scholarly journals Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yan Zhang ◽  
Cuilan Gong ◽  
Ling Zheng ◽  
Xiaoyan Li ◽  
Xiaomei Yang
Keyword(s):  
Endometrial Cancer ◽  
Prediction Model ◽  
Characteristic Curve ◽  
Test Group ◽  
Clinical Information ◽  
Training Group ◽  
Imaging Parameters ◽  
Imaging Model ◽  
Noninvasive Markers ◽  
Intelligent Recognition

The aim of the study was to investigate the intelligent recognition of radiomics based on the convolutional neural network (CNN) in predicting endometrial cancer (EC). In this study, 158 patients with EC in hospital were selected as the research objects and divided into a training group and a test group. All the patients underwent magnetic resonance imaging (MRI) before surgery. Based on the CNN, the imaging model of EC prediction was constructed according to the characteristics. Besides, the comprehensive prediction model was established through the clinical information and imaging parameters. The results showed that the area under the working characteristic curve (AUC) of the radiomics model and comprehensive prediction model was 0.897 and 0.913 in the training group, respectively. In addition, the AUC of the radiomics model was 0.889 in the test group and that of the comprehensive prediction model was 0.897. The comprehensive prediction model was established through specific imaging parameters and clinical pathological information, and its prediction performance was good, indicating that radiomics parameters could be applied as noninvasive markers to predict EC.

scholarly journals Additional Value of PET/CT-Based Radiomics to Metabolic Parameters in Diagnosing Lynch Syndrome and Predicting PD1 Expression in Endometrial Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xinghao Wang ◽  
Ke Wu ◽  
Xiaoran Li ◽  
Junjie Jin ◽  
Yang Yu ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lynch Syndrome ◽  
Fdg Pet ◽  
Test Group ◽  
Training Group ◽  
Total Lesion Glycolysis ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct ◽  
Independent Cohort

PurposeWe aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features.Materials and MethodsWe conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data.ResultsThere was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (rs =0.577; p < 0.001).ConclusionsIn patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.

scholarly journals A novel model to predict mental distress among medical graduate students in China.

2021 ◽  
Author(s):  
Fei Guo ◽  
Min Yi ◽  
Li Sun ◽  
Ting Luo ◽  
Ruili Han ◽  
...  
Keyword(s):  
Graduate Students ◽  
Prediction Model ◽  
Learning Style ◽  
Psychological Health ◽  
Mental Distress ◽  
Negative Impact ◽  
Characteristic Curve ◽  
Training Group ◽  
Medical Graduate ◽  
Validation Group

Abstract Background: Several studies have reported serious mental status among medical graduate students, which triggered a negative impact on their physical and psychological health. This study aimed to develop a novel prediction model to calculate the risk of mental distress among medical graduate students. Methods: This study analyzed 1079 graduate students via an online questionnaire. Included subjects were randomly divided into the training group and validation group. In the training group, a formula was developed, and validation of the formula was performed in the validation group. The discrimination and calibration ability were assessed for the predictive performance of the formula. Results: One thousand and fifteen subjects were enrolled and randomly divided into the training group (n=508) and the validation group (n=507). The prevalence of severe mental distress was 14.96% in the training group, and 16.77% in the validation group. The formula included six variables, including year of study, type of student, daily research time, monthly income, scientific learning style, and feeling of time stress. The area under the receiver operating characteristic curve (AUROC) and calibration slope for the formula were 0.70 and 0.90 (95% CI: 0.65~1.15) in the training group, respectively; and 0.66 and 0.80 (95% CI: 0.51~1.09) in the validation group, respectively. Conclusion: Six risk factors for anxiety and depression were identified and a prediction model was created. The formula may be a useful model that can identify a high risk of mental distress among medical students.

scholarly journals A novel model to predict mental distress among medical graduate students in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Guo ◽  
Min Yi ◽  
Li Sun ◽  
Ting Luo ◽  
Ruili Han ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Graduate Students ◽  
Prediction Model ◽  
Learning Style ◽  
Mental Distress ◽  
Characteristic Curve ◽  
Training Group ◽  
Medical Graduate ◽  
Validation Group

Abstract Background Poor mental health was reported among medical graduate students in some studies. Identification of risk factors for predicting the mental health is capable of reducing psychological distress among medical graduate students. Therefore, the aim of the study was to identify potential risk factors relating to mental health and further create a novel prediction model to calculate the risk of mental distress among medical graduate students. Methods This study collected and analyzed 1079 medical graduate students via an online questionnaire. Included participants were randomly classified into a training group and a validation group. A model was developed in the training group and validation of the model was performed in the validation group. The predictive performance of the model was assessed using the discrimination and calibration. Results One thousand and fifteen participants were enrolled and then randomly divided into the training group (n = 508) and the validation group (n = 507). The prevalence of severe mental distress was 14.96% in the training group, and 16.77% in the validation group. The model was developed using the six variables, including the year of study, type of student, daily research time, monthly income, scientific learning style, and feeling of time stress. The area under the receiver operating characteristic curve (AUROC) and calibration slope for the model were 0.70 and 0.90 (95% CI: 0.65 ~ 1.15) in the training group, respectively, and 0.66 and 0.80 (95% CI, 0.51 ~ 1.09) in the validation group, respectively. Conclusions The study identified six risk factors for predicting anxiety and depression and successfully created a prediction model. The model may be a useful tool that can identify the mental status among medical graduate students. Trial registration No.ChiCTR2000039574, prospectively registered on 1 November 2020.

