scholarly journals Nomogram Analysis and Internal Validation to Predict the Risk of Cystobiliary Communication in Patients Undergoing Hydatid Liver Cyst Surgery

2020 ◽  
Vol 44 (11) ◽  
pp. 3884-3892 ◽  
Author(s):  
Zhan Wang ◽  
Jin Xu ◽  
MingQuan Pang ◽  
Bin Guo ◽  
XiaoLei Xu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Roc Curve ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Liver Cyst ◽  
Curve Analysis ◽  
Imaging Features ◽  
Cyst Size ◽  
Biliary Leakage ◽  
Decision Curve Analysis

Abstract Purpose Biliary leakage caused by cystobiliary communication (CBC) is a common clinical concern. This study sought to identify predictors of CBC in hepatic cystic echinococcosis (HCE) patients undergoing hydatid liver cyst surgery and establish nomograms to predict CBC. Methods A predictive model was established in a training cohort of 310 HCE patients diagnosed between January 2013 and May 2017. Upon revision of the records of clinical parameters and imaging features of these patients, the lasso regression model was used to optimize feature selection for the CBC risk model. Combined with feature selection, a CBC nomogram was developed with multivariable logistic regression. C-index and calibration plots were used to analyze and evaluate the discrimination and calibration. The net benefit and predictive accuracy of the nomogram were performed via decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. An independent validation cohort of 132 patients recruited from June 2017 to May 2019 was used to evaluate the practicability of the nomogram. Results Predictors contained four features, namely alkaline phosphatase (ALP), glutamyl transpeptidase (GGT), cyst size and cyst location. The C-index of the nomogram is 0.791 (95% CI, 0.736–0.845), while the C-index verified by bootstrap is 0.746, indicating high prediction accuracy. The area under the curve (AUC) of the CBC in training was 0.766. ROC curve analysis demonstrated high sensitivity and specificity. Decision curve analysis confirmed the CBC nomogram was clinically useful when the intervention was determined at the non-adherence possibility threshold of 8%. Conclusion The nomogram developed using the ALP, GGT, cyst size and cyst location could be used to facilitate the CBC risk prediction in HCE patients.

scholarly journals Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao Yu Yu ◽  
Jialiang Ren ◽  
Yushan Jia ◽  
Hui Wu ◽  
Guangming Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ovarian Cancer ◽  
Peritoneal Carcinomatosis ◽  
Calibration Curve ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Imaging Features ◽  
Decision Curve Analysis ◽  
Dce Mri

ObjectivesTo evaluate the predictive value of radiomics features based on multiparameter magnetic resonance imaging (MP-MRI) for peritoneal carcinomatosis (PC) in patients with ovarian cancer (OC).MethodsA total of 86 patients with epithelial OC were included in this retrospective study. All patients underwent FS-T2WI, DWI, and DCE-MRI scans, followed by total hysterectomy plus omentectomy. Quantitative imaging features were extracted from preoperative FS-T2WI, DWI, and DCE-MRI images, and feature screening was performed using a minimum redundancy maximum correlation (mRMR) and least absolute shrinkage selection operator (LASSO) methods. Four radiomics models were constructed based on three MRI sequences. Then, combined with radiomics characteristics and clinicopathological risk factors, a multi-factor Logistic regression method was used to construct a radiomics nomogram, and the performance of the radiomics nomogram was evaluated by receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis.ResultsThe radiomics model from the MP-MRI combined sequence showed a higher area under the curve (AUC) than the model from FS-T2WI, DWI, and DCE-MRI alone (0.846 vs. 0.762, 0.830, 0.807, respectively). The radiomics nomogram (AUC=0.902) constructed by combining radiomics characteristics and clinicopathological risk factors showed a better diagnostic effect than the clinical model (AUC=0.858) and the radiomics model (AUC=0.846). The decision curve analysis shows that the radiomics nomogram has good clinical application value, and the calibration curve also proves that it has good stability.ConclusionRadiomics nomogram based on MP-MRI combined sequence showed good predictive accuracy for PC in patients with OC. This tool can be used to identify peritoneal carcinomatosis in OC patients before surgery.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

scholarly journals Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruohui Mo ◽  
Rong Shi ◽  
Yuhong Hu ◽  
Fan Hu
Keyword(s):  
Diabetic Retinopathy ◽  
Regression Analysis ◽  
Roc Curve ◽  
External Validation ◽  
Curve Analysis ◽  
Postprandial Blood Glucose ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Decision Curve Analysis ◽  
Course Of Disease

Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

scholarly journals Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Bin Zhang ◽  
Qin Liu ◽  
Xiao Zhang ◽  
Shuyi Liu ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Clinical Utility ◽  
Predictive Accuracy ◽  
Fasting Blood Glucose ◽  
External Validation ◽  
Predictive Performance ◽  
Curve Analysis ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

