scholarly journals Effectiveness of hysteroscopic resection of a uterine caesarean niche can be predicted: a prospective cohort study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian Zhu ◽  
Xiaoqing He ◽  
Ling Jiang ◽  
Guiling Liang ◽  
Chenfeng Zhu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Prospective Cohort ◽  
Multivariable Logistic Regression Analysis ◽  
Mri Findings ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Tenfold Cross Validation ◽  
Magnetic Resonance Imaging Mri ◽  
Individualized Prediction ◽  
Hysteroscopic Resection

Abstract This study aimed to develop and validate a model for the preoperative prediction of the effectiveness of hysteroscopic resection of a uterine cesarean niche in patients with postmenstrual spotting. The predictive model was developed in a primary prospective cohort consisting of 208 patients with niche treated by hysteroscopic resection. Multivariable logistic regression analysis was performed to develop the predictive model, which incorporated preoperative menstrual characteristics and magnetic resonance imaging (MRI) findings. Surgical efficacy was defined as a decrease in postmenstrual spotting duration of at least 3 days at the 3-month follow-up compared with baseline. The predictive model was presented with a nomogram, and the performance was assessed with respect to its calibration, discrimination, and clinical use. Internal validation was performed using tenfold cross-validation. The predictive factors in the final model were as follows: preoperative menstrual duration, thickness of the residual myometrium (TRM), length, TRM/thickness of the adjacent myometrium ratio, angle γ, area, and presence of a lateral branch of the niche. The model showed good performance in predicting the effectiveness of hysteroscopic niche resection. Incorporating the preoperative duration of the menstrual period and MRI findings of the niche into an easy-to-use nomogram facilitates the individualized prediction of the effectiveness of a hysteroscopic niche resection by 26 Fr resectoscope, but multicenter prospective studies are needed to validate it.

scholarly journals Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Jianqiu Kong ◽  
Junjiong Zheng ◽  
Jieying Wu ◽  
Shaoxu Wu ◽  
Jinhua Cai ◽  
...  
Keyword(s):  
External Validation ◽  
Area Under The Curve ◽  
Multivariable Logistic Regression Analysis ◽  
Lasso Regression ◽  
Clinical Predictors ◽  
Internal Validation ◽  
Clinical Model ◽  
Magnetic Resonance Imaging Mri ◽  
Individualized Prediction ◽  
Radiomics Signature

Abstract Background Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. Methods In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. Results Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. Conclusion We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

scholarly journals Hematological Indexes Can Be Used to Predict the Incidence of Hypothyroidism in Nasopharyngeal Carcinoma Patients after Radiotherapy

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ling Zhou ◽  
Jia Chen ◽  
Chang-Juan Tao ◽  
Shuang Huang ◽  
Jiang Zhang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Predictive Model ◽  
Immune Cells ◽  
Cross Validation ◽  
Complete Blood Count ◽  
Univariate Analysis ◽  
Optimal Method ◽  
Internal Validation ◽  
N Stage ◽  
Tenfold Cross Validation

Background. This study explored the relationship between thyroid-associated antibodies, immune cells, and hypothyroidism to establish a predictive model for the incidence of hypothyroidism in patients with nasopharyngeal carcinoma (NPC) after radiotherapy. Methods. A total of 170 patients with NPC treated at the Cancer Hospital of University of Chinese Academy of Sciences between January 2015 and August 2018 were included. The complete blood count, biochemical, coagulation function, immune cells, and thyroid-associated antibodies tested before radiotherapy were evaluated. A logistic regression model was performed to elucidate which hematological indexes were related to hypothyroidism development. A predictive model for the incidence of hypothyroidism was established. Internal verification of the multifactor model was performed using the tenfold cross-validation method. Results. The univariate analysis showed that immune cells had no statistically significant differences among the patients with and without hypothyroidism. Sex, N-stage, antithyroid peroxidase antibody (TPO-Ab), antithyroglobulin antibody (TG-Ab), thyroglobulin (TG), and fibrinogen (Fb) were associated with hypothyroidism. Males and early N-stage were protective factors of thyroid function, whereas increases in TPO-Ab, TG-Ab, TG, and Fb counts were associated with an increased rate of hypothyroidism incidence. The multivariate analysis showed that TPO-Ab, TG-Ab, TG, and Fb were independent predictors of hypothyroidism. The comprehensive effect of the significant model, including TPO-Ab, TG-Ab, TG, and Fb counts, represented the optimal method of predicting the incidence of radiation-induced hypothyroidism (AUC=0.796). Tenfold cross-validation methods were applied for internal validation. The AUCs of the training and testing sets were 0.792 and 0.798, respectively. Conclusion. A model combining TPO-Ab, TG-Ab, TG, and Fb can be used to screen populations at a high risk of developing hypothyroidism after radiotherapy.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

