Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies.

BACKGROUND: Numerous meta-analyses have explored the association between the triglyceride-glucose (TyG) index and diverse health outcomes, yet the comprehensive assessment of the scope, validity, and quality of this evidence remains incomplete. Our aim was to systematically review and synthesise existing meta-analyses of TyG index and health outcomes and to assess the quality of the evidence. METHODS: A thorough search of PubMed, EMBASE, and Web of Science databases was conducted from their inception through to 8 April 2024. We assessed the quality of reviews using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. This study was registered with PROSPERO (CRD: 42024518587). RESULTS: Overall, a total of 95 associations from 29 meta-analyses were included, investigating associations between TyG index and 30 health outcomes. Of these, 83 (87.4%) associations were statistically significant (P < 0.05) according to the random effects model. Based on the AMSTAR tool, 16 (55.2%) meta-analyses were high quality and none was low quality. The certainty of the evidence, assessed by the GRADE framework, showed that 6 (6.3%) associations were supported by moderate-quality evidence. When compared with the lowest category of the TyG index, the risk of contrast-induced nephropathy (CIN) [relative risk (RR) = 2.25, 95%CI 1.82, 2.77], the risk of stroke in patients with diabetes mellitus (RR = 1.26, 95%CI 1.18, 1.33) or with acute coronary syndrome disease (RR = 1.56, 95%CI 1.06, 2.28), the prognosis of coronary artery disease (CAD)-non-fatal MI (RR = 2.02, 95%CI 1.32, 3.10), and the severity of CAD including coronary artery stenosis (RR = 3.49, 95%CI 1.71, 7.12) and multi-vessel CAD (RR = 2.33, 95%CI 1.59, 3.42) increased with high TyG index. CONCLUSION: We found that the TyG index was positively associated with many diseases including the risk of CIN and stroke, the prognosis of CAD, and the severity of CAD which were supported by moderate-quality evidence. TyG index might be useful to identify people at high-risk for developing these diseases.

Human papillomavirus infection and the risk of cancer at specific sites other than anogenital tract and oropharyngeal region: an umbrella review.

BACKGROUND: Despite numerous studies having evaluated the associations between human papillomavirus (HPV) infection and risk of specific cancers other than anogenital tract and oropharyngeal, the findings are inconsistent and the quality of evidence has not been systematically quantified. We aimed to summarise the existing evidence as well as to evaluate the strength and credibility of these associations. METHODS: We conducted an umbrella review of systematic reviews and meta-analyses of observational studies. PubMed, EMBASE, and Web of Science were searched from inception to March 2024. Studies with systematic reviews and meta-analyses that examined associations between HPV or HPV-associated genotypes infection and specific cancers were eligible for this review. The quality of the methodology was evaluated using A Measurement Tool to Assess systematic Reviews (AMSTAR). The credibility of the evidence was assessed using GRADE. The protocol was preregistered with PROSPERO (CRD42023439070). FINDINGS: The umbrella review identified 31 eligible studies reporting 87 associations with meta-analytic estimates, including 1191 individual studies with 336,195 participants. Of those, 29 (93.5%) studies were rated as over moderate quality by AMSTAR. Only one association indicating HPV-18 infection associated with an increased risk of breast cancer (odds ratio [OR] = 3.48, 95% confidence interval [CI] = 2.24-5.41) was graded as convincing evidence. There were five unique outcomes identified as highly suggestive evidence, including HPV infection increased the risk of oral squamous cell carcinoma (OR = 7.03, 95% CI = 3.87-12.76), oesophageal cancer (OR = 3.32, 95% CI = 2.54-4.34), oesophageal squamous cell carcinoma (OR = 2.69, 95% CI = 2.05-3.54), lung cancer (OR = 3.60, 95% CI = 2.59-5.01), and breast cancer (OR = 6.26, 95% CI = 4.35-9.00). According to GRADE, one association was classified as high, indicating that compared with the controls in normal tissues, HPV infection was associated with an increased risk of breast cancer. INTERPRETATION: The umbrella review synthesised up-to-date observational evidence on HPV infection with the risk of breast cancer, oral squamous cell carcinoma, oesophageal cancer, oesophageal squamous cell carcinoma, and lung cancer. Further larger prospective cohort studies are needed to verify the associations, providing public health recommendations for prevention of disease. FUNDING: National Key Research and Development Program of China, Natural Science Foundation of China, Outstanding Scientific Fund of Shengjing Hospital of China Medical University, and 345 Talent Project of Shengjing Hospital of China Medical University.

