Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

Cui, Wen Jing; Wang, Cheng; Jia, Ling; Ren, Shuai; Duan, Shao Feng; Cui, Can; Chen, Xiao; Wang, Zhong Qiu

doi:10.3389/fonc.2019.00433

Cited by 15 publications

(18 citation statements)

References 21 publications

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“…The results indicated that the diagnostic performance of the TA model or the combination model was greater than that achieved with image-based analysis alone (AUC: 0.894 vs 0.886 vs 0.687), even though there was no difference between the TA model and the combination model. Therefore, compared with conventional imaging findings based on human visual analysis, TA or combined with imaging findings has the potential in improving the differential diagnosis ability between BPTs and BMPTs, which is consistent with the result of a previous study by Cui et al (31), who found that combining mammography findings and texture features can provide optimal predictions in the classification of PTs in mammography.…”

Section: Discussionsupporting

confidence: 87%

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Guo

Chen

et al. 2021

Front. Oncol.

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PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.

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Section: Discussionsupporting

confidence: 87%

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Guo

Chen

et al. 2021

Front. Oncol.

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“…They concluded that the texture data obtained from MG images of PTs allowed identification of grade 1 PTs from grades 2 and 3. In addition, the combination of using both MG and texture findings increased both the sensitivity and specificity of the diagnoses [1].…”

Section: Discussionmentioning

confidence: 99%

“…Ein unabhängiger maschineller Lernansatz, MATLAB R2020a (Math Works, Natick, Massachu-Introduction Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions. Less than 1 % of breast lesions and 2-3 % of all fibroepithelial lesions are diagnosed as PTs [1]. They are classified as three different forms: benign (BPT), borderline (BoPT), and malignant phyllodes tumors (MPT) with a grading system accepted by the World Health Organization [2].…”

Section: Zusammenfassungmentioning

confidence: 99%

Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors

et al. 2021

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Purpose Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs. Methods The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups. Results In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs. Conclusion In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.

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“…In the recent years, radiomics increasingly draws attention and has demonstrated that it may be a tool that can obtain high-fidelity target information to comprehensively evaluate lesions, especially the texture features in the image that are not recognized by the naked eye and reveal the inherent heterogeneity of the tissue, reflecting the subtle differences between different tissues. Radiomics can be combined with the imaging appearance to further improve the differential diagnosis ability of the lesion ( 30 , 31 ). The AK, an imaging analytic software used in this study, has been used in many research reports ( 32 , 33 ).…”

Section: Discussionmentioning

confidence: 99%

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

Zhang

et al. 2020

Front. Oncol.

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Background: Kimura disease may be easily misdiagnosed as malignant tumors such as lymph node metastases based on imaging and clinical symptoms. The aim of this article is to investigate whether the radiomic features and the model based on the features on venous-phase contrast-enhanced CT (CECT) images can distinguish Kimura disease from lymph node metastases in the head and neck. Methods: A retrospective analysis of 14 patients of head and neck Kimura disease (a total of 38 enlarged lymph nodes) and 39 patients with head and neck lymph node metastases (a total of 39 enlarged lymph nodes), confirmed by biopsy or surgery resection, was conducted. All patients accepted CECT within 10 days before biopsy or surgery resection. Radiomic features based on venous-phase CECT were generated automatically from Artificial-Intelligence Kit (AK) software. All lymph nodes were randomly divided into the training set (n = 54) and testing set (n = 23) in a ratio of 7:3. ANOVA + Mann-Whitney, Spearman correlation, least absolute shrinkage and selection operator, and Gradient Descent were introduced for the reduction of the highly redundant features. Binary logistic regression model was constructed based on the selected features. Receiver operating characteristic was used to evaluate the diagnostic performance of the features and the model. Finally, a nomogram was established for model application. Results : Seven features were screened out at the end. Significant difference was found between the two groups for all the features with area under the curves (AUCs) ranging from 0.759 to 0.915. The AUC of the model's identification performance was 0.970 in the training group and 0.977 in the testing group. The disease discrimination efficiency of the model was better than that of any single feature. Conclusions : The radiomic features and the model based on these features on venous-phase CECT images had very good performance for the discrimination between Kimura disease and lymph node metastases in the head and neck.

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Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

Cited by 15 publications

References 21 publications

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

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