Multiple Follicular Hybrid Tumors Presenting in Association With Myotonic Dystrophy Type 1.

ABSTRACT: Follicular hybrid cysts are uncommon entities derived from 2 or more components of the folliculo-sebaceous-apocrine unit. The pathogenesis of follicular hybrid cysts is uncertain; however, they are proposed to derive from the multipotent nature of follicular stem cells. Myotonic dystrophy type 1 is an inherited muscular dystrophy caused by an unstable trinucleotide repeat expansion in the myotonic dystrophy protein kinase gene, notably associated with multiple pilomatricomas. We report a novel case of multiple follicular hybrid tumors presenting in association with myotonic dystrophy type 1. We suspect that multipotent follicular stem cells, under the influence of the hypermutability phenotype present in myotonic dystrophy type 1, contributed to the pathogenesis of multiple follicular hybrid tumors in our patient.

Mapping the Anatomy of the Human Lymphatic System.

BACKGROUND: While substantial anatomical study has been pursued throughout the human body, anatomical study of the human lymphatic system remains in its infancy. For microsurgeons specializing in lymphatic surgery, a better command of lymphatic anatomy is needed to further our ability to offer surgical interventions with precision. In an effort to facilitate the dissemination and advancement of human lymphatic anatomy knowledge, our teams worked together to create a map. The aim of this paper is to present our experience in mapping the anatomy of the human lymphatic system. METHODS: Three steps were followed to develop a modern map of the human lymphatic system: (1) identifying our source material, which was "Anatomy of the human lymphatic system," published by Rouvière and Tobias (1938), (2) choosing a modern platform, the Miro Mind Map software, to integrate the source material, and (3) transitioning our modern platform into The Human BioMolecular Atlas Program (HuBMAP). RESULTS: The map of lymphatic anatomy based on the Rouvière textbook contained over 900 data points. Specifically, the map contained 404 channels, pathways, or trunks and 309 lymph node groups. Additionally, lymphatic drainage from 165 distinct anatomical regions were identified and integrated into the map. The map is being integrated into HuBMAP by creating a standard data format called an Anatomical Structures, Cell Types, plus Biomarkers table for the lymphatic vasculature, which is currently in the process of construction. CONCLUSION: Through a collaborative effort, we have developed a unified and centralized source for lymphatic anatomy knowledge available to the entire scientific community. We believe this resource will ultimately advance our knowledge of human lymphatic anatomy while simultaneously highlighting gaps for future research. Advancements in lymphatic anatomy knowledge will be critical for lymphatic surgeons to further refine surgical indications and operative approaches.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Spillover Effects of Medicare's Voluntary Bundled Payments for Joint Replacement Surgery to Patients Insured by Commercial Health Plans.

BACKGROUND: Under the Bundled Payments for Care Improvement (BPCI) program, bundled paymtents for lower-extremity joint replacement (LEJR) are associated with 2% to 4% cost savings with stable quality among Medicare fee-for-service beneficiaries. However, BPCI may prompt practice changes that benefit all patients, not just fee-for-service beneficiaries. OBJECTIVE: To examine the association between hospital participation in BPCI and LEJR outcomes for patients with commercial insurance or Medicare Advantage (MA). DESIGN: Quasi-experimental study using Health Care Cost Institute claims from 2011 to 2016. SETTING: LEJR at 281 BPCI hospitals and 562 non-BPCI hospitals. PATIENTS: 184 922 patients with MA or commercial insurance. MEASUREMENTS: Differential changes in LEJR outcomes at BPCI hospitals versus at non-BPCI hospitals matched on propensity score were evaluated using a difference-in-differences (DID) method. Secondary analyses evaluated associations by patient MA status and hospital characteristics. Primary outcomes were changes in 90-day total spending on LEJR episodes and 90-day readmissions; secondary outcomes were postacute spending and discharge to postacute care providers. RESULTS: Average episode spending decreased more at BPCI versus non-BPCI hospitals (change, -2.2% [95% CI, -3.6% to -0.71%]; P = 0.004), but differences in changes in 90-day readmissions were not significant (adjusted DID, -0.47 percentage point [CI, -1.0 to 0.06 percentage point]; P = 0.084). Participation in BPCI was also associated with differences in decreases in postacute spending and discharge to institutional postacute care providers. Decreases in episode spending were larger for hospitals with high baseline spending but did not vary by MA status. LIMITATION: Nonrandomized studies are subject to residual confounding and selection. CONCLUSION: Participation in BPCI was associated with modest spillovers in episode savings. Bundled payments may prompt hospitals to implement broad care redesign that produces benefits regardless of insurance coverage. PRIMARY FUNDING SOURCE: Leonard Davis Institute of Health Economics at the University of Pennsylvania.

