Pregnancy-lactation cycle: how to use imaging methods for breast evaluation

Abstract Pregnancy and lactation constitute states of intense hormonal variation with secretory and structural changes in the breast parenchyma. These changes translate into important features on breast imaging, as well as the emergence of specific benign and malignant lesions. This literature review aims to discuss the safety of the use of breast imaging methods (mammography, ultrasound, and magnetic resonance imaging) during the pregnancy-lactation cycle, and to present the expected physiological changes and imaging appearance of the main breast diseases that may occur in this period, such as galactocele, lactating adenoma, fibroadenoma, puerperal mastitis, and pregnancy-associated breast cancer.

Download Full-text

Breast Ultrasound during Lactation: Benign and Malignant Lesions

Breast Care ◽

10.1159/000491781 ◽

2018 ◽

Vol 14 (1) ◽

pp. 30-34

Author(s):

Nuray Haliloglu ◽

Evren Ustuner ◽

Esra Ozkavukcu

Keyword(s):

Breast Cancer ◽

Structural Changes ◽

Imaging Findings ◽

Breast Parenchyma ◽

Lactating Women ◽

Malignant Lesions ◽

Us Findings ◽

Us Scan ◽

True Mass

Background: Structural changes during lactation make breast physical examination difficult. When breast problems occur, patients are often referred for an ultrasound (US) scan. Most breast lesions diagnosed in these patients are benign, but the diagnosis of breast cancer is a challenge. We aim to demonstrate the spectrum of US imaging findings in lactating women. Methods: 77 breastfeeding patients who underwent breast US in our department between February 2012 and March 2017 were evaluated. Patients' electronic medical records were reviewed for the presenting complaint, US reports, pathology results if available, and clinical/radiologic follow-up. All examinations were performed by 2 radiologists. Results: 28 of the 77 patients had normal US findings. Cysts were seen in 16 patients. 4 patients had stable fibroadenomas. 6 patients had US imaging findings suggestive of mastitis, 5 patients had galactoceles, 1 patient had an abscess, and 1 patient had unilateral hypertrophy without any accompanying lesion. In 13 patients, BI-RADS 3 solid masses were diagnosed. Invasive breast cancer was diagnosed in 3 patients. Conclusion: US can demonstrate or exclude a true mass against the background of a nodular breast parenchyma. Radiologists must be aware of malignant US features to avoid delays in the diagnosis of pregnancy-associated breast cancer.

Download Full-text

Management of Breast Magnetic Resonance Imaging-Detected Lesions

Canadian Association of Radiologists Journal ◽

10.1016/j.carj.2010.11.003 ◽

2012 ◽

Vol 63 (3) ◽

pp. 192-206 ◽

Cited By ~ 3

Author(s):

Jean M. Seely

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Breast Imaging ◽

Breast Magnetic Resonance Imaging ◽

Resonance Imaging ◽

Predictive Values ◽

Guided Biopsy ◽

Malignant Lesions ◽

Breast Magnetic Resonance ◽

Magnetic Resonance Imaging Mri

Breast magnetic resonance imaging (MRI) has become an essential component of breast imaging. Whether it is used as a problem-solving tool or a screening test or for staging patients with breast cancer, it detects many lesions in the breast. The challenge for the radiologist is to distinguish significant from insignificant lesions and to direct their management. A brief summary of the terminology according to the American College of Radiologists lexicon will be provided. This review article will cover the differential diagnosis of enhancing lesions, including masses and nonmass enhancement, from benign and malignant causes. Some of the specific morphologic and kinetic features that help to differentiate benign from malignant lesions will be illustrated, and positive predictive values of these features will be reviewed. The various methods of investigating enhancing lesions of the breast will be discussed, including second-look ultrasound, ultrasound-guided biopsy, stereotactic biopsy, and MRI-guided biopsy. A practical approach to the management of MRI-detected lesions will include timing of follow-up, when to biopsy and when to ignore enhancing lesions in the breast.

Download Full-text

A Computational Approach Toward Identification of Malignant Lesions of the Human Breast: The Dynamics of Magnetic Resonance Imaging Contrast Agent Uptake

10.21236/ada394210 ◽

2000 ◽

Author(s):

Jonathan H. Kaufman ◽

John S. Leigh

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Contrast Agent ◽

Computational Approach ◽

Human Breast ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Contrast ◽

Malignant Lesions ◽

Imaging Contrast Agent

Download Full-text

Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

Applied Sciences ◽

10.3390/app11041880 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1880

Author(s):

Roberta Fusco ◽

Adele Piccirillo ◽

Mario Sansone ◽

Vincenza Granata ◽

Paolo Vallone ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Artificial Intelligence ◽

Feature Selection ◽

Resonance Imaging ◽

Dynamic Features ◽

Surgical Specimen ◽

Dce Mri ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Malignant Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.

Download Full-text

Diagnostic Breast Imaging: Mammography, Sonography, Magnetic Resonance Imaging, and Interventional Procedures

Journal of Medical Radiation Sciences ◽

10.1002/jmrs.90 ◽

2015 ◽

Vol 62 (1) ◽

pp. 86-87

Author(s):

Ann Poulos

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Breast Imaging ◽

Interventional Procedures ◽

Resonance Imaging

Download Full-text

Abnormalities of auditory evoked magnetic fields and structural changes in the left hemisphere of male schizophrenics—A magnetoencephalographic magnetic resonance imaging study

Biological Psychiatry ◽

10.1016/s0006-3223(96)00428-3 ◽

1997 ◽

Vol 42 (7) ◽

pp. 609-616 ◽

Cited By ~ 40

Author(s):

Marketa Hajek ◽

Ralph Huonker ◽

Clemens Boehle ◽

Volz Hans-Peter ◽

Hannes Nowak ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Magnetic Fields ◽

Left Hemisphere ◽

Structural Changes ◽

Imaging Study ◽

Magnetic Resonance Imaging Study ◽

Resonance Imaging ◽

Auditory Evoked Magnetic Fields

Download Full-text

Breast Imaging Reporting and Data System Category 3 for Magnetic Resonance Imaging

Topics in Magnetic Resonance Imaging ◽

10.1097/rmr.0000000000000037 ◽

2014 ◽

Vol 23 (6) ◽

pp. 337-344 ◽

Cited By ~ 5

Author(s):

Karen A. Lee

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Breast Imaging ◽

Data System ◽

Resonance Imaging

Download Full-text

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

BMC Cancer ◽

10.1186/s12885-020-07413-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Sihua Niu ◽

Jianhua Huang ◽

Jia Li ◽

Xueling Liu ◽

Dan Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Breast Imaging ◽

Texture Features ◽

Matrix Analysis ◽

Width Ratio ◽

Malignant Group ◽

Malignant Lesions ◽

Occurrence Matrix ◽

Benign Group

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

Download Full-text