scholarly journals The Effect of Accompanying In Situ Ductal Carcinoma on Accuracy of Measuring Malignant Breast Tumor Size Using B-Mode Ultrasonography and Real-Time Sonoelastography

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
A. A. Soliman ◽  
S. Wojcinski ◽  
F. Degenhardt
Keyword(s):  
Real Time ◽  
Tumor Size ◽  
Breast Tumor ◽  
Ductal Carcinoma ◽  
Malignant Breast Tumor ◽  
Malignant Breast

Comparison of Mammography and Ultrasonography for Tumor Size of DCIS of Breast Cancer

2019 ◽  
Vol 15 (2) ◽  
pp. 209-213
Author(s):  
Yu Wang ◽  
Jiantao Wang ◽  
Haiping Wang ◽  
Xinyu Yang ◽  
Liming Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Correlation Coefficient ◽  
Tumor Size ◽  
Ductal Carcinoma ◽  
Pearson Correlation ◽  
Dense Breast ◽  
Pathological Examination ◽  
Negative Group ◽  
The Difference

Objective: Accurate assessment of breast tumor size preoperatively is important for the initial decision-making in surgical approach. Therefore, we aimed to compare efficacy of mammography and ultrasonography in ductal carcinoma in situ (DCIS) of breast cancer. Methods: Preoperative mammography and ultrasonography were performed on 104 women with DCIS of breast cancer. We compared the accuracy of each of the imaging modalities with pathological size by Pearson correlation. For each modality, it was considered concordant if the difference between imaging assessment and pathological measurement is less than 0.5cm. Results: At pathological examination tumor size ranged from 0.4cm to 7.2cm in largest diameter. For mammographically determined size versus pathological size, correlation coefficient of r was 0.786 and for ultrasonography it was 0.651. Grouped by breast composition, in almost entirely fatty and scattered areas of fibroglandular dense breast, correlation coefficient of r was 0.790 for mammography and 0.678 for ultrasonography; in heterogeneously dense and extremely dense breast, correlation coefficient of r was 0.770 for mammography and 0.548 for ultrasonography. In microcalcification positive group, coeffient of r was 0.772 for mammography and 0.570 for ultrasonography. In microcalcification negative group, coeffient of r was 0.806 for mammography and 0.783 for ultrasonography. Conclusion: Mammography was more accurate than ultrasonography in measuring the largest cancer diameter in DCIS of breast cancer. The correlation coefficient improved in the group of almost entirely fatty/ scattered areas of fibroglandular dense breast or in microcalcification negative group.

scholarly journals Clinicopathological predictors of postoperative upstaging to invasive ductal carcinoma (IDC) in patients preoperatively diagnosed with ductal carcinoma in situ (DCIS): a multi-institutional retrospective cohort study

Breast Cancer ◽  
2021 ◽  
Author(s):  
Kiyo Tanaka ◽  
Norikazu Masuda ◽  
Naoki Hayashi ◽  
Yasuaki Sagara ◽  
Fumikata Hara ◽  
...  
Keyword(s):  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma In Situ ◽  
Tumor Size ◽  
Medical Records ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Palpable Mass ◽  
Combined Use ◽  
A Prospective Study

Abstract Background We conducted a prospective study with the intention to omit surgery for patients with ductal carcinoma in situ (DCIS) of the breast. We aimed to identify clinicopathological predictors of postoperative upstaging to invasive ductal carcinoma (IDC) in patients preoperatively diagnosed with DCIS. Patients and methods We retrospectively analyzed patients with DCIS diagnosed through biopsy between April 1, 2010 and December 31, 2014, from 16 institutions. Clinical, radiological, and histological variables were collected from medical records. Results We identified 2,293 patients diagnosed with DCIS through biopsy, including 1,663 DCIS (72.5%) cases and 630 IDC (27.5%) cases. In multivariate analysis, the presence of a palpable mass (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.2–2.6), mammography findings (≥ category 4; OR 1.8; 95% CI 1.2–2.6), mass formations on ultrasonography (OR 1.8; 95% CI 1.2–2.5), and tumor size on MRI (> 20 mm; OR 1.7; 95% CI 1.2–2.4) were independent predictors of IDC. Among patients with a tumor size on MRI of ≤ 20 mm, the possibility of postoperative upstaging to IDC was 22.1%. Among the 258 patients with non-palpable mass, nuclear grade 1/2, and positive for estrogen receptor, the possibility was 18.1%, even if the upper limit of the tumor size on MRI was raised to ≤ 40 mm. Conclusion We identified four independent predictive factors of upstaging to IDC after surgery among patients with DCIS diagnosed by biopsy. The combined use of various predictors of IDC reduces the possibility of postoperative upstaging to IDC, even if the tumor size on MRI is larger than 20 mm.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