Role of artificial intelligence in digital pathology for gynecological cancers.

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

SH3RF2 contributes to cisplatin resistance in ovarian cancer cells by promoting RBPMS degradation.

Platinum-based chemotherapy remains one of the major choices for treatment of ovarian cancer (OC). However, primary or acquired drug resistance severely impairs their efficiency, thereby causing chemotherapy failure and poor prognosis. SH3 domain containing ring finger 2 (SH3RF2) has been linked to the development of cancer. Here we find higher levels of SH3RF2 in the tumor tissues from cisplatin-resistant OC patients when compared to those from cisplatin-sensitive patients. Similarly, cisplatin-resistant OC cells also express higher levels of SH3RF2 than normal OC cells. Through in vitro and in vivo loss-of-function experiments, SH3RF2 is identified as a driver of cisplatin resistance, as evidenced by increases in cisplatin-induced cell apoptosis and DNA damage and decreases in cell proliferation induced by SH3RF2 depletion. Mechanistically, SH3RF2 can directly bind to the RNA-binding protein mRNA processing factor (RBPMS). RBPMS has been reported as an inhibitor of cisplatin resistance in OC. As a E3 ligase, SH3RF2 promotes the K48-linked ubiquitination of RBPMS to increase its proteasomal degradation and activator protein 1 (AP-1) transactivation. Impairments in RBPMS function reverse the inhibitory effect of SH3RF2 depletion on cisplatin resistance. Collectively, the SH3RF2-RBPMS-AP-1 axis is an important regulator in cisplatin resistance and inhibition of SH3RF2 may be a potential target in preventing cisplatin resistance.

Adherence to Sulfur Microbial Diet and Ovarian Cancer Survival: Evidence from a Prospective Cohort Study.

SCOPE: The study aims to investigate the role of the sulfur microbial diet in the survival of ovarian cancer (OC). METHODS AND RESULTS: A prospective cohort study is conducted with 703 patients diagnosed with OC between 2015 and 2020. Diet information is collected using a validated food frequency questionnaire. Deaths are ascertained up to March 31, 2021, via the death registry linkage. During the follow-up period (median: 37.2 months, interquartile range: 24.7-50.2 months), 130 deaths are observed. A higher sulfur microbial diet score is significantly associated with an increased risk of all-cause mortality among OC patients (tertile 3 vs tertile 1: HR = 1.93, 95% CI = 1.11-3.35). Each 1-standard deviation increment in the sulfur microbial diet score increases the all-cause mortality risk by 33% (95% CI = 1.04-1.71). Stratified analysis shows that significant associations are found in OC patients diagnosed over 50 years of age, with body mass index ≥24  kg m-2 , who changed their diet after diagnosis, or without residual lesions. CONCLUSIONS: Adherence to the sulfur microbial diet, characterized by high intakes of red meats and processed meats, and low intakes of fruits, vegetables, and whole grains, is associated with poor survival in OC patients.

Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes.

Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets.

BACKGROUND AND AIMS: It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets. METHODS: Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892). RESULTS: We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity. CONCLUSIONS: Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.

Association of pre-diagnosis specific color groups of fruit and vegetable intake with ovarian cancer survival: results from the ovarian cancer follow-up study (OOPS).

