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 < 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 < 0.05), with no significant difference among the three models in differentiating between OGCTs and OTCA–FTCA (P > 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 Endoscopic ultrasound in diagnosis of solid pancreatic lesions: Elastography or contrast-enhanced harmonic alone versus the combination

2017 ◽  
Vol 05 (11) ◽  
pp. E1136-E1143 ◽  
Author(s):  
Tanyaporn Chantarojanasiri ◽  
Yoshiki Hirooka ◽  
Hiroki Kawashima ◽  
Eizaburo Ohno ◽  
Takamichi Kuwahara ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Endoscopic Ultrasound ◽  
Inflammatory Pseudotumor ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Contrast Enhanced ◽  
The Individual

Abstract Background and study aims Endoscopic ultrasound (EUS) elastography (EUS-E) and contrast-enhanced harmonic EUS (CH-EUS) are useful methods for the diagnosis of pancreatic lesions. This study aims to compare the accuracy of combined EUS-E and CH-EUS with that of EUS-E or CH-EUS alone in the differential diagnosis of pancreatic solid lesions. Patients and methods One hundred thirty-six patients with solid pancreatic lesions underwent EUS with both EUS-E and CH-EUS were included. Diagnoses were classified as adenocarcinoma, neuroendocrine tumor (NET), and inflammatory pseudotumor in 95, 22, and 19 patients, respectively. EUS records in each case were rearranged into 3 groups: EUS-E, CH-EUS, and combination. Each modality was randomly reviewed by 3 reviewers with different levels of clinical experience. Sensitivity, specificity, and accuracy of each modality according to each diagnosis group were evaluated. For the combined diagnosis populations, the proportions of correct diagnoses among the 3 modalities were compared by using the multivariate logistic regression analysis. Results The accuracies of EUS-E, CH-EUS, and the combination of them were 68.4 %, 65.4 %, and 75.7 %, respectively, for adenocarcinoma group; 83.8 %, 82.4 %, and 86.8 % for NET group; 80.1 %, 78.7 %, and 81.6 % for inflammatory pseudotumor group. The multivariate logistic regression analysis for the combined diagnosis populations showed that the proportion of correct diagnoses when EUS-E and CH-EUS were combined was slightly higher than with the other 2 modalities, although the significant differences among them were not observed. Conclusion EUS-E and CH-EUS combined may improve differential diagnosis of solid pancreatic lesions compared with use of the individual modalities.

scholarly journals How to Identify White Coat Hypertension at The First Diagnosis: Establishment of A Scoring Model for The Differential Diagnosis of White Coat Hypertension and Sustained Hypertension

2020 ◽  
Author(s):  
Peng Cai ◽  
Yan Peng ◽  
YuXi Chen ◽  
Yan Wang ◽  
Xukai Wang
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
White Coat ◽  
White Coat Hypertension ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Scoring Model

Abstract Background: To establish a scoring model for the differential diagnosis of white coat hypertension (WCH) and sustained hypertension (SHT). Methods: 553 adults with elevated office blood pressure, normal renal function and no antihypertensive drugs were included in this study. 17 parameters, such as gender and age, were obtained by questionnaire investigation and biochemical index detection. WCH and SHT were distinguished by 24-hour ambulatory blood pressure monitoring. Participants were randomly divided into a training set (445 cases) and a verification set (108 cases). In the training set, the above parameters were screened by LASSO regression and univariate logistic regression analysis, then, the scoring model was constructed through multivariate logistic regression analysis. ROC curve and calibration curve were used to discuss the discrimination and calibration of this scoring model respectivelyResults: 6 parameters were finally selected, namely isolated systolic hypertension, systolic blood pressure, diastolic blood pressure, triglyceride, serum creatinine, and cardiovascular and cerebrovascular diseases. Multivariate logistic regression was used to establish the scoring model. The R2 and AUC of the scoring model in the training set were 0.163 and 0.705, respectively. In the verification set, the R2 of the scoring model was 0.206, and AUC was 0.718. The calibration test results showed that the scoring model had good stability in both training set and verification set (MSE=0. 001, MAE=0. 014; MSE=0. 001, MAE=0. 025, respectively).Conclusion: A stable scoring model for distinguishing WCH can be established, which can assist clinical medical workers to identify WCH at the first diagnosis.

