scholarly journals MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Tianping Wang ◽  
Haijie Wang ◽  
Yida Wang ◽  
Xuefen Liu ◽  
Lei Ling ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Predictive Performance ◽  
Net Benefit ◽  
Prognosis Prediction ◽  
Epithelial Ovarian Tumor ◽  
System A ◽  
Area Under Roc Curve ◽  
Prognosis Prediction Model

Abstract Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

scholarly journals MetS Risk Score: A Clear Scoring Model to Predict a 3-Year Risk for Metabolic Syndrome

2018 ◽  
Vol 50 (09) ◽  
pp. 683-689 ◽  
Author(s):  
Tian-Tian Zou ◽  
Yu-Jie Zhou ◽  
Xiao-Dong Zhou ◽  
Wen-Yue Liu ◽  
Sven Van Poucke ◽  
...  
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Risk Score ◽  
Validation Cohort ◽  
Medical Center ◽  
Characteristic Curve ◽  
Model Development ◽  
Predictive Performance ◽  
Risk Scores ◽  
Individual Risk

AbstractAlthough several risk factors for metabolic syndrome (MetS) have been reported, there are few clinical scores that predict its incidence. Therefore, we created and validated a risk score for prediction of 3-year risk for MetS. Three-year follow-up data of 4395 initially MetS-free subjects, enrolled for an annual physical examination from Wenzhou Medical Center were analyzed. Subjects at enrollment were randomly divided into the training and the validation cohort. Univariate and multivariate logistic regression models were employed for model development. The selected variables were assigned an integer or half-integer risk score proportional to the estimated coefficient from the logistic model. Risk scores were tested in a validation cohort. The predictive performance of the model was tested by computing the area under the receiver operating characteristic curve (AUROC). Four independent predictors were chosen to construct the MetS risk score, including BMI (HR=1.906, 95% CI: 1.040–1.155), FPG (HR=1.507, 95% CI: 1.305–1.741), DBP (HR=1.061, 95% CI: 1.002–1.031), HDL-C (HR=0.539, 95% CI: 0.303–0.959). The model was created as –1.5 to 4 points, which demonstrated a considerable discrimination both in the training cohort (AUROC=0.674) and validation cohort (AUROC=0.690). Comparison of the observed with the estimated incidence of MetS revealed satisfactory precision. We developed and validated the MetS risk score with 4 risk factors to predict 3-year risk of MetS, useful for assessing the individual risk for MetS in medical practice.

scholarly journals Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao-Ran Cheng ◽  
Gui-Qian Huang ◽  
Zi-Qian Wu ◽  
Yue-Min Wu ◽  
Gang-Qiang Lin ◽  
...  
Keyword(s):  
Deep Vein Thrombosis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Training Cohort ◽  
Vein Thrombosis ◽  
Distal Deep Vein Thrombosis ◽  
Deep Vein

Abstract Background Although isolated distal deep vein thrombosis (IDDVT) is a clinical complication for acute ischemic stroke (AIS) patients, very few clinicians value it and few methods can predict early IDDVT. This study aimed to establish and validate an individualized predictive nomogram for the risk of early IDDVT in AIS patients. Methods This study enrolled 647 consecutive AIS patients who were randomly divided into a training cohort (n = 431) and a validation cohort (n = 216). Based on logistic analyses in training cohort, a nomogram was constructed to predict early IDDVT. The nomogram was then validated using area under the receiver operating characteristic curve (AUROC) and calibration plots. Results The multivariate logistic regression analysis revealed that age, gender, lower limb paralysis, current pneumonia, atrial fibrillation and malignant tumor were independent risk factors of early IDDVT; these variables were integrated to construct the nomogram. Calibration plots revealed acceptable agreement between the predicted and actual IDDVT probabilities in both the training and validation cohorts. The nomogram had AUROC values of 0.767 (95% CI: 0.742–0.806) and 0.820 (95% CI: 0.762–0.869) in the training and validation cohorts, respectively. Additionally, in the validation cohort, the AUROC of the nomogram was higher than those of the other scores for predicting IDDVT. Conclusions The present nomogram provides clinicians with a novel and easy-to-use tool for the prediction of the individualized risk of IDDVT in the early stages of AIS, which would be helpful to initiate imaging examination and interventions timely.

scholarly journals A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Treatment Strategies ◽  
Predictive Performance ◽  
Maximum Dimension ◽  
Ki 67 ◽  
Training Cohort ◽  
Index Level

BackgroundThe Ki-67 index is an indicator of proliferation and aggressive behavior in pituitary adenomas (PAs). This study aims to develop and validate a predictive nomogram for forecasting Ki-67 index levels preoperatively in PAs.MethodsA total of 439 patients with PAs underwent PA resection at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020; they were enrolled in this retrospective study and were classified randomly into a training cohort (n = 300) and a validation cohort (n = 139). A range of clinical, radiological, and laboratory characteristics were collected. The Ki-67 index was classified into the low Ki-67 index (<3%) and the high Ki-67 index (≥3%). Least absolute shrinkage and selection operator algorithm and uni- and multivariate logistic regression analyses were applied to identify independent risk factors associated with Ki-67. A nomogram was constructed to visualize these risk factors. The receiver operation characteristic curve and calibration curve were computed to evaluate the predictive performance of the nomogram model.ResultsAge, primary-recurrence subtype, maximum dimension, and prolactin were included in the nomogram model. The areas under the curve (AUCs) of the nomogram model were 0.694 in the training cohort and 0.658 in the validation cohort. A well-fitted calibration curve was also generated for the nomogram model. A subgroup analysis revealed stable predictive performance for the nomogram model. A correlation analysis revealed that age (R = −0.23; p < 0.01), maximum dimension (R = 0.17; p < 0.01), and prolactin (R = 0.16; p < 0.01) were all significantly correlated with the Ki-67 index level.ConclusionsAge, primary-recurrence subtype, maximum dimension, and prolactin are independent predictors for the Ki-67 index level. The current study provides a novel and feasible nomogram, which can further assist neurosurgeons to develop better, more individualized treatment strategies for patients with PAs by predicting the Ki-67 index level preoperatively.