Background: The colors of fruits and vegetables (FV) reflect the presence of pigmented bioactive compounds. The evidence of pre-diagnosis specific FV color group intake contributing to ovarian cancer (OC) survival is limited and inconsistent. Methods: A prospective cohort study was conducted between 2015 and 2020 with 700 newly diagnosed OC patients. Pre-diagnosis dietary information was assessed by a validated food frequency questionnaire. We classified FV into five groups based on the color of their edible parts (e.g., green, red/purple, orange/yellow, white, and uncategorized groups). Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of specific color groups of FV before diagnosis with OC survival. Potential multiplicative and additive interactions were assessed. Results: 130 patients died during a median follow-up of 37.57 (interquartile: 24.77-50.20) months. We observed the improved survival with a higher pre-diagnosis intake of total FV (HRtertile 3 vs. tertile 1 = 0.63, 95%CI = 0.40-0.99), total vegetables (HRtertile 3 vs. tertile 1 = 0.57, 95%CI = 0.36-0.90), and red/purple FV (HRtertile 3 vs. tertile 1 = 0.52, 95%CI = 0.33-0.82). In addition, we observed significant dose-response relationships for per standard deviation increment between total vegetable intake (HR = 0.79, 95%CI = 0.65-0.96) and red/purple group intake (HR = 0.77, 95%CI = 0.60-0.99) before diagnosis with OC survival. Additionally, pre-diagnosis green FV intake was borderline associated with better OC survival (HRper standard deviation increment = 0.83; 95%CI = 0.69-1.00). In contrast, we did not observe significant associations between pre-diagnosis intake of total fruits, orange/yellow, white, and uncategorized groups and OC survival. Conclusion: Pre-diagnosis FV intake from various color groups, especially the green and red/purple ones, may improve OC survival. Further studies are needed to validate our findings.

Diet quality and survival after ovarian cancer: results from an ovarian cancer follow-up study (OOPS).

Background and aims: Owing to the limited studies and controversial evidence, the connection between diet quality and survival of patients with ovarian cancer (OC) has been indistinct. Our study intends to first investigate this topic based on Chinese diet quality scores. Methods: Our data come from an ovarian cancer follow-up study, which includes 796 patients with OC between 2015 and 2020. Three diet quality scores, including the Chinese Healthy Eating Index (CHEI), Dietary Balance Index (DBI), and Chinese Food Pagoda Score (CFPS), were calculated using a validated 111-item food frequency questionnaire. We used the Cox proportional hazards regression model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Potential multiplicative and additive interactions were also assessed. Results: With a median follow-up time of 37.17 months (interquartile: 24.73-50.17 months), we recorded 130 deaths. According to comparisons of the highest to lowest tertile of scores, the pre-diagnosis CHEI was linked to better overall survival (OS) in patients (HR = 0.56, 95% CI: 0.36, 0.88). A dose-response relationship between CHEI and OS was also observed (HR = 0.85, 95% CI: 0.71, 1.00, per 1 standard deviation increment). However, no evidence of significant associations between DBI and CFPS with OS was observed. Additionally, significant multiplicative and additive interactions were seen in the diet quality scores (CHEI and DBI) with the body mass index and the menopausal status. Conclusions: A high CHEI was associated with an improved OS for patients with OC, while DBI and CFPS were unrelated to OC survival.

A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images.

BACKGROUND: Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign. PURPOSE: This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach. METHODS: EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split-sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions. RESULTS: A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively. CONCLUSIONS: We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non-invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.

Non-Genetic Factors and Risk of Cervical Cancer: An Umbrella Review of Systematic Reviews and Meta-Analyses of Observational Studies.

Objectives: The association between non-genetic risk factors and cervical cancer (CC) remains controversial and unclear. This umbrella review was conducted to evaluate and synthesize previously published systematic reviews and meta-analyses related to non-genetic factors and CC risk. Methods: We searched PubMed, Web of Science, and EMBASE to identify studies investigating the association between extragenetic factors and CC risk. For each article, we calculated the summary effect size and the 95% confidence interval. Specific criteria were used to classify the association into four levels: strong, highly suggestive, suggestive, or weak. Results: A total of 18 meta-analyses of different risk factors for CC were examined; these studies covered risk factors related to diet, lifestyle, reproduction, disease, viral infection, microorganisms, and parasites. Oral contraceptive use and Chlamydia trachomatis infection were shown to increase CC risk, and this was supported by strong evidence. Additionally, there were four risk factors supported by highly suggestive evidence and six risk factors supported by suggestive evidence. Conclusion: In conclusion, there is a strong association between oral contraceptive use, Chlamydia trachomatis infection, and increased CC risk.

Ovarian Cancer and Parkinson's Disease: A Bidirectional Mendelian Randomization Study.