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. METHODS: Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. RESULTS: Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists. CONCLUSIONS: These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. KEY POINTS: • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.

Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making. PURPOSE: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma. STUDY TYPE: Retrospective. POPULATION: In all, 376 patients with 430 renal cell carcinoma lesions from 2008-2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set. FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T/T2 -weighted and T1 contrast-enhanced sequences. ASSESSMENT: The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model. STATISTICAL TESTS: Mann-Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity. RESULTS: The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73-0.96), sensitivity of 0.89 (95% CI: 0.74-0.96), and specificity of 0.88 (95% CI: 0.73-0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73-0.90), sensitivity of 0.92 (95% CI: 0.84-0.97), and specificity of 0.78 (95% CI: 0.68-0.86) in the WHO/ISUP test set. DATA CONCLUSION: Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization.

PURPOSE: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome. MATERIALS AND METHODS: Clinical data were collected on patients treated with uterine fibroid embolization at the Hospital of the University of Pennsylvania from 2007 to 2018. Fibroids for each patient were manually segmented by an abdominal radiologist on a T1-weighted contrast-enhanced (T1C) sequence and a T2-weighted sequence of MR imaging obtained before and after embolization. A residual convolutional neural network (ResNet) model to predict clinical outcome was trained using MR imaging obtained before the procedure. RESULTS: Inclusion criteria were met by 727 fibroids in 409 patients. At clinical follow-up, 85.6% (n = 350) of 409 patients (590 of 727 fibroids; 81.1%) experienced symptom resolution or improvement, and 14.4% (n = 59) of 409 patients (137 of 727 fibroids; 18.9%) had no improvement or worsening symptoms. The T1C trained model achieved a test accuracy of 0.847 (95% confidence interval [CI], 0.745-0.914), sensitivity of 0.932 (95% CI, 0.833-0.978), and specificity of 0.462 (95% CI, 0.232-0.709). In comparison, the average of 4 radiologists achieved a test accuracy of 0.722 (95% CI, 0.609-0.813), sensitivity of 0.852 (95% CI, 0.737-0.923), and specificity of 0.135 (95% CI, 0.021-0.415). CONCLUSIONS: This study demonstrates that deep learning based on a ResNet model achieves good accuracy in predicting outcome of uterine fibroid embolization. If further validated, the model may help clinicians better identify patients who can most benefit from this therapy and aid clinical decision making.

Clinical effectiveness and cost-effectiveness of teledermatology: Where are we now, and what are the barriers to adoption?

There has been rapid growth in teledermatology over the past decade, and teledermatology services are increasingly being used to support patient care across a variety of care settings. Teledermatology has the potential to increase access to high-quality dermatologic care while maintaining clinical efficacy and cost-effectiveness. Recent expansions in telemedicine reimbursement from the Centers for Medicare & Medicaid Services (CMS) ensure that teledermatology will play an increasingly prominent role in patient care. Therefore, it is important that dermatologists be well informed of both the promises of teledermatology and the potential practice challenges a continuously evolving mode of care delivery brings. In this article, we will review the evidence on the clinical and cost-effectiveness of teledermatology and we will discuss system-level and practice-level barriers to successful teledermatology implementation as well as potential implications for dermatologists.

Increased risk of vertebral fracture in patients with diabetes: a meta-analysis of cohort studies.

PURPOSE: The relationship between diabetes and risk of fracture has been reported differently in study design and risk estimates, and the relationship between diabetes and risk of vertebral fracture remained unclear. Therefore, we performed a meta-analysis of prospective or retrospective cohort studies to assess the potential relationship between diabetes and vertebral fracture. METHODS: We searched medical databases for prospective or retrospective cohort studies on the association between diabetes and vertebral fracture risk. Pooled relative risks (RR) and corresponding 95 % confidence intervals (95 % CI) were calculated with a random-effects model of meta-analysis. RESULTS: Meta-analysis of eight studies showed that the pooled RR of vertebral fracture for diabetic individuals was 2.03 (95 % CI 1.60-2.59; p < 0.0001). Subgroup analysis by gender showed that the corresponding RRs for male and female were 2.70 (95 % CI 1.34-5.43; p = 0.005) and 1.93 (95 % CI 1.18-3.13; p = 0.008), respectively. Subgroup analysis by study design showed that the corresponding RRs for prospective design and retrospective design were 1.81 (95  % CI 1.19-2.75; p = 0.006) and 2.23 (95  % CI 1.60-3.10; p < 0.0001), respectively. Subgroup analysis by time of follow-up showed that the RR of vertebral fracture for patients with >20 and <20 years of follow-up were 2.23 (95 % CI 1.98-3.62; p < 0.0001) and 1.67 (95  % CI 1.29-2.16; p < 0.0001), respectively. CONCLUSIONS: Diabetes is an independent risk factor for vertebral fracture, primarily being due to diabetic osteoporosis.