2016 ◽  
Vol 34 (18) ◽  
pp. 2157-2164 ◽  
Author(s):  
Yan-qi Huang ◽  
Chang-hong Liang ◽  
Lan He ◽  
Jie Tian ◽  
Cui-shan Liang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Validation Cohort ◽  
Good Discrimination ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Individualized Prediction ◽  
Radiomics Signature ◽  
Good Calibration

Purpose To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). Patients and Methods The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous–phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. Results The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusion This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

A MRI-based radiomics model for preoperative prediction of five-year survival status in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15596-e15596
Author(s):  
Xiao-Hang Wang ◽  
Liu-Hua Long ◽  
Yong Cui ◽  
Angela Y Jia ◽  
Xiang-Gao Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Model Performance ◽  
Area Under The Curve ◽  
Clinical Risk Factor ◽  
Multivariable Logistic Regression Analysis ◽  
Survival Status ◽  
Clinical Risk ◽  
Preoperative Prediction ◽  
Magnetic Resonance Imaging Mri ◽  
Radiomics Signature

e15596 Background: Recurrence is the major cause of mortality in resected hepatocellular carcinoma (HCC) patients. However, without a standard approach to evaluate prognosis, it is difficult to select potential candidates for additional therapy. We aim to develop and evaluate a magnetic resonance imaging (MRI)-based radiomics model to predict 5-year survival status of HCC patients in the preoperative setting. Methods: A total of 201 HCC patients who were followed up for at least 5 years (unless death occurred) after curative hepatectomy were enrolled in this retrospective multicenter study. 3144 radiomics features were extracted from four conventional sequences of preoperative MRI (T1WI, T2WI, DWI and dynamic contrast-enhanced MRI). The random forest method was used for feature selection and radiomics signature building. 5-fold cross validation was used for robust estimation. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. The model performance was evaluated by its discrimination and calibration. Results: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on survival status at 5 years from surgery. The 30 most survival-related radiomics features were selected to develop the radiomics signature. The preoperative alpha-fetoprotein level was integrated into the model as an independent clinical risk factor in multivariable logistic regression analysis (OR = 3.764; 95% CI 1.997-7.096). The radiomics model demonstrated good calibration and satisfactory discrimination, with the mean area under the curve of 0.9340 (95% CI 0.9222-0.9458) in training set and 0.7383 (95% CI 0.6914-0.7852) in validation set. Conclusions: The MRI-based radiomics model represents a valid method to predict 5-year survival status in HCC patients in the preoperative setting, and may be used to guide neoadjuvant or adjuvant treatment decisions in high-risk patients.

scholarly journals Development of a Predictive Model of Tuberculosis Transmission among Household Contacts

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Saibin Wang
Keyword(s):  
Predictive Model ◽  
Secondary Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Lasso Regression ◽  
Internal Validation ◽  
Tuberculosis Transmission ◽  
Household Contacts ◽  
Study Sites ◽  
Individualized Prediction ◽  
Index Cases

Background. Household contacts of patients with tuberculosis (TB) are at great risk of TB infection. The aim of this study was to develop a predictive model of TB transmission among household contacts. Method. This was a secondary analysis of data from a prospective cohort study, in which a total of 700 TB patients and 3417 household contacts were enrolled between 2010 and 2013 at two study sites in Peru. The incidence of secondary TB cases among household contacts of index cases was recorded. The LASSO regression method was used to reduce the data dimension and to filter variables. Multivariate logistic regression analysis was applied to develop the predictive model, and internal validation was performed. A nomogram was constructed to display the model, and the AUC was calculated. The calibration curve and decision curve analysis (DCA) were also evaluated. Results. The incidence of TB disease among the contacts of index cases was 4.4% (149/3417). Ten variables (gender, age, TB history, diabetes, HIV, index patient’s drug resistance, socioeconomic status, spoligotypes, and the index-contact share sleeping room status) filtered through the LASSO regression technique were finally included in the predictive model. The model showed good discriminatory ability, with an AUC value of 0.761 (95% CI, 0.723–0.800) for the derivation and 0.759 (95% CI, 0.717–0.796) for the internal validation. The predictive model showed good calibration, and the DCA demonstrated that the model was clinically useful. Conclusion. A predictive model was developed that incorporates characteristics of both the index patients and the contacts, which may be of great value for the individualized prediction of TB transmission among household contacts.

