Differential Diagnosis of Malignant Thyroid Calcification Nodule Based on Computed Tomography Image Texture

2021 ◽  
Vol 11 (3) ◽  
pp. 767-772
Author(s):  
Wenxian Peng ◽  
Yijia Qian ◽  
Yingying Shi ◽  
Shuyun Chen ◽  
Kexin Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ensemble Learning ◽  
Logistic Regression Analysis ◽  
Texture Features ◽  
Support Vector ◽  
Classification Algorithms ◽  
Feature Optimization

Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning, support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were selected after feature optimization by logistic regression analysis (P <0.05). Both Ensemble Learning and Support Vector Machine achieved the highest accuracy of 97.1%. The PPV, NPV, SEN, and SPC are 96.9%, 97.4%, 98.4%, and 95.0%, respectively. The AUC was 1. Conclusion: Texture features extracted from calcification nodules could be used as biomarkers to identify benign or malignant thyroid calcification.

scholarly journals Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

2018 ◽  
Vol 7 (10) ◽  
pp. 322 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Hyun-Kyu Yoon ◽  
Karam Nam ◽  
Youn Cho ◽  
Tae Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches

Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data were obtained. The primary outcome was postoperative acute kidney injury (AKI) defined according to the Kidney Disease Improving Global Outcomes criteria. The following machine learning techniques were used: decision tree, random forest, extreme gradient boosting, support vector machine, neural network classifier, and deep learning. The performance of these techniques was compared with that of logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUC). During the first postoperative week, AKI occurred in 770 patients (38.3%). The best performance regarding AUC was achieved by the gradient boosting machine to predict the AKI of all stages (0.78, 95% confidence interval (CI) 0.75–0.80) or stage 2 or 3 AKI. The AUC of logistic regression analysis was 0.69 (95% CI 0.66–0.72). Decision tree, random forest, and support vector machine showed similar performance to logistic regression. In our comprehensive comparison of machine learning approaches with logistic regression analysis, gradient boosting technique showed the best performance with the highest AUC and lower error rate. We developed an Internet–based risk estimator which could be used for real-time processing of patient data to estimate the risk of AKI at the end of surgery.

Power contrast injections through a totally implantable venous power port: A retrospective multicenter study

2019 ◽  
Vol 35 (4) ◽  
pp. 268-272 ◽  
Author(s):  
Ryong seong Son ◽  
Yun Gyu Song ◽  
Jeonghyun Jo ◽  
Byeong-Ho Park ◽  
Gyoo-sik Jung ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Contrast Media ◽  
Logistic Regression Analysis ◽  
Computed Tomography Scan ◽  
Power Injection ◽  
Oncologic Patients ◽  
Computed Tomography Scans ◽  
Tomography Scan

Objectives To evaluate the feasibility and safety of power injection of contrast media through totally implantable venous power ports during computed tomography scans in oncologic patients. Methods The study population consisted of 417 patients who underwent computed tomography scan through a totally implantable venous power port. Clinical data were examined. Logistic regression analysis was used to assess the associations between clinical covariables and computed tomography scan failure. Results Successful computed tomography scans were achieved in 534 of 540 examinations (98.9%). Logistic regression analysis showed that contrast media above a 350 concentration was significantly associated with computed tomography scan failure (95% confidence interval: 1.01–1.13, p = 0.012). No major complications were noted. Conclusions Power injection of contrast media through a totally implantable venous power port for computed tomography examination is feasible and safe. This procedure provides an acceptable alternative in oncologic patients with inadequate peripheral intravenous access when computed tomography examination with contrast enhancement is needed.

scholarly journals The Value of MRI Findings Combined With Texture Analysis in the Differential Diagnosis of Primary Ovarian Granulosa Cell Tumors and Ovarian Thecoma–Fibrothecoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Nai-yu Li ◽  
Bin Shi ◽  
Yu-lan Chen ◽  
Pei-pei Wang ◽  
Chuan-bin Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Texture Analysis ◽  
Granulosa Cell ◽  
Logistic Regression Analysis ◽  
Texture Features ◽  
Intratumoral Hemorrhage ◽  
Granulosa Cell Tumors ◽  
Ovarian Granulosa Cell