Association between height and thyroid cancer risk: a meta-analysis of prospective cohort studies.

While several epidemiological studies have investigated the relationship between height and risk for thyroid cancer, the results were inconsistent. In the present study, a systematic review and meta-analysis of prospective cohort studies was conducted to assess the impact of height on thyroid cancer risk. Online databases were searched up to December 30, 2014, for prospective cohort studies on the association between height and thyroid cancer risk. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated using a random-effects model of meta-analysis. In all, 11 articles were included in this meta-analysis, including 15 prospective cohort studies, containing 6,695,593 participants and 7,062 cases of thyroid cancer. By comparing the highest versus the lowest categories of height, we reported that risk of thyroid cancer was increased with height in both men (summary RR = 1.40, 95%CI 1.09-1.78, p = 0.008) and women (summary RR = 1.54, 95%CI 1.30-1.83, p < 0.001). The summary RR of thyroid cancer per 5-cm increase in height was 1.16 (95%CI 1.09-1.23, p < 0.001). The results were similar among men (per 5-cm increase RR = 1.13, 95%CI 1.03-1.23, p = 0.011) and women (per 5-cm increase RR = 1.18, 95%CI 1.10-1.27, p < 0.001). No obvious risk of publication bias was observed. Our meta-analysis provides strong evidence for a dose-response relationship between height and risk of thyroid cancer in both men and women.

Effect of sodium alginate addition to resveratrol on acute gouty arthritis.

OBJECTIVE: Resveratrol has been shown to exert anti-inflammatory and antioxidant effects, while sodium alginate is a common pharmaceutic adjuvant with antioxidative and immunomodulatory properties. We performed an animal study to investigate the effect of sodium alginate addition to resveratrol on acute gouty arthritis. METHODS: Twenty-four SPF Wistar mice were randomized to four groups receiving the combination of sodium alginate and resveratrol, resveratrol alone, colchicine, and placebo, respectively. Acute gouty arthritis was induced by injection of 0.05 ml monosodium urate (MSU) solution (25g/mL) into ankle joint cavity. IL-1ß, CCR5, and CXCL10 levels in both serum and synovial fluid were measured using ELISA. NLRP3 expression in the synovial tissues was measured using western plot. RESULTS: The combination of sodium alginate and resveratrol significantly reduced synovial levels of IL-1ß, CCR5, and CXCL10 when compared with colchicines, and all P values were less than 0.0001. The combination of sodium alginate and resveratrol was also superior to resveratrol in terms of both serum levels and synovial levels of IL-1ß, CCR5, and CXCL10. In addition, resveratrol, with or without sodium alginate, could reduce NLRP3 expression obviously in the synovial tissues. CONCLUSION: The combination of sodium alginate and resveratrol has better effect over colchicines in treating MSU-induced acute gouty arthritis.

CARD8 rs2043211 polymorphism is associated with gout in a Chinese male population.

BACKGROUND &AIM: Previous studies have suggested genetic factors are involved in the development of gout. We performed a case-control study to investigate the genetic association between CARD8 rs2043211 polymorphism and gout. METHODS: A total of 396 male patients with gout and 403 age- and sex- matched healthy controls were included in this study. Genotyping was performed using TaqMan SNP Genotyping Assays. An association analysis was carried out using the χ² test. The genotype-phenotype analysis was also conducted. RESULTS: The genotype distribution of CARD8 rs2043211 polymorphism confirmed to HWE in the controls (P = 0.27). There was an obvious difference in the genotype distribution of CARD8 rs2043211 polymorphism between cases and controls (P = 0.017). In addition, there was an obvious association between CARD8 rs2043211 polymorphism and gout under the recessive comparison model (AA vs. TT/TA: OR = 0.65, 95%CI 0.47-0.88, P = 0.006). Patients carrying genotype TT of CARD8 rs2043211 polymorphism had higher triglycerides levels compared to those carrying the AA genotype (2.77±2.08 mmol/L vs. 2.07±1.15 mmol/L, P = 0.01). Patients with the TT genotype also had significantly higher systolic blood pressure compared with those with the AA genotype (142.11±21.10 mmHg vs. 135.38±14.66 mmHg, P = 0.03). Patients carrying TT genotype also had an increased risk of renal calculus compared with those carrying the AA genotype. CONCLUSION: CARD8 rs2043211 polymorphism is significantly associated with susceptibility to gout in Chinese Han males.