scholarly journals Nomogram for predicting severe morbidity after pheochromocytoma surgery

2020 ◽  
Vol 9 (4) ◽  
pp. 309-317
Author(s):  
Hongyan Wang ◽  
Bin Wu ◽  
Zichuan Yao ◽  
Xianqing Zhu ◽  
Yunzhong Jiang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Predictive Model ◽  
Validation Cohort ◽  
Binary Logistic Regression ◽  
Hemodynamic Instability ◽  
Primary Treatment ◽  
Multivariable Logistic Regression Analysis ◽  
Binary Logistic Regression Model ◽  
Severe Morbidity ◽  
Individualized Prediction

Purpose Although resection is the primary treatment strategy for pheochromocytoma, surgery is associated with a high risk of morbidity. At present, there is no nomogram for prediction of severe morbidity after pheochromocytoma surgery, thus the aim of the present study was to develop and validate a nomogram for prediction of severe morbidity after pheochromocytoma surgery. Methods The development cohort consisted of 262 patients who underwent unilateral laparoscopic or open pheochromocytoma surgery at our center between 1 January 2007 and 31 December 2016. The patients’ clinicopathological characters were recorded. The least absolute shrinkage and selection operator (LASSO) binary logistic regression model was used for data dimension reduction and feature selection, then multivariable logistic regression analysis was used to develop the predictive model. An independent validation cohort consisted of 128 consecutive patients from 1 January 2017 and 31 December 2018. The performance of the predictive model was assessed in regards to discrimination, calibration, and clinical usefulness. Results Predictors of this model included sex, BMI, coronary heart disease, arrhythmia, tumor size, intraoperative hemodynamic instability, and surgical duration. For the validation cohort, the model showed good discrimination with an AUROC of 0.818 (95% CI, 0.745, 0.891) and good calibration (Unreliability test, P = 0.440). Decision curve analysis demonstrated that the model was also clinically useful. Conclusions A nomogram was developed to facilitate the individualized prediction of severe morbidity after pheochromocytoma surgery and may help to improve the perioperative strategy and treatment outcome.

Accuracy of Rotator Cuff Tears and Tendinosis Diagnoses on Shoulder Ultrasound Performed by a Short-experienced Operator

2018 ◽  
Vol 1 ◽  
pp. 9
Author(s):  
Harshad Arvind Vanjare ◽  
Jyoti Panwar
Keyword(s):  
Rotator Cuff ◽  
Negative Predictive Value ◽  
Rotator Cuff Tear ◽  
Predictive Value ◽  
Rotator Cuff Tears ◽  
Mri Findings ◽  
Cuff Tear ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
Sensitivity Specificity

Objective The objective of the study was to assess the accuracy of ultrasound examination for the diagnosis of rotator cuff tear and tendinosis performed by a short experienced operator, compared to magnetic resonance imaging (MRI) results. Method A total of 70 subjects suspected to have rotator cuff tear or tendinosis and planned for shoulder MRI were included in the study. Shoulder ultrasound was performed either before or after the MRI scan on the same day. Ultrasound operator had a short experience in performing an ultrasound of the shoulder. Ultrasound findings were correlated to MRI findings. Results Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the diagnosis of tendinosis were 58%, 84%, 63%, 80%, and 75%, respectively, and it was 68%, 91%, 73%, 88%, and 85%, respectively, for the diagnosis of rotator cuff tear. Conclusions Sensitivity for diagnosing rotator cuff tear or tendinosis was moderate but had a higher negative predictive value. Thus, the ultrasound operator with a short experience in performing shoulder ultrasound had moderate sensitivity in diagnosing tendinosis or tears; however, could exclude them with confidence.

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