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 < 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P > 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 < 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.

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 The Value of Preoperative Alpha-L-Fucosidase Levels in Evaluation of Malignancy and Differential Diagnosis of Urothelial Neoplasms

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Dongshan Chen ◽  
Naidong Xing ◽  
Zhanwu Cui ◽  
Cong Zhang ◽  
Zhao Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Clinicopathological Parameters ◽  
Roc Curve Analysis ◽  
Urothelial Tumors

Purpose. To evaluate the role of Alpha-L-fucosidase (AFU) in diagnosis and differential diagnosis of pure urothelial carcinoma (UC), urothelial carcinoma with squamous differentiation (UCSD), and squamous cell carcinoma (SqCC). Methods. A retrospective study was performed for 599 patients who were histologically confirmed with urothelial tumor. Preoperative AFU levels were compared across the distinct subgroups with different clinicopathological parameters. ROC curve analysis and logistic regression analysis were performed to further evaluate the clinical application value of serum AFU levels in diagnosis and differential diagnosis of urothelial tumors. Results. There were no statistically significant differences in the AFU levels between different groups with different malignant degrees (UC versus papilloma and papillary urothelial neoplasm of low malignant potential [PUNLMP], high-grade UC versus low-grade UC, invasive versus noninvasive malignant uroepithelial tumor) and different pathological types (UC, UCSD, and SqCC) (all P>0.05). ROC curve analysis and logistic regression analysis showed that there was no statistically significant association between AFU levels and the tumor characteristics (all P>0.05). Conclusions. Preoperative AFU levels cannot serve as a reliable predictor for malignant degree and differential diagnosis, including pure UC, UCSD, and SqCC of urothelial tumors.

scholarly journals Potential Role of the Renal Arterial Resistance Index in the Differential Diagnosis of Diabetic Kidney Disease

2022 ◽  
Vol 12 ◽  
Author(s):  
Haiyang Li ◽  
Yunzhu Shen ◽  
Zhikai Yu ◽  
Yinghui Huang ◽  
Ting He ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Kidney Disease ◽  
Logistic Regression Analysis ◽  
Diabetic Kidney Disease ◽  
Potential Role ◽  
Multivariate Logistic Regression Analysis ◽  
Diagnostic Model ◽  
Diabetic Kidney

AimsTo investigate the potential role of renal arterial resistance index (RI) in the differential diagnosis between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) and establish a better-quantified differential diagnostic model.Materials and MethodsWe consecutively reviewed 469 type 2 diabetes patients who underwent renal biopsy in our center. According to the renal biopsy results, eligible patients were classified into the DKD group and the NDKD group. The diagnostic significance of RI was evaluated by receiver operating characteristic (ROC) curve analysis. Logistic regression analysis was used to search for independent risk factors associated with DKD. Then a novel diagnostic model was established using multivariate logistic regression analysis.ResultsA total of 332 DKD and 137 NDKD patients were enrolled for analysis. RI was significantly higher in the DKD group compared with those in the NDKD group (0.70 vs. 0.63, p&lt; 0.001). The optimum cutoff value of RI for predicting DKD was 0.66 with sensitivity (69.2%) and specificity (80.9%). Diabetic retinopathy, diabetes duration ≥ 60 months, HbA1c ≥ 7.0(%), RI ≥ 0.66, and body mass index showed statistical significance in the multivariate logistic regression analysis. Then, we constructed a new diagnostic model based on these results. And the validation tests indicated that the new model had good sensitivity (81.5%) and specificity (78.6%).ConclusionsRI has a potential role in discriminating DKD from NDKD. The RI-based predicting model can be helpful for differential diagnosis of DKD and NDKD.

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