Serum 25-hydroxyvitamin D levels and prognosis in hematological malignancies: a systematic review and meta-analysis.

BACKGROUND/AIMS: Serum 25-hydroxyvitamin D [25(OH)D] levels proved to be associated with prognosis of patients with colorectal cancer or breast cancer, but its prognostic role in hematological malignancies was still unclear. A systematic review and meta-analysis was performed to comprehensively evaluate the association between serum 25(OH)D levels and prognosis of patients with hematological malignancies. METHODS: We searched Pubmed, Embase, Web of Science, and Google Scholar for studies evaluating the association between serum 25(OH)D levels and prognosis of patients with hematological malignancies. The hazard ratios (HR) with 95% confidence intervals (95%CI) for overall survival (OS) or relapse-free survival (RFS) were pooled using meta-analysis. RESULTS: Seven studies with a total of 2,643 patients with hematological cancer were finally included into the meta-analysis. Overall, compared with normal serum 25(OH)D levels, low serum 25(OH)D levels were significantly associated with both poorer OS (HR = 1.85, 95% CI 1.54-2.23, P <0.001) and poorer RFS (HR = 1.45, 95% CI 1.25 to 1.70, P <0.001) in hematological malignancies. Subgroup analysis further showed that low serum 25(OH)D levels were significantly associated with poorer OS and RFS in both lymphoma and leukemia. CONCLUSION: Low serum 25(OH)D levels are significantly associated with poorer prognosis in patients with hematological malignancies including lymphoma and leukemia.

An open multicenter comparative randomized clinical study on chitosan.

Chitosan, a natural polysaccharide derivate from chitin, offers a promising alternative biomaterial for use in wound dressings. In this work, the safety and efficacy of a next-generation KA01 chitosan wound dressing in facilitating the healing of nonhealing chronic wounds was studied. This open multicenter comparative prospective randomized clinical study was conducted at three medical centers in China. A total of 90 patients (45 in test group and 45 in control group) with unhealed chronic wounds including pressure ulcers, vascular ulcers, diabetic foot ulcers, and wounds with minor infections, or at risk of infection, were treated with the next generation chitosan wound dressing as the test article or traditional vaseline gauze as a control. Baseline assessments were undertaken with the primary end point being wound area reduction. The secondary end points included pain reduction (using the NRS11 pain scale) at dressing change, wound exudate levels, wound depth and duration of the treatment. After 4 weeks treatment, the wound area reduction was significantly greater in the test group (65.97 ± 4.48%) than the control group (39.95 ± 4.48%). The average pain level in the test group was 1.12 ± 0.23 and 2.30 ± 0.23 in the control group. The wound depth was also lower in the test group 0.30 ± 0.48 cm than the control group 0.54 ± 0.86 cm. The level of exudate fell and the dressing could be removed integrally in both the test and control groups. The mean duration of the test group was 27.31 ± 5.37 days and control group 27.09 ± 6.44 days. No adverse events were reported in either group. In conclusion this open multicenter comparative prospective randomized clinical study has provided compelling evidence that the next generation chitosan wound dressing can enhance wound progression towards healing by facilitating wound reepithelialization and reducing the patients pain level. Furthermore the dressing was shown to be clinically safe and effective in the management of chronic wounds.

High leptin level is an independent risk factor of endometrial cancer: a meta-analysis.

BACKGROUND/AIMS: Previous studies suggested that high leptin level might increase risk of endometrial cancer, but available data were conflicting and whether high leptin level was an independent risk factor of endometrial cancer was still unclear. Therefore, a meta-analysis was performed to assess whether high leptin level was an independent risk factor of endometrial cancer. METHODS: PubMed, Web of Science, and Embase databases were searched for epidemiological studies published up to June 26, 2014. The pooled risk ratio (RR) with 95% confidence interval (95%CI) was used to assess the association between leptin level and risk of endometrial cancer. RESULTS: Six studies with a total of 3136 individuals were finally included into the meta-analysis. Meta-analysis of total 6 studies showed that high leptin level was associated with increased risk of endometrial cancer (RR = 2.55, 95%CI 1.91-3.41, P < 0.001). After adjusting for confounding factors, high leptin level was also associated with increased risk of endometrial cancer (RR =1.59, 95%CI 1.27-1.98, P < 0.001). Sensitivity analysis proved the stability of the pooled estimates. The RR of endometrial cancer was 1.10 (95%CI, 1.03-1.18, P = 0.005) per 5 ng/mL increment in leptin levels. There was no obvious risk of publication bias (P Egger = 0.54). CONCLUSION: Our findings suggest that high leptin level is an independent risk factor of endometrial cancer. More prospective studies are needed to further confirm the association in the future.