significance

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states.

Aim

This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection.

Approach

We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging.

Results

QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research.

Conclusions

QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now

Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.

Correlation of Serum CA242, CA724, and TPA Levels with Clinicopathological Features and Prognosis in Patients with Inflammatory Bowel Disease Complicated with Rectal Cancer

Objective. To investigate the correlation of serum cancer antigen 242 (CA242), cancer antigen 72-4 (CA724), and tissue polypeptide antigen (TPA) levels with clinicopathological features and prognosis in patients with inflammatory bowel disease (IBD) complicated with rectal cancer. Methods. The data of 120 patients with IBD were retrospectively analyzed. Patients were divided into the IBD group (without rectal cancer, $n=60$ ) and the rectal cancer group (with rectal cancer, $n=60$ ), and 60 healthy individuals receiving medical examination during the same period were selected as the healthy group. Serum CA242, CA724, and TPA levels of research subjects were measured by enzyme-linked immunosorbent assay (ELISA). Meanwhile, the clinical data of the patients were collected. The patients were followed up for 3 years and divided into the survival group and the dead group. The relationship between the levels of CA242, CA724, TPA, and prognosis was tested. Results. Significant differences were found in the serum CA242, CA724, and TPA levels among three groups ( $P<0.001$ ). CA242 was related to tumor size, histological stage, growth mode, and TNM stage in patients with IBD and rectal cancer. CA724 was related to histological stage, growth mode, depth of tumor invasion (T stage), lymph node metastasis (N stage), distant metastasis (M stage), and TNM stage in patients with IBD and rectal cancer. TPA was related to histological stage, T stage, M stage, and TNM stage in patients with IBD and rectal cancer. Serum CA242, CA724, and TPA levels in the survival group were significantly lower than those in the dead group after 3 years ( $P<0.001$ ). As for the combined prediction of serum CA242, CA724, and TPA for patients’ prognosis, the confidence interval was 0.000-1.000, AUC was 0.875, standard error was 0.093, and sensitivity was 0.750. Conclusion. Serum CA242, CA724, and TPA levels are closely related to the clinicopathological features such as location, stage, and metastasis of rectal cancer. The combined detection of serum CA242, CA724, and TPA levels has a significant correlation with the prognosis of patients with rectal cancer, which can be used in monitoring the disease progression.

Artificial intelligence and colonoscopy − enhancements and improvements

Artificial intelligence is a technology that processes and analyzes information with reproducibility and accuracy. Its application in medicine, especially in the field of gastroenterology, has great potential to facilitate in diagnosis of various disease states. Currently, the role of artificial intelligence as it pertains to colonoscopy revolves around enhanced polyp detection and characterization. The aim of this article is to review the current and potential future applications of artificial intelligence for enhanced quality of detection for colorectal neoplasia.

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

Field carcinogenesis for risk stratification of colorectal cancer

Colorectal cancer (CRC) is characterized by genetic-environmental interplay leading to diffuse changes in the entire colonic mucosa (field carcinogenesis or field of injury) and to a pro-neoplastic genetic/epigenetic/physiological milieu. The clinical consequences are increased risk of synchronous and metachronous neoplasia. Factors such as genetics, race, ethnicity, age, and socioeconomic status are thought to influence neoplasia development. Here, we explore the potential improvement to CRC screening through exploiting field carcinogenesis, with particular focus on racial disparities and chemoprevention strategies. Also, we discuss future directions for field carcinogenesis/risk stratification using molecular and novel biophotonic techniques for personalized CRC screening.