Three plasma-based microRNAs as potent diagnostic biomarkers for endometrial cancer

2021 ◽  
pp. 1-12
Author(s):  
Xingchen Fan ◽  
Minmin Cao ◽  
Cheng Liu ◽  
Cheng Zhang ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Diagnostic Value ◽  
Diagnostic Biomarkers ◽  
Non Invasive ◽  
Polymerase Chain ◽  
Public Datasets ◽  
Plasma Mirnas

BACKGROUND: MicroRNAs (miRNAs), with noticeable stability and unique expression pattern in plasma of patients with various diseases, are powerful non-invasive biomarkers for cancer detection including endometrial cancer (EC). OBJECTIVE: The objective of this study was to identify promising miRNA biomarkers in plasma to assist the clinical screening of EC. METHODS: A total of 93 EC and 79 normal control (NC) plasma samples were analyzed using Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) in this four-stage experiment. The receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic value. Additionally, the expression features of the identified miRNAs were further explored in tissues and plasma exosomes samples. RESULTS: The expression of miR-142-3p, miR-146a-5p, and miR-151a-5p was significantly overexpressed in the plasma of EC patients compared with NCs. Areas under the ROC curve of the 3-miRNA signature were 0.729, 0.751, and 0.789 for the training, testing, and external validation phases, respectively. The diagnostic performance of the identified signature proved to be stable in the three public datasets and superior to the other miRNA biomarkers in EC diagnosis. Moreover, the expression of miR-151a-5p was significantly elevated in EC plasma exosomes. CONCLUSIONS: A signature consisting of 3 plasma miRNAs was identified and showed potential for the non-invasive diagnosis of EC.

scholarly journals Serum MicroRNA Signature as a Diagnostic and Therapeutic Marker in Patients with Psoriatic Arthritis

2020 ◽  
Vol 47 (12) ◽  
pp. 1760-1767
Author(s):  
Sarah M. Wade ◽  
Trudy McGarry ◽  
Siobhan C. Wade ◽  
Ursula Fearon ◽  
Douglas J. Veale
Keyword(s):  
Psoriatic Arthritis ◽  
Characteristic Curve ◽  
High Specificity ◽  
Serum Samples ◽  
Rna Molecules ◽  
Serum Mirna ◽  
Serum Microrna ◽  
Specificity And Sensitivity ◽  
European League Against Rheumatism ◽  
Noninvasive Markers

ObjectiveMicroRNA (miRNA) are small endogenous regulatory RNA molecules that have emerged as potential therapeutic targets and biomarkers in autoimmunity. Here, we investigated serum miRNA levels in patients with psoriatic arthritis (PsA) and further assessed a serum miRNA signature in therapeutic responder versus nonresponder PsA patients.MethodsSerum samples were collected from healthy controls (HC; n = 20) and PsA patients (n = 31), and clinical demographics were obtained. To examine circulatory miRNA in serum from HC and PsA patients, a focused immunology miRNA panel was analyzed utilizing a miRNA Fireplex assay (FirePlex Bioworks Inc.). MiRNA expression was further assessed in responders versus nonresponders according to the European League Against Rheumatism response criteria.ResultsSix miRNA (miR-221-3p, miR-130a-3p, miR-146a-5p, miR-151-5p, miR-26a-5p, and miR-21-5p) were significantly higher in PsA compared to HC (all P < 0.05), with high specificity and sensitivity determined by receiver-operating characteristic curve analysis. Analysis of responder versus nonresponders demonstrated higher baseline levels of miR-221-3p, miR-130a-3p, miR-146a-5p, miR-151-5p, and miR-26a-5p were associated with therapeutic response.ConclusionThis study identified a 6-serum microRNA signature that could be attractive candidates as noninvasive markers for PsA and may help to elucidate the disease pathogenesis.