scholarly journals Multimodal Monitoring in Large Hemispheric Infarction: Quantitative Electroencephalography Combined With Transcranial Doppler for Prognosis Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Yajie Qi ◽  
Yingqi Xing ◽  
Lijuan Wang ◽  
Jie Zhang ◽  
Yanting Cao ◽  
...  
Keyword(s):  
Transcranial Doppler ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Survival Duration ◽  
Quantitative Electroencephalography ◽  
Prognosis Prediction ◽  
Prognosis Prediction Model ◽  
Gcs Score ◽  
Hemispheric Infarction

Background: We aimed to explore whether transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) can improve prognosis evaluation in patients with a large hemispheric infarction (LHI) and to establish an accurate prognosis prediction model.Methods: We prospectively assessed 90-day mortality in patients with LHI. Brain function was monitored using TCD-QEEG at the bedside of the patient.Results: Of the 59 (55.3 ± 10.6 years; 17 men) enrolled patients, 37 (67.3%) patients died within 90 days. The Cox regression analyses revealed that the Glasgow Coma Scale (GCS) score ≤ 8 [hazard ratio (HR), 3.228; 95% CI, 1.335–7.801; p = 0.009], TCD-terminal internal carotid artery as the offending vessel (HR, 3.830; 95% CI, 1.301–11.271; p = 0.015), and QEEG-a (delta + theta)/(alpha + beta) ratio ≥ 3 (HR, 3.647; 95% CI, 1.170–11.373; p = 0.026) independently predicted survival duration. Combining these three factors yielded an area under the receiver operating characteristic curve of 0.905 and had better predictive accuracy than those of individual variables (p < 0.05).Conclusion: TCD and QEEG complement the GCS score to create a reliable multimodal method for monitoring prognosis in patients with LHI.

scholarly journals Radiomic Analysis of Gd-EOB-DTPA-enhanced MRI Predicts Ki-67 Expression in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Yanfen Fan ◽  
Yixing Yu ◽  
Ximing Wang ◽  
Mengjie Hu ◽  
Chunhong Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Hepatobiliary Phase ◽  
Net Benefit ◽  
Combined Model ◽  
Ki 67 ◽  
Portal Venous Phase ◽  
Enhanced Mri

Abstract Background: Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods: This retrospective study included 151 (training cohort: n=103; validation cohort: n=48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results: The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC=0.873 vs 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30-60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions: A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

scholarly journals Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yanfen Fan ◽  
Yixing Yu ◽  
Ximing Wang ◽  
Mengjie Hu ◽  
Chunhong Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Hepatobiliary Phase ◽  
Net Benefit ◽  
Combined Model ◽  
Ki 67 ◽  
Portal Venous Phase ◽  
Enhanced Mri

Abstract Background Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30–60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

scholarly journals Comparison among different precursor prostatespecific antigen isoform derivatives on prostate cancer prediction in patients with serum prostatespecific antigen bellow 10 ng/ml

2020 ◽  
Vol 148 (3-4) ◽  
pp. 160-166
Author(s):  
Milorad Stojadinovic ◽  
Damnjan Pantic ◽  
Marija Andjelkovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Prostate Cancer ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Predictive Performance ◽  
Clinicopathological Characteristics ◽  
Net Benefit ◽  
Free Psa ◽  
Prostate Biopsies ◽  
Prostate Health

Introduction/Objective. The precursor prostate-specific antigen (proPSA) especially its isoform p2PSA is useful in the detection of prostate cancer (PCa). However, the prediction value of different p2PSA derivatives remains unclear. The aim of the study was to compare the performance of the p2PSA, percentage of p2PSA to free PSA (%p2PSA), prostate health index (Phi), and one prostate dimension-adjusted index, p2PSA density (p2PSAD), with each other for PCa prediction in patients with serum PSA 10 ng/ml or less. Methods. This prospective study included patients who had undergone ultrasound-guided prostate biopsies and p2PSA testing. The data about patients? clinicopathological characteristics were collected and %p2PSA, p2PSAD and Phi were calculated. Different aspect of predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), the specificities at set sensitivities, and clinical utility using decision curve analyses (DCA). Results. PCa was diagnosed in 23 (32.4%) out of 71 patients. Results of multivariate analysis showed that only the Phi and digital rectal examination were independent predictors of PCa. The AUC of p2PSA, %p2PSA, p2PSAD and Phi were 76.2%, 81.5%, 88.7%, 89.6%, respectively. At pre-specified sensitivity of 90% and 95%, Phi demonstrated a greater specificity than the other p2PSA derivatives. Phi and p2PSAD lead to the higher net benefit in DCA. Conclusion. Compared with other p2PSA derivatives Phi is the most useful parameter for selection of the patients that do not need to be undergone to biopsy and thereby avoiding unnecessary procedures.

scholarly journals Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Yan ◽  
Zi-Xin Guo ◽  
Dong-Hu Yu ◽  
Chen Chen ◽  
Xiao-Ping Liu ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Copy Number Variations ◽  
Gene Model ◽  
Cox Regression Analysis ◽  
Prognosis Prediction ◽  
Model Training ◽  
Rare Malignancy ◽  
Prognosis Prediction Model

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

Sign in / Sign up

Export Citation Format

Share Document