Investigating the Spatial Distribution of Proliferating Cells in Primary Ovarian Cancers.

BACKGROUND: Ovarian cancer (OC) accounts for about 4% of female cancers globally. While Ki67-immunopositive (Ki67+) cell density is commonly used to assess proliferation in OC, the two-dimensional (2D) distribution pattern of these cells is poorly understood. This study explores the 2D distribution pattern of Ki67+ cells in primary OC tissues and models the proliferation process to improve our understanding of this hallmark of cancer. METHODS: A total of 100 tissue cores, included in a tissue microarray (TMA) representing 5 clear cell carcinomas, 62 serous carcinomas, 10 mucinous adenocarcinomas, 3 endometrioid adenocarcinomas, 10 lymph node metastases from OC, and 10 samples of adjacent normal ovary tissue, were stained using a standardized immunohistochemical protocol. The computer-aided image analysis system assessed the 2D distribution pattern of Ki67+ proliferating cells, providing the cell number and density, patterns of randomness, and cell-to-cell closeness. Three computer models were created to simulate behavior and responses, aiming to gain insights into the variations in the proliferation process. RESULTS: Significant differences in Ki67+ cell density were found between low- and high-grade serous carcinoma/mucinous adenocarcinomas (p = 0.003 and p = 0.01, respectively). The Nearest Neighbor Index of Ki67+ cells differed significantly between high-grade serous carcinomas and endometrioid adenocarcinomas (p = 0.01), indicating distinct 2D Ki67+ distribution patterns. Proxemics analysis revealed significant differences in Ki67+ cell-to-cell closeness between low- and high-grade serous carcinomas (p = 0.002). Computer models showed varied effects on the overall organization of Ki67+ cells and the ability to preserve the original 2D distribution pattern when altering the location and/or density of Ki67+ cells. CONCLUSIONS: Cell proliferation is a hallmark of OCs. This study provides new evidence that investigating the Ki67+ cell density and 2D distribution pattern can assist in understanding the proliferation status of OCs. Moreover, our computer models suggest that changes in Ki67+ cell density and their location are critical for maintaining the 2D distribution pattern.

Prostate cancer risk stratification via eNose urine odor analysis: a preliminary report.

Introduction: Prostate cancer (PCa) is known for its highly diverse clinical behavior, ranging from low-risk, slow-growing tumors to aggressive and life-threatening forms. To avoid over-treatment of low-risk PCa patients, it would be very important prior to any therapeutic intervention to appropriately classify subjects based on tumor aggressiveness. Unfortunately, there is currently no reliable test available for this purpose. The aim of the present study was to evaluate the ability of risk stratification of PCa subjects using an electronic nose (eNose) detecting PCa-specific volatile organic compounds (VOCs) in urine samples. Methods: The study involved 120 participants who underwent diagnostic prostate biopsy followed by robot assisted radical prostatectomy (RARP). PCa risk was categorized as low, intermediate, or high based on the D'Amico risk classification and the pathological grade (PG) assessed after RARP. The eNose's ability to categorize subjects for PCa risk stratification was evaluated based on accuracy and recall metrics. Results: The study population comprised 120 participants. When comparing eNose predictions with PG an accuracy of 79.2% (95%CI 70.8 - 86%) was found, while an accuracy of 74.2% (95%CI 65.4 - 81.7%) was found when compared to D'Amico risk classification system. Additionally, if compared low- versus -intermediate-/high-risk PCa, the eNose achieved an accuracy of 87.5% (95%CI 80.2-92.8%) based on PG or 90.8% (95%CI 84.2-95.3%) based on D'Amico risk classification. However, when using low-/-intermediate versus -high-risk PCa for PG, the accuracy was found to be 91.7% (95%CI 85.2-95.9%). Finally, an accuracy of 80.8% (95%CI72.6-87.4%) was found when compared with D'Amico risk classification. Discussion: The findings of this study indicate that eNose may represent a valid alternative not only for early and non-invasive diagnosis of PCa, but also to categorize patients based on tumor aggressiveness. Further studies including a wider sample population will be necessary to confirm the potential clinical impact of this new technology.