(1) Background: Ovarian cancer (OC) and Parkinson's disease (PD) represent a huge public health burden. The relationship of these two diseases is suggested in the literature while not fully understood. To better understand this relationship, we conducted a bidirectional Mendelian ran-domization analysis using genetic markers as a proxy. (2) Methods: Utilizing single nucleotide polymorphisms associated with PD risk, we assessed the association between genetically predicted PD and OC risk, overall and by histotypes, using summary statistics from previously conducted genome-wide association studies of OC within the Ovarian Cancer Association Consortium. Similarly, we assessed the association between genetically predicted OC and PD risk. The inverse variance weighted method was used as the main method to estimate odds ratios (OR) and 95% confidence intervals (CI) for the associations of interest. (3) Results: There was no significant association between genetically predicted PD and OC risk: OR = 0.95 (95% CI: 0.88-1.03), or between genetically predicted OC and PD risk: OR = 0.80 (95% CI: 0.61-1.06). On the other hand, when examined by histotypes, a suggestive inverse association was observed between genetically predicted high grade serous OC and PD risk: OR = 0.91 (95% CI: 0.84-0.99). (4) Conclusions: Overall, our study did not observe a strong genetic association between PD and OC, but the observed potential association between high grade serous OC and reduced PD risk warrants further investigation.

PM2.5 exposure and its interaction of oxidative balance score on ovarian cancer survival: A prospective cohort study.

Recent evidence advises particles with a diameter of 2.5 µm or less (PM2.5) might be a prognostic factor for ovarian cancer (OC) survival. The oxidative balance score (OBS) incorporates diet-lifestyle factors to estimate individuals' anti-oxidant exposure status which may be relevant to cancer prognosis. We aimed to investigate the roles of PM2.5, and OBS and their interaction in OC prognosis. 663 patients with OC were enrolled in the current study. Satellite-derived annual average exposures to PM2.5 based on patients' residential locations. The OBS was calculated based on 16 different diet-lifestyle components derived using an acknowledged self-reported questionnaire. The Cox regression model was performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS). We also assessed the effect of modification between PM2.5 and OS by OBS via interaction terms. During a median follow-up of 37.57 (interquartile:35.27-40.17) months, 123 patients died. Compared to low-concentration PM2.5 exposure, high PM2.5 during 1 year before diagnosis was associated with worse OC survival (HR= 1.19, 95% CI = 1.01-1.42). We observed an improved OS with the highest compared with the lowest OBS (HR = 0.46, 95% CI = 0.27-0.79, P for trend < 0.05). Notably, we also found an additive interaction between low OBS and high exposure to PM2.5, with the corresponding associations of PM2.5 being more pronounced among participants with lower OBS (HR = 1.42, 95% CI = 1.09-1.86). PM2.5 may blunt OC survival, but high OBS represented an antioxidative performance that could alleviate the adverse association of PM2.5 and OS.

Association of long-term particulate matter exposure with all-cause mortality among patients with ovarian cancer: A prospective cohort.

BACKGROUND: Evidence of the association between particles with a diameter of 2.5 µm or less (PM2.5) in long term and ovarian cancer (OC) mortality is limited. METHODS: This prospective cohort study analyzed data collected between 2015 and 2020 from 610 newly diagnosed OC patients, aged 18-79 years. The residential average PM2.5 concentrations 10 years before the date of OC diagnosis were assessed by random forest models at a 1 km × 1 km resolution. Cox proportional hazard models fully adjusted for the covariates (including age at diagnosis, education, physical activity, kitchen ventilation, FIGO stage, and comorbidities) and distributed lag non-linear models were used to estimate the hazard ratios (HRs) and 95 % confidence intervals (CIs) of PM2.5 and all-cause mortality of OC. RESULTS: During a median follow-up of 37.6 months (interquartile: 24.8-50.5 months), 118 (19.34 %) deaths were confirmed among 610 OC patients. One-year PM2.5 exposure levels before OC diagnosis was significantly associated with an increase in all-cause mortality among OC patients (single-pollutant model: HR = 1.22, 95 % CI: 1.02-1.46; multi-pollutant models: HR = 1.38, 95 % CI: 1.10-1.72). Furthermore, during 1 to 10 years prior to diagnosis, the lag-specific effect of long-term PM2.5 exposure on the all-cause mortality of OC had a risk increase for lag 1-6 years, and the exposure-response relationship was linear. Of note, significant interactions between several immunological indicators as well as solid fuel use for cooking and ambient PM2.5 concentrations were observed. CONCLUSION: Higher ambient PM2.5 concentrations were associated with an increased risk of all-cause mortality among OC patients, and there was a lag effect in long-term PM2.5 exposure.

