Prospective-retrospective analysis of a novel blood-based combinatorial biomarker assay (liquid biopsy) in 859 patients to demonstrate the benefit of combining biomarker data with imaging to differentiate benign breast lesions from invasive breast cancer in women.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. e22260-e22260
Author(s):  
David Emery Reese ◽  
Kasey Benson ◽  
Michael Silver ◽  
Sherri Borman ◽  
Meredith C. Henderson ◽  
...  
2019 ◽  
Author(s):  
George Plitas ◽  
Monica Morrow ◽  
Brandon R Bruns

A breast mass is the most common presenting symptom among patients in a breast clinic. The presence of a breast mass can cause a great deal of anxiety in women, as well as their physicians. The differential diagnosis of a palpable breast abnormality is broad, although the majority of breast masses are benign. The responsibility of the physician who is evaluating a breast mass is to exclude the presence of malignancy. Once cancer is ruled out, the physician should then attempt to provide an accurate diagnosis, appropriate treatment, and reassurance to the patient. This chapter discusses the assessment of normal breast physiology, identification of a breast mass, evaluation of the various classifications of breast mass (e.g., dominant mass with clinically benign features and dominant mass with suspicious features), differential diagnosis and management of common benign breast masses (e.g., cysts, fibroadenomas, phyllodes tumors, hamartomas, fat necrosis), and the risk of breast cancer associated with benign breast lesions. The chapter also discusses the diagnosis and management of a breast mass in male patients. Tables outline breast lesions that may present as a palpable abnormality, factors used for the assessment of breast cancer risk, physical characteristics of benign and malignant breast masses, the accuracy of fine-needle aspiration, and benign breast lesions by category. Figures illustrate diagnostic procedures, the anatomy of the human breast, visual inspection of the breasts, physical examination of the breasts, breast palpation technique, the evaluation and management of a new breast mass, and the identification of cysts. This review contains 10 figures, 14 tables, and 64 references. Keywords: breast mass, lobuloalveolar development, subareolar nodularity, parenchyma (glandular elements), stromal tissue, ovarian graafian follicles


2019 ◽  
Author(s):  
George Plitas ◽  
Monica Morrow ◽  
Brandon R Bruns

A breast mass is the most common presenting symptom among patients in a breast clinic. The presence of a breast mass can cause a great deal of anxiety in women, as well as their physicians. The differential diagnosis of a palpable breast abnormality is broad, although the majority of breast masses are benign. The responsibility of the physician who is evaluating a breast mass is to exclude the presence of malignancy. Once cancer is ruled out, the physician should then attempt to provide an accurate diagnosis, appropriate treatment, and reassurance to the patient. This chapter discusses the assessment of normal breast physiology, identification of a breast mass, evaluation of the various classifications of breast mass (e.g., dominant mass with clinically benign features and dominant mass with suspicious features), differential diagnosis and management of common benign breast masses (e.g., cysts, fibroadenomas, phyllodes tumors, hamartomas, fat necrosis), and the risk of breast cancer associated with benign breast lesions. The chapter also discusses the diagnosis and management of a breast mass in male patients. Tables outline breast lesions that may present as a palpable abnormality, factors used for the assessment of breast cancer risk, physical characteristics of benign and malignant breast masses, the accuracy of fine-needle aspiration, and benign breast lesions by category. Figures illustrate diagnostic procedures, the anatomy of the human breast, visual inspection of the breasts, physical examination of the breasts, breast palpation technique, the evaluation and management of a new breast mass, and the identification of cysts. This review contains 10 figures, 14 tables, and 64 references. Keywords: breast mass, lobuloalveolar development, subareolar nodularity, parenchyma (glandular elements), stromal tissue, ovarian graafian follicles


2017 ◽  
Vol Volume 10 ◽  
pp. 477-481 ◽  
Author(s):  
Sandi Shen ◽  
Shizhen Zhong ◽  
Gaofang Xiao ◽  
Haibo Zhou ◽  
Wenhua Huang

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yu Ji ◽  
Hui Li ◽  
Alexandra V. Edwards ◽  
John Papaioannou ◽  
Wenjuan Ma ◽  
...  

Abstract Background As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists. Methods Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task. Results In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies. Conclusion On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.


2013 ◽  
Vol 39 (5) ◽  
pp. 475 ◽  
Author(s):  
John Winstanley ◽  
Mark Pearson ◽  
Philip Rudland ◽  
Angela Platt Higgins

2007 ◽  
Vol 13 (18) ◽  
pp. 5474-5479 ◽  
Author(s):  
Maria J. Worsham ◽  
Usha Raju ◽  
Mei Lu ◽  
Alissa Kapke ◽  
Jingfang Cheng ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document