ObjectiveThis study aims to explore the value of magnetic resonance imaging (MRI) and texture analysis (TA) in the differential diagnosis of ovarian granulosa cell tumors (OGCTs) and thecoma-fibrothecoma (OTCA–FTCA).MethodsThe preoperative MRI data of 32 patients with OTCA–FTCA and 14 patients with OGCTs, confirmed by pathological examination between June 2013 and August 2020, were retrospectively analyzed. The texture data of three-dimensional MRI scans based on T2-weighted imaging and clinical and conventional MRI features were analyzed and compared between tumor types. The Mann–Whitney U-test, χ2 test/Fisher exact test, and multivariate logistic regression analysis were used to identify differences between the OTCA–FTCA and OGCTs groups. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic curve analysis was carried out to evaluate diagnostic efficiency.ResultsA multivariate analysis of the imaging-based features combined with TA revealed that intratumoral hemorrhage (OR = 0.037), log-sigma-20mm-3D_glszm_SmallAreaEmphasis (OR = 4.40), and log-sigma-2-0mm-3D_glszm_SmallAreaHighGrayLevelEmphasis (OR = 1.034) were independent features for discriminating between OGCTs and OTCA–FTCA (P &lt; 0.05). An imaging-based diagnosis model, TA-based model, and combination model were established. The areas under the curve of the three models in predicting OGCTs and OTCA–FTCA were 0.935, 0.944, and 0.969, respectively; the sensitivities were 93.75, 93.75, and 96.87%, respectively; and the specificities were 85.71, 92.86, and 92.86%, respectively. The DeLong test indicated that the combination model had the highest predictive efficiency (P &lt; 0.05), with no significant difference among the three models in differentiating between OGCTs and OTCA–FTCA (P &gt; 0.05).ConclusionsCompared with OTCA–FTCA, intratumoral hemorrhage may be characteristic MR imaging features with OGCTs. Texture features can reflect the microheterogeneity of OGCTs and OTCA–FTCA. MRI signs and texture features can help differentiate between OGCTs and OTCA–FTCA and provide a more comprehensive and accurate basis for clinical treatment.

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

scholarly journals Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Meng-ru Li ◽  
Ming-zhu Liu ◽  
Ya-qiong Ge ◽  
Ying Zhou ◽  
Wei Wei
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Gene Mutation ◽  
Logistic Regression Analysis ◽  
Peritoneal Metastasis ◽  
Texture Features ◽  
Brca Gene ◽  
Ct Features ◽  
Brca Gene Mutation

PurposeTo evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer.MethodRetrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology and complete preoperative images in our hospital, including 37 and 58 cases with mutant type and wild type BRCA, respectively (total: 95 cases). The enrolled patients’ routine CT features were analyzed by two radiologists. Then, ROIs were jointly determined through negotiation, and the ITK-SNAP software package was used for 3D outlining of the third-stage images of the primary tumor lesions and obtaining texture features. For routine CT features and texture features, Mann-Whitney U tests, single-factor logistic regression analysis, minimum redundancy, and maximum correlation were used for feature screening, and the performance of individual features was evaluated by ROC curves. Multivariate logistic regression analysis was used to further screen features, find independent predictors, and establish the prediction model. The established model’s diagnostic efficiency was evaluated by ROC curve analysis, and the histogram was obtained to conduct visual analysis of the prediction model.ResultsAmong the routine CT features, the type of peritoneal metastasis, mesenteric involvement, and supradiaphragmatic lymph node enlargement were correlated with BRCA gene mutation (P &lt; 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P &gt; 0.05). Multivariate logistic regression analysis retained six features, including one routine CT feature and five texture features. Among them, the type of peritoneal metastasis was used as an independent predictor (P &lt; 0.05), which had the highest diagnostic efficiency. Its AUC, accuracy, specificity, and sensitivity were 0.74, 0.79, 0.90, and 0.62, respectively. The prediction model based on the combination of routine CT features and texture features had an AUC of 0.86 (95% CI: 0.79–0.94) and accuracy, specificity, and sensitivity of 0.80, 0.76, and 0.81, respectively, indicating a better performance than that of any single feature.ConclusionsBoth routine CT features and texture features had value for predicting the mutation state of the BRCA gene, but their predictive efficiency was low. When the two types of features were combined to establish a predictive model, the model’s predictive efficiency was significantly higher than that of independent features.

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