P0560POST-DISCHARGE FOLLOW UP AFTER AKI. AN EXTERNAL VALIDATION AND DECISION CURVE ANALYSIS OF TWO RISK MODELS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Simon Sawhney ◽  
Zhi Tan ◽  
Corri Black ◽  
Brenda Hemmelgarn ◽  
Angharad Marks ◽  
...  
Keyword(s):  
Risk Model ◽  
Discharge Planning ◽  
External Validation ◽  
Curve Analysis ◽  
Net Benefit ◽  
Post Discharge ◽  
Decision Curve Analysis ◽  
One Year ◽  
Unplanned Admissions

Abstract Background and Aims There is limited evidence to inform which people should receive follow up after AKI and for what reasons. Here we report the external validation (geographical and temporal) and potential clinical utility of two complementary models for predicting different post-discharge outcomes after AKI. We used decision curve analysis, a technique that enables visualisation of the trade-off (net benefit) between identifying true positives and avoiding false positives across a range of potential risk thresholds for a risk model. Based on decision curve analysis we compared model guided approaches to follow up after AKI with alternative strategies of standardised follow up – e.g. follow up of all people with AKI, severe AKI, or a discharge eGFR&lt;30. Method The Alberta AKI risk model predicts the risk of stage G4 CKD at one year after AKI among those with a baseline GFR&gt;=45 and at least 90 days survival (2004-2014, n=9973). A trial is now underway using this tool at a 10% threshold to identify high risk people who may benefit from specialist nephrology follow up. The Aberdeen AKI risk model provides complementary predictions of early mortality or unplanned readmissions within 90 days of discharge (2003, n=16453), aimed at supporting non-specialists in discharge planning, with a threshold of 20-40% considered clinically appropriate in the study. For the Alberta model we externally validated using Grampian residents with hospital AKI in 2011-2013 (n=9382). For the Aberdeen model we externally validated using all people admitted to hospital in Grampian in 2012 (n=26575). Analysis code was shared between the sites to maximise reproducibility. Results Both models discriminated well in the external validation cohorts (AUC 0.855 for CKD G4, and AUC 0.774 for death and readmissions model), but as both models overpredicted risks, recalibration was performed. For both models, decision curve analysis showed that prioritisation of patients based on the presence or severity of AKI would be inferior to a model guided approach. For predicting CKD G4 progression at one year, a strategy guided by discharge eGFR&lt;30 was similar to a model guided approach at the prespecified 10% threshold (figure 1). In contrast for early unplanned admissions and mortality, model guided approaches were superior at the prespecified 20-40% threshold (figure 2). Conclusion In conclusion, prioritising AKI follow up is complex and standardised recommendations for all people may be an inefficient and inadequate way of guiding clinical follow-up. Guidelines for AKI follow up should consider suggesting an individualised approach both with respect to purpose and prioritisation.

scholarly journals MRI-Based Radiomics Nomogram To Predict Intraoperative Hemorrhage of Placenta Previa

2021 ◽  
Author(s):  
Ye Song ◽  
Liping Zhu ◽  
Dali Chen ◽  
Yongmei Li ◽  
Qi Xi ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Placenta Previa ◽  
Preoperative Evaluation ◽  
Curve Analysis ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Preoperative Prediction ◽  
Mri Features ◽  
Wide Range ◽  
Validation Set

Abstract Background: Placenta previa is associated with higher percentage of intraoperative and postpartum hemorrhage, increased obstetric hysterectomy, significant maternal morbidity and mortality. We aimed to develop and validate a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for placenta previa, which might contribute to adequate assessment and preoperative preparation for the obstetricians.Methods: Between May 2015 and December 2019, a total of 125 placenta previa pregnant women were divided into a training set (n = 80) and a validation set (n = 45). Radiomics features were extracted from MRI images of each patient. A MRI-based model comprising seven features was built for the classification of patients into IPH and non-IPH groups in a training set and validation set. Multivariate nomograms based on logistic regression analyses were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. Results: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus and placental signals in the cervix were signifcantly independent predictors for IPH (all p < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. The AUC was 0.918 ( 95% CI, 0.857-0.979 ) in the training set and 0.866( 95% CI, 0.748-0.985 ) in the validation set by the combination of four MRI features.Conclusions: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for placenta previa. Our study enables obstetricians to perform adequate preoperative evaluation to minimize blood loss and reduce the rate of caesarean hysterectomy.

Contribution of C-reactive protein to predicting overall survival in patients with advanced urothelial carcinoma: Development of a nomogram and evaluation by decision curve analysis.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 280-280
Author(s):  
Junichiro Ishioka ◽  
Kazutaka Saito ◽  
Mizuaki Sakura ◽  
Minato Yokoyama ◽  
Yoh Matsuoka ◽  
...  
Keyword(s):  
Overall Survival ◽  
Urothelial Carcinoma ◽  
Predictive Accuracy ◽  
Curve Analysis ◽  
Negative Consequences ◽  
C Reactive Protein ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Reactive Protein ◽  
Advanced Urothelial Carcinoma

