ABSTRACT
Rectal cancer (RC) is a globally prevalent malignant tumor, presenting significant challenges in its management and treatment. Currently, magnetic resonance imaging (MRI) offers superior soft tissue contrast and radiation-free effects for RC patients, making it the most widely used and effective detection method. In early screening, radiologists rely on patients' medical radiology characteristics and their extensive clinical experience for diagnosis. However, diagnostic accuracy may be hindered by factors such as limited expertise, visual fatigue, and image clarity issues, resulting in misdiagnosis or missed diagnosis. Moreover, the distribution of surrounding organs in RC is extensive with some organs having similar shapes to the tumor but unclear boundaries; these complexities greatly impede doctors' ability to diagnose RC accurately. With recent advancements in artificial intelligence, machine learning techniques like deep learning (DL) have demonstrated immense potential and broad prospects in medical image analysis. The emergence of this approach has significantly enhanced research capabilities in medical image classification, detection, and segmentation fields with particular emphasis on medical image segmentation. This review aims to discuss the developmental process of DL segmentation algorithms along with their application progress in lesion segmentation from MRI images of RC to provide theoretical guidance and support for further advancements in this field.
ABSTRACT
Background: Although observational studies suggest a correlation between psoriasis (PS) and cancers, it is still unknown whether this association can replace causal relationships due to the limitations of observational studies. Therefore, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationship between PS and cancers. Methods: PS genetic summary data were obtained from two genome-wide association studies (GWAS). We employed MR Base for individuals retrieving tumors from distinct locations. Inverse-variance weighted analysis was the principal method used for MR, supplemented by weighted median, MR Egger, simple mode, and weighted mode. To investigate the possible link between psoriasis and cancers, we performed two independent two-sample MR studies and a meta-analysis based on two independent MR analyses. Results: Two independent MR analyses both found no significant causal relationship between PS and overall cancers (OR=1.0000, 95% confidence interval [CI]:0.9999-1.0001, P=0.984; OR=1.0000, 95% CI:0.9999-1.0001, P=0.761), and no significant causal relationship with 17 site-specific cancers. In the meta-analysis conducted by two two-sample MR analyses, there was no significant causal relationship between PS and overall cancers (OR=1.0000, 95% CI: 0.9999-1.0001, P=1.00, I 2 = 0.0%), and there was no significant causal relationship with 17 site-specific cancers. Conclusions: Our findings do not support a genetic link between PS and cancers. More population-based and experimental investigations will be required better to understand the complicated relationship between PS and cancers.
ABSTRACT
INTRODUCTION: The link between cruciferous vegetables (CVs) and ovarian cancer (OC) is still uncertain. This meta-analysis intended to investigate the association between CVs consumption and the risk of OC, as well as to conduct a dose-response analysis to determine the degree of correlation between them. METHODS: We systematically searched PubMed, Web of Science, Embase, and Cochrane Library databases between database creation and October 2023. The present meta-analysis has been duly registered and assigned the registration number CRD42023470299. This study followed the PRISMA guidelines. The statistical analysis was performed using Stata 14.0 software. RESULTS: There were a total of 7 cohort studies and 7 case-control studies with 7,269 cases and 742,952 subjects. The combined relative risk (RR) of the highest intake of CVs was 0.90 (95% confidence intervals [CIs]: 0.84-0.96; I2=54.7%; P=0.007) compared to the lowest intake of CVs. The odds ratio (OR) was 0.97 (95% CIs: 0.86-1.08; P=0.192) for case-control studies, and the RR was 0.79 (95% CIs: 0.67-0.91; P=0.167) for cohort studies. The intake of CVs and the risk of OC were linearly correlated. Adding 15 grams of CVs to the diet each day decreased the likelihood of developing OC by almost 4% (RR=0.963, 95% CIs: 0.905-1.025; P=0.235). CONCLUSIONS: Consumption of CVs may be linked to a lower risk of OC.
ABSTRACT
The prevalence of pelvic organ prolapse (POP) has been steadily increasing over the years, rendering it a pressing global health concern that significantly impacts women's physical and mental wellbeing as well as their overall quality of life. With the advancement of three-dimensional reconstruction and computer simulation techniques for pelvic floor structures, research on POP has progressively shifted toward a biomechanical focus. Finite element (FE) analysis is an established tool to analyze the biomechanics of complex systems. With the advancement of computer technology, an increasing number of researchers are now employing FE analysis to investigate the pathogenesis of POP in women. There is a considerable number of research on the female pelvic FE analysis and to date there has been less review of this technique. In this review article, we summarized the current research status of FE analysis in various types of POP diseases and provided a detailed explanation of the issues and future development in pelvic floor disorders. Currently, the application of FE analysis in POP is still in its exploratory stage and has inherent limitations. Through continuous development and optimization of various technologies, this technique can be employed with greater accuracy to depict the true functional state of the pelvic floor, thereby enhancing the supplementation of the POP mechanism from the perspective of computer biomechanics.