P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii28-ii28
Author(s):  
X Xue ◽  
Q Gao
Keyword(s):  
Prediction Model ◽  
Training Group ◽  
Clinical Prediction ◽  
Validation Group ◽  
Who Grade ◽  
Clinical Prediction Model ◽  
Cox Regression Analysis ◽  
Who Grade Ii Glioma ◽  
Grade Ii ◽  
Grade Ii Glioma

Abstract OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

scholarly journals Development and Validation of a Nine-Redox-Related Long Noncoding RNA Signature in Renal Clear Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-30
Author(s):  
Xia Qi-Dong ◽  
Xun Yang ◽  
Jun-Lin Lu ◽  
Chen-Qian Liu ◽  
Jian-Xuan Sun ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Clear Cell ◽  
Test Group ◽  
Training Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature

Background. Redox plays an essential role in the pathogeneses and progression of tumors, which could be regulated by long noncoding RNA (lncRNA). We aimed to develop and verify a novel redox-related lncRNA-based prognostic signature for clear cell renal cell carcinoma (ccRCC). Materials and Methods. A total of 530 ccRCC patients from The Cancer Genome Atlas (TCGA) were included in this study. All the samples were randomly split into training and test group at a 1 : 1 ratio. Then, we screened differentially expressed redox-related lncRNAs and constructed a novel prognostic signature from the training group using the least absolute shrinkage and selection operation (LASSO) and COX regression. Next, to verify the accuracy of the signature, we conducted risk and survival analysis, as well as the construction of ROC curve, nomogram, and calibration curves in the training group, test group, and all samples. Finally, the redox gene-redox-related lncRNA interaction network was constructed, and gene set enrichment analysis (GSEA) was performed to investigate the status of redox-related functions between high/low-risk groups. Results. A nine-redox-related lncRNA signature consisted of AC025580.3, COLCA1, AC027601.2, DLEU2, AC004918.3, AP006621.2, AL031670.1, SPINT1-AS1, and LAMA5-AS1 was significantly associated with overall survival in ccRCC patients. The signature proved efficient, and thus, a nomogram was successfully assembled. In addition, the GSEA results demonstrated that two major redox-related functions were enhanced in the high-risk group ccRCC patients. Conclusions. Our findings robustly demonstrate that the nine-redox-related lncRNA signature could serve as an efficient prognostic indicator for ccRCC.

scholarly journals Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

2021 ◽  
Vol 11 ◽  
Author(s):  
Jin-feng Pan ◽  
Rui Su ◽  
Jian-zhou Cao ◽  
Zhen-ya Zhao ◽  
Da-wei Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Elevated Serum ◽  
Training Group ◽  
Validation Group ◽  
Clinical Indicators

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

scholarly journals MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kaiyue Zhang ◽  
Yu Zhang ◽  
Xin Fang ◽  
Jiangning Dong ◽  
Liting Qian
Keyword(s):  
Endometrial Carcinoma ◽  
Cox Regression ◽  
Myometrial Invasion ◽  
Test Group ◽  
Inverse Correlation ◽  
Training Group ◽  
Figo Stage ◽  
Clinicopathological Features ◽  
Prognostic Information ◽  
Adc Values

Abstract Background To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). Methods One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group. Results ADCmean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADCmean, ADCmin and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADCmean and radscore were independent risk factors for recurrence, which were verified in the test group. Conclusions ADCmean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.

Diagnostic Value of MicroRNA 21 in Endometrial Cancer and Benign Lesions and its Differential Expression in Relation to Clinicopathological Parameters

MicroRNA ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Amal Bouziyane ◽  
Maryame Lamsisi ◽  
Hicham Benaguida ◽  
Mustapha Benhessou ◽  
Mohamed El Kerroumi ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Characteristic Curve ◽  
Myometrial Invasion ◽  
Figo Stage ◽  
Diagnostic Value ◽  
Clinicopathological Parameters ◽  
Benign Lesions ◽  
Tumor Tissues ◽  
Potential Biomarker ◽  
Noninvasive Samples

Background: Endometrial cancer is one of the most common malignancies among women worldwide. Although this cancer is often diagnosed at early stages, the need for biomarkers of diagnosis remains a necessity to overcome conventional invasive procedures of diagnosis. Objective: In our study, we aim to investigate the diagnostic value of microRNA-21 in endometrial cancer and its relation to clinicopathological features. Methods: We used RT-qPCR to measure the expression of microRNA-21 in 71 tumor tissues, 53 adjacent tissues, and 54 benign lesions. Results: Our results show that microRNA-21 is a potential biomarker for endometrial cancer with an area under the receiver operating characteristic curve of 0.925 (95% CI = 0.863 - 0.964, P<0.0001). The sensitivity was 84.51% (95% CI = 74.0 - 92.0) and specificity was 86.79% (95% CI = 74.7 - 94.5). For discrimination between benign lesions and controls the AUC was 0,881 with a sensitivity of 100% (95% CI = 93.4 - 100.0) and specificity of 66.04 % (95% CI = 51.7 - 78.5), and for discriminating benign lesions from tumors the AUC was 0,750 with a sensitivity of 54.93% (95% CI = 42.7 - 66.8) and specificity of 90.74% (95% CI = 79.7 - 96.9). We also found that tumors with elevated microRNA-21 expression are of advanced FIGO stage, high histological grades, and have cervical invasion, myometrial invasion and distant metastasis. Conclusion: Our findings support the important role of miR-21 as a biomarker for the diagnosis of endometrial cancer. Further studies on minimally invasive/noninvasive samples such as serum, blood, and urine are necessary to provide a better alternative to current diagnosis methods.

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