280 Background: To accurately estimate the individual survival of patients with advanced urothelial carcinoma (UC), the application of prediction models such as nomograms has been warranted in clinical use. We therefore constructed a nomogram which included C-reactive protein (CRP) as a novel biomarker in order to increase its predictive accuracy. Furthermore, the clinical usefulness of this nomogram was evaluated by decision curve analysis which incorporated the negative consequences of each decision to generate a net benefit (Vickers et al, BMC Med Inform Decis Mak, 2008). Methods: A total of 232 consecutive patients with locally advanced or metastatic urothelial carcinoma (UC) were treated at our institute. Among them, 9 patients with missing data were excluded. The current study cohort was comprised of the remaining 223 patients. A nomogram predicting 6- and 12-month survival probability was developed based on the results of the final multivariate analytic model. To evaluate the efficacy of this nomogram, a quantified concordance-index (c-index) was computed and a decision curve analysis was performed. Results: Overall, 184 patients died of the primary disease and the remaining 39 were censored. The median follow-up period and length of overall survival were 5 and 6 months, respectively. The 6- and 12-month survival rates were 48% and 30% respectively. A nomogram was developed which included the parameters of age, PS, visceral metastasis, hemoglobin and CRP. The c-index of this prediction model was 0.79 compared with 0.75 for that of a model without CRP. The decision curve analysis revealed that a novel nomogram which incorporated CRP had a superior net benefit to that without CRP for most of the examined threshold probabilities. Conclusions: Incorporation of CRP increased the predictive accuracy of a prognostic nomogram for advanced UC. In clinical practice, this nomogram would contribute to the decision making process in the treatment of patients suffering from this form of carcinoma.

scholarly journals Modified N Stage of Esophageal Cancer Based on the Evaluation of the Hezode Rate of the Negative and Positive Lymph Node

2020 ◽  
Author(s):  
Jinling Zhang ◽  
Hongyan Li ◽  
Liangjian Zhou ◽  
lianling Yu ◽  
Fengyuan Che ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Roc Curve ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Positive Lymph Node ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Validation Method ◽  
N Stage

Abstract Objective:The study aimed to propose a modified N stage of esophageal cancer (EC) on basis of based on the number of positive lymph node (PLN) and the number of negative lymph node (NLN) simultaneously. Method:Data from 13,491 patients with EC registered in the SEER database were reviewed. The parameters related to prognosis were investigated using a Cox proportional hazards regression model. A modified N stage was proposed based on the cut-off number of the re-adjusted ratio of the number of PLN (numberPLN) to the number of NLN (numberNLN), which derived from the comparison of the hezode rate (HR) of numberPLN and numberNLN. The modified N stage was confirmed using the cross-validation method with the training and validation cohort, and it was also compared to the N stage from the American Joint Committee on Cancer (AJCC) staging system (7th edition) using Receiver Operating Characteristic (ROC) curve analysis.Results:The numberPLN on prognosis was 1.042, while numberNLN was 0.968. The modified N stage was defined as follows: N1 stage: the ratio range was from 0 to 0.21; N2 stage: more than 0.21, but no more than 0.48; N3 stage: more than 0.48. Cross-validation method within the cohort identified the predictive accuracy of this modified N stage, and ROC curve analysis demonstrated the superiority of this modified N stage over that of the AJCC.Conclusion:The modified N stage based on the re-adjusted ratio of numberPLN to numberNLN can evaluate tumor stage more accurately than the traditional N stage.

Overall and cancer-specific survival in patients with breast Paget disease: A population-based study

2021 ◽  
pp. 153537022110562
Author(s):  
Tingting Hu ◽  
Zhiyuan Chen ◽  
Meng Hou ◽  
Kezhi Lin
Keyword(s):  
Predictive Accuracy ◽  
Tumor Grade ◽  
Curve Analysis ◽  
Histological Type ◽  
Related Factors ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis ◽  
Paget Disease ◽  
Ajcc Stage ◽  
Calibration Curves

Paget disease of the breast is an uncommon malignant tumor with an inferior outcome. Therefore, establishing nomograms to predict the survival outcomes of breast Paget disease patients is urgent. Clinicopathological and follow-up data of breast Paget disease patients diagnosed between 2010 and 2016 were retrieved through the Surveillance, Epidemiology, and End Result (SEER) database. The significant factors were screened out, and then those factors were utilized to build two valuable nomograms. The discriminative ability of nomograms was investigated using concordance-index (C-index), while the predictive accuracy and benefits were evaluated using calibration curves and decision curve analysis. Finally, a total of 417 breast Paget disease patients were enrolled. Tumor grade, histological type, American Joint Committee on Cancer (AJCC) stage, surgery, chemotherapy, and marital status were confirmed as independent overall survival (OS)-related factors; tumor grade, histological type, AJCC stage, and age were associated with independent cancer-specific survival (CSS)-related factors. The values of the C-index for OS nomogram acquired were 0.827 and 0.745 for training and validation cohorts, respectively. Meanwhile, the corresponding values of the C-index to CSS nomogram were 0.890 and 0.655, respectively. The calibration curves and decision curve analysis indicated that both nomograms had an excellent performance. Finally, the nomogram-based risk stratification system indicated that all breast Paget disease patients could be classified into low- and high-risk groups and showed distinct outcomes. In conclusion, two valuable nomograms incorporating various clinicopathological indicators were established for breast Paget disease patients. These prognostic nomograms provide accurate prognostic assessment for breast Paget disease patients and help clinicians select appropriate treatment strategies.

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