The association of macronutrient quality and its interactions with energy intake with survival among patients with ovarian cancer: results from a prospective cohort study.

BACKGROUND: Emerging evidence supports shifting the focus from the quantity of macronutrients to quality to obtain greater benefits for the prognosis of ovarian cancer (OC). Additionally, despite the high relevance between macronutrient quality and quantity, the interaction of these parameters on OC survival remains unknown. OBJECTIVE: A multidimensional macronutrient quality index (MQI) was applied to investigate the association between overall macronutrient quality and the survival of patients with OC. METHODS: A prospective cohort study was conducted with 701 females diagnosed with OC who were enrolled from 2015 to 2020. Dietary intake information was obtained from a validated food frequency questionnaire. The MQI was calculated based on 3 quality indices: carbohydrate quality index (CQI), fat quality index (FQI), and protein quality index (PQI). Cox proportional hazards regression was conducted to calculate HRs and 95% CIs. Furthermore, we evaluated whether energy intake status (total energy intake and energy balance) modified the association between MQI and OC survival. RESULTS: During a median follow-up period of 38 (interquartile: 35-40) mo, 130 deaths occurred. The prediagnosis high MQI scores were associated with substantially improved survival among females with OC (HRtertile 3 vs. tertile 1 = 0.50, 95% CI: 0.33, 0.77). For sub-indices of the MQI, higher CQI (HR = 0.60, 95% CI: 0.36, 0.99), higher FQI (HR = 0.55, 95% CI: 0.34, 0.87), and higher PQI (HR = 0.58, 95% CI: 0.35, 0.94) scores were all associated with better survival. Notably, significant interactions were observed for the MQI score with total energy intake and energy balance as well as the quantity and quality of carbohydrates on survival. CONCLUSIONS: Intake of high-quality macronutrients before diagnosis was associated with improved survival among females with OC, especially for those with energy imbalance.

Strong Cumulative Evidence of Associations of 6 Single Nucleotide Polymorphisms with Ovarian Cancer Risk: An Umbrella Review.

Background: An increasing number of studies have reported associations between single nucleotide polymorphisms (SNPs) and ovarian cancer (OC) risk. However, some of the findings were inconsistent. The objective of this umbrella review was to evaluate the associations comprehensively and quantitatively. Methods: The protocol of this review was registered in PROSPERO (No. CRD42022332222). We searched the PubMed, Web of Science, and Embase databases to identify related systematic reviews and meta-analyses from inception to 15 October 2021. In addition to estimating the summary effect size by using fixed and random effects models and calculating the 95% prediction interval, we evaluated the cumulative evidence for associations with nominally statistical significance based on the Venice criteria and false positive report probability (FPRP). Results: Forty articles were included in this umbrella review, which referred to a total of 54 SNPs. The median number of original studies per meta-analysis was four, while the median number of total subjects was 3455. All included articles had greater than moderate methodological quality. A total of 18 SNPs were nominally statistically associated with OC risk; 6 SNPs (8 genetic models), 5 SNPs (7 genetic models), and 16 SNPs (25 genetic models) were identified as strong, moderate, and weak cumulative evidence, respectively. Conclusion: This umbrella review revealed associations between SNPs and OC risk and suggested strong cumulative evidence of associations of six SNPs (eight genetic models) with OC risk.

Nutrients-Rich Food Index Scores and the Overall Survival of Ovarian Cancer Patients: Results from the Ovarian Cancer Follow-Up Study, a Prospective Cohort Study.

