scholarly journals Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations

2021 ◽  
Vol 12 ◽  
Author(s):  
Shaozhi Zhao ◽  
Qi Zhao ◽  
Yuming Jiao ◽  
Hao Li ◽  
Jiancong Weng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Features ◽  
Arteriovenous Malformations ◽  
Clinical Usefulness ◽  
Clinical Risk Factors ◽  
Combined Model ◽  
Clinical Model ◽  
Clinical Risk ◽  
Brain Arteriovenous Malformations ◽  
Tof Mra

Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy.Methods: This retrospective study enrolled 176 patients with unruptured bAVMs. After manual lesion segmentation, a total of 858 radiomics features were extracted from time-of-flight magnetic resonance angiography (TOF-MRA). A radiomics model was constructed, and a radiomics score was calculated. Meanwhile, the demographic and angioarchitectural characteristics of patients were assessed to build a clinical model. Incorporating the radiomics score and independent clinical risk factors, a combined model was constructed. The performance of the models was assessed with respect to discrimination, calibration, and clinical usefulness.Results: The clinical model incorporating 3 clinical features had an area under the curve (AUC) of 0.71. Fifteen radiomics features were used to build the radiomics model, which had a higher AUC of 0.78. Incorporating the radiomics score and clinical risk factors, the combined model showed a favorable discrimination ability and calibration, with an AUC of 0.82. Decision curve analysis (DCA) demonstrated that the combined model outperformed the clinical model and radiomics model in terms of clinical usefulness.Conclusions: The radiomics features extracted from TOF-MRA were associated with epilepsy in patients with unruptured bAVMs. The radiomics-clinical nomogram, which was constructed based on the model incorporating the radiomics score and clinical features, showed favorable predictive efficacy for bAVM-related epilepsy.

scholarly journals A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Liang ◽  
Xin Zhi ◽  
Ya Sun ◽  
Huarong Li ◽  
Jiajun Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Diagnostic Performance ◽  
Clinical Risk Factors ◽  
Net Benefit ◽  
Combined Model ◽  
Clinical Model ◽  
Clinical Risk ◽  
Wide Range ◽  
Multiparametric Ultrasound

ObjectivesTo evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).MethodsA total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.ResultsThe multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.ConclusionsClinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

scholarly journals An Improvement of Survival Stratification in Glioblastoma Patients via Combining Subregional Radiomics Signatures

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Yang ◽  
Yu Han ◽  
Xintao Hu ◽  
Wen Wang ◽  
Guangbin Cui ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cox Regression ◽  
Clinical Usefulness ◽  
Clinical Risk Factors ◽  
Survival Prediction ◽  
Clinical Risk ◽  
Single Region ◽  
Tumor Region ◽  
Selection Operator ◽  
Radiomics Signature

PurposeTo investigate whether combining multiple radiomics signatures derived from the subregions of glioblastoma (GBM) can improve survival prediction of patients with GBM.MethodsIn total, 129 patients were included in this study and split into training (n = 99) and test (n = 30) cohorts. Radiomics features were extracted from each tumor region then radiomics scores were obtained separately using least absolute shrinkage and selection operator (LASSO) COX regression. A clinical nomogram was also constructed using various clinical risk factors. Radiomics nomograms were constructed by combing a single radiomics signature from the whole tumor region with clinical risk factors or combining three radiomics signatures from three tumor subregions with clinical risk factors. The performance of these models was assessed by the discrimination, calibration and clinical usefulness metrics, and was compared with that of the clinical nomogram.ResultsIncorporating the three radiomics signatures, i.e., Radscores for ET, NET, and ED, into the radiomics-based nomogram improved the performance in estimating survival (C-index: training/test cohort: 0.717/0.655) compared with that of the clinical nomogram (C-index: training/test cohort: 0.633/0.560) and that of the radiomics nomogram based on single region radiomics signatures (C-index: training/test cohort: 0.656/0.535).ConclusionThe multiregional radiomics nomogram exhibited a favorable survival stratification accuracy.

scholarly journals Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for the Prediction of Neoadjuvant Chemotherapy-Insensitive Breast Cancers

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhongyi Wang ◽  
Fan Lin ◽  
Heng Ma ◽  
Yinghong Shi ◽  
Jianjun Dong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Neoadjuvant Chemotherapy ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Usefulness ◽  
Clinical Risk Factors ◽  
Breast Cancers ◽  
Training Set ◽  
Clinical Risk ◽  
Background Parenchymal Enhancement ◽  
Contrast Enhanced

PurposeWe developed and validated a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict neoadjuvant chemotherapy (NAC)-insensitive breast cancers prior to treatment.MethodsWe enrolled 117 patients with breast cancer who underwent CESM examination and NAC treatment from July 2017 to April 2019. The patients were grouped randomly into a training set (n = 97) and a validation set (n = 20) in a ratio of 8:2. 792 radiomics features were extracted from CESM images including low-energy and recombined images for each patient. Optimal radiomics features were selected by using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, to develop a radiomics score in the training set. A radiomics nomogram incorporating the radiomics score and independent clinical risk factors was then developed using multivariate logistic regression analysis. With regard to discrimination and clinical usefulness, radiomics nomogram was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC) and decision curve analysis (DCA).ResultsThe radiomics nomogram that incorporates 11 radiomics features and 3 independent clinical risk factors, including Ki-67 index, background parenchymal enhancement (BPE) and human epidermal growth factor receptor-2 (HER-2) status, showed an encouraging discrimination power with AUCs of 0.877 [95% confidence interval (CI) 0.816 to 0.924] and 0.81 (95% CI 0.575 to 0.948) in the training and validation sets, respectively. DCA revealed the increased clinical usefulness of this nomogram.ConclusionThe proposed radiomics nomogram that integrates CESM-derived radiomics features and clinical parameters showed potential feasibility for predicting NAC-insensitive breast cancers.

Long-Term Follow-Up of Patients With Isolated Side Branch Coronary Artery Disease

Angiology ◽  
2021 ◽  
pp. 000331972110280
Author(s):  
Sukru Arslan ◽  
Ahmet Yildiz ◽  
Okay Abaci ◽  
Urfan Jafarov ◽  
Servet Batit ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Vessel Diameter ◽  
Side Branch ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Artery Disease

The data with respect to stable coronary artery disease (SCAD) are mainly confined to main vessel disease. However, there is a lack of information and long-term outcomes regarding isolated side branch disease. This study aimed to evaluate long-term major adverse cardiac and cerebrovascular events (MACCEs) in patients with isolated side branch coronary artery disease (CAD). A total of 437 patients with isolated side branch SCAD were included. After a median follow-up of 38 months, the overall MACCE and all-cause mortality rates were 14.6% and 5.9%, respectively. Among angiographic features, 68.2% of patients had diagonal artery and 82.2% had ostial lesions. In 28.8% of patients, the vessel diameter was ≥2.75 mm. According to the American College of Cardiology lesion classification, 84.2% of patients had either class B or C lesions. Age, ostial lesions, glycated hemoglobin A1c, and neutrophil levels were independent predictors of MACCE. On the other hand, side branch location, vessel diameter, and lesion complexity did not affect outcomes. Clinical risk factors seem to have a greater impact on MACCE rather than lesion morphology. Therefore, the treatment of clinical risk factors is of paramount importance in these patients.

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