scholarly journals A case of giant cell tumor of the breast, clinically suspected as malignant breast tumor

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Mitsuo Terada ◽  
Naomi Gondo ◽  
Masataka Sawaki ◽  
Masaya Hattori ◽  
Akiyo Yoshimura ◽  
...  
Keyword(s):  
Giant Cell Tumor ◽  
Giant Cell ◽  
Breast Tumor ◽  
Cell Tumor ◽  
Malignant Breast Tumor ◽  
Malignant Breast

scholarly journals A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Mengwan Wei ◽  
Yongzhao Du ◽  
Xiuming Wu ◽  
Qichen Su ◽  
Jianqing Zhu ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Morphological Features ◽  
High Dimensional ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

scholarly journals Characterization of ductal carcinoma in situ cell lines established from breast tumor of a Singapore Chinese patient

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Jacklyn WY Yong ◽  
Meng Ling Choong ◽  
SiFang Wang ◽  
Yu Wang ◽  
Shermaine QY Lim ◽  
...  
Keyword(s):  
Cell Lines ◽  
Ductal Carcinoma In Situ ◽  
Breast Tumor ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Chinese Patient ◽  
Singapore Chinese

Diagnostic Values of Breast Imaging Reporting and Data System and ultrasound Elastography in Benign and Malignant Breast Tumor

2016 ◽  
Vol 13 (10) ◽  
pp. 6509-6513
Author(s):  
Xin-Hua Lu
Keyword(s):  
Breast Cancer ◽  
Roc Curve ◽  
Predictive Value ◽  
Breast Tumor ◽  
Breast Imaging ◽  
Data System ◽  
Ultrasound Elastography ◽  
Malignant Breast Tumor ◽  
Diagnostic Values ◽  
Malignant Breast

Objective: To evaluate the diagnostic values of Breast Imaging Reporting and Data System (BI-RADS), ultrasound elastography (UE) and the combination in differentiating benign and malignant breast tumor. Methods: The BI-RADS and UE image features of 248 breast cancer patients (a total of 260 lesions) proved by surgery and pathology from February 2013 to March 2015 were retrospectively analyzed. With the pathologic results as the gold standard, the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated for BI-RADS, UE and the combination. On the basis of the sensitivity and specificity, they were analyzed by receiver operating characteristic (ROC) curve. Results: In all 260 lesions, 71 lesions were benign and 189 were malignant according to UE diagnosis; 50 lesions were benign and 210 were malignant proved by BI-RADS; 55 lesions were benign and 205 were malignant diagnosed by the combination. The sensitivity (86.09%), specificity (61.64%), positive predictive value (85.19%), negative predictive value (63.38%), and accuracy (79.23%) of ultrasound elastography were all less than that of BI-RADS (98.39%, 64.38%, 88.85%, 87.62%, 94.00%) and the combination (99.47%, 73.97%, 92.31%, 90.73%, 98.18%). The areas under the ROC curve for UE, BI-RADS and the combination were respectively 0.746[95%CI(0.673–0.818)], 0.814[95%CI(0.744–0.884)] and 0.867[95%CI(0.805–0.929)]. Conclusion: Ultrasonic BI-RADS can be the first choice for diagnosing breast cancer, with UE as the auxiliary method. The combined application can further improve the diagnosis rate of benign and malignant breast tumor.

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