Background: The nutrients-rich food (NRF) index provides a score of diet quality. Although high diet quality is associated with survival of ovarian cancer (OC), the associations between NRF index scores and OC survival remain unevaluated. Methods: The prospective cohort study enrolled 703 women with newly diagnosed epithelial OC to assess the correlations between NRF index scores and overall survival (OS) in OC patients. Dietary consumption was evaluated through a food frequency questionnaire and diet quality was calculated based on NRF index scores, including three limited nutrients and six (NRF6.3), nine (NRF9.3), or eleven (NRF11.3) benefit nutrients. All-cause deaths were ascertained through medical records combined with active follow-up. Immunohistochemistry (IHC) analyses were conducted to evaluate the expression of IHC indicators (including Estrogen Receptor, Progesterone Receptor, p53, Vimentin, and Wilms' tumor 1), which were identified by two independent pathologists. The Cox proportional hazards regression models were applied for estimating the hazard ratios (HRs) and 95% confidence intervals (CIs). Moreover, we performed the penalized cubic splines model to assess the curvilinear associations of NRF index scores with OC survival. Results: During the median follow-up of 37.17 (interquartile: 24.73-50.17) months, 130 deaths were documented. Compared to the lowest tertiles, the highest tertile of index scores [NRF9.3 (HR = 0.63, 95% CI = 0.41-0.95), NRF6.3 (HR = 0.59, 95% CI = 0.39-0.89), and NRF11.3 (HR = 0.57, 95% CI = 0.38-0.87)] were correlated to better OS, showing an obvious linear trend (all p trend < 0.05). Interestingly, the curvilinear association between the NRF6.3 index score and OC survival was also observed (p non-linear < 0.05). Subgroup analyses, stratified by clinical, demographic, and IHC features, showed similar risk associations as the unstratified results. Furthermore, there were significant multiplicative interactions between NRF index scores and Progestogen Receptors as well as Wilms' tumor 1 expressions (all p interaction < 0.05). Conclusions: Higher NRF index scores were associated with an improved OS in OC patients.

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.

BACKGROUND: Small intestinal vascular malformations (angiodysplasias) are common causes of small intestinal bleeding. While capsule endoscopy has become the primary diagnostic method for angiodysplasia, manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload, which affects the accuracy of diagnosis. AIM: To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time. METHODS: A convolutional neural network semantic segmentation model with a feature fusion method, which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour, thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions, was proposed. Resnet-50 was used as the skeleton network to design the fusion mechanism, fuse the shallow and depth features, and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia. The training set and test set were constructed and compared with PSPNet, Deeplab3+, and UperNet. RESULTS: The test set constructed in the study achieved satisfactory results, where pixel accuracy was 99%, mean intersection over union was 0.69, negative predictive value was 98.74%, and positive predictive value was 94.27%. The model parameter was 46.38 M, the float calculation was 467.2 G, and the time length to segment and recognize a picture was 0.6 s. CONCLUSION: Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.

Clinical Characteristics of Patients With Surgically Resected Pancreatic Cysts: A Retrospective Analysis of 136 Patients.

OBJECTIVES: To retrospectively analyze the characteristics of pancreatic cysts with respect to histopathological diagnosis and various diagnostic imaging tools. METHODS: The clinical features of 136 patients and characteristics of histopathologically diagnosed cysts were retrospectively assessed. The diagnostic accuracy of endoscopic ultrasound (EUS), computed tomography (CT), and magnetic resonance imaging (MRI) for pancreatic cysts was compared. Risk factors for high-grade dysplasia/invasive cancer in patients with intraductal papillary mucinous neoplasms (IPMNs) were also determined. RESULTS: The final analysis included 30 serous cystic neoplasms (SCNs) (21.6%), 13 mucinous cystic neoplasms (MCNs) (9.4%), 65 IPMNs (46.8%), and 13 solid pseudopapillary neoplasms (SPNs) (9.4%). The percentage of women with MCNs, SPNs, SCNs, and IPMNs was 100.0, 76.9, 73.3, and 47.7%, respectively (P < .001). The percentages of patients over 60 years of age with IPMNs, SCNs, MCNs, and SPNs were 73.9, 23.3, 0, and 0%, respectively (P < .001). The percentage of cysts located in the body and tail of the pancreas in MCNs, SCNs, SPNs, and IPMNs was 100, 70, 53.9, and 46.2%, respectively (P < .001). A unique honeycomb appearance was observed in 26.7% of SCNs. The overall diagnostic accuracy of EUS, CT, and MRI for pancreatic cysts was 82.6, 72.5, and 73.9%, respectively. Lesion size and presence of solid components were independent predictors of high-risk IPMNs. CONCLUSIONS: Patient characteristics and cyst features can help to differentiate pancreatic cyst types and identify high-risk IPMNs. The diagnostic accuracy of EUS for pancreatic cysts is superior to that of CT and MRI.