Is Artificial Intelligence the Key to Revolutionizing Benign Prostatic Hyperplasia Diagnosis and Management?

Benign prostatic hyperplasia (BPH) is a prevalent condition among older men that is characterized by the enlargement of the prostate gland and compression of the urethra, which often results in lower urinary tract symptoms, such as frequent urination, difficulty in starting urination, and incomplete bladder emptying. The development of BPH is thought to be primarily due to an imbalance between cell proliferation and apoptosis, underlying inflammation, epithelial-to-mesenchymal transition, and local paracrine and autocrine growth factors, although the exact molecular mechanisms are not yet fully understood. Anatomical structures considered natural and benign observations can occasionally present multi-parametric magnetic resonance imaging appearances that resemble prostate cancer (PCa), posing a risk of misinterpretation and generating false-positive outcomes and subsequently, unnecessary interventions. To aid in the diagnosis of BPH, distinguish it from PCa, and assist with treatment and outcome prediction, various Artificial Intelligence (AI)-based algorithms have been proposed to assist clinicians in the medical practice. Here, we explore the results of these new technological advances and discuss their potential to enhance clinicians' cognitive abilities and expertise. There is no doubt that AI holds extensive medical potential, but the cornerstone for secure, efficient, and ethical integration into diverse medical fields still remains well-structured clinical trials.

Is Artificial Intelligence the Key to Revolutionizing Benign Prostatic Hyperplasia Diagnosis and Management?

Benign prostatic hyperplasia (BPH) is a prevalent condition among older men that is characterized by the enlargement of the prostate gland and compression of the urethra, which often results in lower urinary tract symptoms, such as frequent urination, difficulty in starting urination, and incomplete bladder emptying. The development of BPH is thought to be primarily due to an imbalance between cell proliferation and apoptosis, underlying inflammation, epithelial-to-mesenchymal transition, and local paracrine and autocrine growth factors, although the exact molecular mechanisms are not yet fully understood. Anatomical structures considered natural and benign observations can occasionally present multi-parametric magnetic resonance imaging appearances that resemble prostate cancer (PCa), posing a risk of misinterpretation and generating false-positive outcomes and subsequently, unnecessary interventions. To aid in the diagnosis of BPH, distinguish it from PCa, and assist with treatment and outcome prediction, various Artificial Intelligence (AI)-based algorithms have been proposed to assist clinicians in the medical practice. Here, we explore the results of these new technological advances and discuss their potential to enhance clinicians’ cognitive abilities and expertise. There is no doubt that AI holds extensive medical potential, but the cornerstone for secure, efficient, and ethical integration into diverse medical fields still remains well-structured clinical trials (AU)

Prostate Cancer Microvascular Routes: Exploration and Measurement Strategies.

Angiogenesis is acknowledged as a pivotal feature in the pathology of human cancer. Despite the absence of universally accepted markers for gauging the comprehensive angiogenic activity in prostate cancer (PCa) that could steer the formulation of focused anti-angiogenic treatments, the scrutiny of diverse facets of tumoral blood vessel development may furnish significant understanding of angiogenic processes. Malignant neoplasms, encompassing PCa, deploy a myriad of strategies to secure an adequate blood supply. These modalities range from sprouting angiogenesis and vasculogenesis to intussusceptive angiogenesis, vascular co-option, the formation of mosaic vessels, vasculogenic mimicry, the conversion of cancer stem-like cells into tumor endothelial cells, and vascular pruning. Here we provide a thorough review of these angiogenic mechanisms as they relate to PCa, discuss their prospective relevance for predictive and prognostic evaluations, and outline the prevailing obstacles in quantitatively evaluating neovascularization via histopathological examinations.

Fractal nature of human gastrointestinal system: Exploring a new era.

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.