scholarly journals Radiomic Analysis of Contrast-Enhanced Mammography With Different Image Types: Classification of Breast Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Simin Wang ◽  
Ning Mao ◽  
Shaofeng Duan ◽  
Qin Li ◽  
Ruimin Li ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
High Energy ◽  
Breast Lesions ◽  
Wilcoxon Rank Sum Test ◽  
Contrast Enhanced ◽  
Malignant Breast ◽  
The Mean ◽  
Selection Operator

Objective: A limited number of studies have focused on the radiomic analysis of contrast-enhanced mammography (CEM). We aimed to construct several radiomics-based models of CEM for classifying benign and malignant breast lesions.Materials and Methods: The retrospective, double-center study included women who underwent CEM between November 2013 and February 2020. Radiomic analysis was performed using high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images from CEM. Datasets were randomly divided into the training and testing sets at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) method and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the radiomic features and construct the best classification models. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Leave-group-out cross-validation (LGOCV) for 100 rounds was performed to obtain the mean AUCs, which were compared by the Wilcoxon rank-sum test and the Kruskal–Wallis rank-sum test.Results: A total of 192 women with 226 breast lesions (101 benign; 125 malignant) were enrolled. The median age was 48 years (range, 22–70 years). For the classification of breast lesions, the AUCs of the best models were 0.931 (95% CI: 0.873–0.989) for HE, 0.897 (95% CI: 0.807–0.981) for LE, 0.882 (95% CI: 0.825–0.987) for DES images and 0.960 (95% CI: 0.910–0.998) for all of the CEM images in the testing set. According to LGOCV, the models constructed with the HE images and all of the CEM images showed the highest mean AUCs for the training (0.931 and 0.938, respectively; P < 0.05 for both) and testing sets (0.892 and 0.889, respectively; P = 0.55 for both), which were significantly higher than those of the two models constructed with the LE and DES images in the training (0.912 and 0.899, respectively; all P < 0.05) and testing sets (0.866 and 0.862, respectively; all P < 0.05).Conclusions: Radiomic analysis of CEM images was valuable for classifying benign and malignant breast lesions. The use of HE images or all three types of CEM images can achieve the best performance.

scholarly journals Diagnostic Value of Contrast-Enhanced Ultrasonography With SonoVue in the Differentiation of Benign and Malignant Breast Lesions: A Meta-Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382097158
Author(s):  
Jianghao Lu ◽  
Peng Zhou ◽  
Chunchun Jin ◽  
Lifeng Xu ◽  
Xiaomin Zhu ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Breast Lesions ◽  
Summary Receiver Operating Characteristic ◽  
Contrast Enhanced ◽  
Malignant Breast ◽  
Contrast Enhanced Ultrasonography ◽  
Receiver Operating

Purpose: A meta-analysis was conducted to evaluate the diagnostic performance of contrast-enhanced ultrasonography using the contrast agent SonoVue to differentiate benign from malignant breast lesions. Method: A comprehensive search of the literature was performed using the Embase, PubMed, and Web of Science databases to retrieve studies published before February 2020. Data were extracted, and pooled sensitivity, specificity, and diagnostic odds ratios were calculated with meta-analysis software. Heterogeneity was evaluated via the Q test and I2 statistic. Meta-regression and subgroup analyses were applied to evaluate potential sources of heterogeneity. Publication bias was assessed using the Deeks’ funnel plot asymmetry test. A summary receiver operating characteristic curve (SROC) was constructed. Results: A total of 27 studies including 5378 breast lesions subjected to CEUS examination with SonoVue were included in the meta-analysis. The pooled sensitivity and specificity values were 0.90 (95% confidence interval [CI], 0.88–0.91; inconsistency index [ I2] = 75.7%) and 0.83 (95% CI, 0.82–0.85; I2 = 91.0%), respectively. The pooled diagnostic odds ratio was 48.35% (95% CI, 31.22–74.89; I2 = 77.6%). The area under the summary receiver operating characteristic curve (AUC) was 0.9354. Meta-regression analysis revealed the region of patient residence and dose of contrast agent as potential sources of heterogeneity (P < .01). Subgroup analysis showed a higher area under the summary receiver operating characteristic curve for European and higher contrast agent dose subgroups (P < .05). Conclusion: Contrast-enhanced ultrasonography with SonoVue displays high sensitivity, specificity, and accuracy when differentiating benign from malignant breast lesions. Despite its current limitations, this technique presents a promising tool for diagnosing breast lesions in clinical practice.

scholarly journals Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Kun Sun ◽  
Zhicheng Jiao ◽  
Hong Zhu ◽  
Weimin Chai ◽  
Xu Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diagnostic Performance ◽  
Characteristic Curve ◽  
Diffusion Kurtosis Imaging ◽  
Support Vector ◽  
Breast Lesions ◽  
Malignant Breast ◽  
The Mean ◽  
Learning Analysis

Abstract Background This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions. Methods This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADCall b, mADC0–1000), BE (mD, mD*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance. Results RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC0–1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04). Conclusions The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.

scholarly journals Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ran Wei ◽  
Hao Wang ◽  
Lanyun Wang ◽  
Wenjuan Hu ◽  
Xilin Sun ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multiparametric Mri ◽  
Extrathyroidal Extension ◽  
Papillary Thyroid ◽  
Predictive Capability ◽  
Contrast Enhanced ◽  
Selection Operator

Abstract Background To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically. Methods The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model’s performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve. Results Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively. Conclusions Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

scholarly journals Contrast-enhanced ultrasound of breast fibromatosis: a case report

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110106
Author(s):  
Shanhong Lin ◽  
Yong Cao ◽  
Libin Chen ◽  
Mei Chen ◽  
Shengmin Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Rare Case ◽  
Common Type ◽  
Breast Lesions ◽  
Contrast Enhanced Ultrasound ◽  
High Similarity ◽  
Potential Value ◽  
Contrast Enhanced ◽  
Malignant Breast ◽  
Contrast Enhanced Ultrasonography

We herein present a rare case of breast fibromatosis, the contrast-enhanced ultrasonography (CEUS) findings of which we believe have never been described. The high similarity between the clinical and imaging manifestations of breast cancer makes its differential diagnosis difficult. In this report, we describe the CEUS findings of a less common type of fibromatosis, discuss the potential value of CEUS to differentiate it from malignant breast lesions, and briefly review the literature.

scholarly journals Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huanhuan Li ◽  
Long Gao ◽  
He Ma ◽  
Dooman Arefan ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Histological Subtypes ◽  
Promising Tool ◽  
Contrast Enhanced Ct ◽  
The Neural Network ◽  
Contrast Enhanced ◽  
Enhanced Ct

ObjectivesTo evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images.Materials and MethodsA total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples. 107 features were used in five machine learning classifiers to perform the predictive analysis among three subtypes. Models were trained and validated in two conditions: using radiomic features alone, and combining clinical features with radiomic features. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC).ResultsThe highest AUCs in classifying ADC vs. SCC, ADC vs. SCLC, and SCC vs. SCLC were 0.879, 0.836, 0.783, respectively by using only radiomic features in a feedforward neural network.ConclusionOur study indicates that radiomic features based on the CECT images might be a promising tool for noninvasive prediction of histological subtypes in central lung cancer and the neural network classifier might be well-suited to this task.

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