Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features

Non-ischemic cardiomyopathies are defined as either primary or secondary diseases of the myocardium resulting in cardiac dysfunction. While primary cardiomyopathies are confined to the heart and can be genetic or acquired, secondary cardiomyopathies show involvement of the heart as a manifestation of an underlying systemic disease including metabolic, inflammatory, granulomatous, infectious, or autoimmune entities. Non-ischemic cardiomyopathies are currently classified as hypertrophic, dilated, restrictive, or unclassifiable, including left ventricular non-compaction. Cardiovascular Magnetic Resonance Imaging (CMRI) not only has the capability to assess cardiac morphology and function, but also the ability to detect edema, hemorrhage, fibrosis, and intramyocardial deposits, providing a valuable imaging tool in the characterization of non-ischemic cardiomyopathies. This pictorial essay shows some of the most important non-ischemic cardiomyopathies with an emphasis on magnetic resonance imaging features.

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Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy

The Breast ◽

10.1016/j.breast.2019.11.009 ◽

2020 ◽

Vol 49 ◽

pp. 115-122 ◽

Cited By ~ 6

Author(s):

Roberto Lo Gullo ◽

Sarah Eskreis-Winkler ◽

Elizabeth A. Morris ◽

Katja Pinker

Keyword(s):

Magnetic Resonance Imaging ◽

Machine Learning ◽

Magnetic Resonance ◽

Neoadjuvant Chemotherapy ◽

Early Prediction ◽

Resonance Imaging ◽

Prediction Of Response ◽

Multiparametric Magnetic Resonance Imaging ◽

Response To Neoadjuvant Chemotherapy

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Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging and Fusion Guided Targeted Biopsy Evaluated by Transperineal Template Saturation Prostate Biopsy for the Detection and Characterization of Prostate Cancer

The Journal of Urology ◽

10.1016/j.juro.2018.02.067 ◽

2018 ◽

Vol 200 (2) ◽

pp. 309-318 ◽

Cited By ~ 20

Author(s):

Ashkan Mortezavi ◽

Olivia Märzendorfer ◽

Olivio F. Donati ◽

Gianluca Rizzi ◽

Niels J. Rupp ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Prostate Cancer ◽

Magnetic Resonance ◽

Diagnostic Accuracy ◽

Prostate Biopsy ◽

Targeted Biopsy ◽

Resonance Imaging ◽

Multiparametric Magnetic Resonance Imaging

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Multiparametric magnetic resonance imaging findings of prostatic pure leiomyomas

Einstein (São Paulo) ◽

10.1590/s1679-45082016ao3715 ◽

2016 ◽

Vol 14 (3) ◽

pp. 374-377 ◽

Cited By ~ 1

Author(s):

Thais Caldara Mussi ◽

Yves Bohrer Costa ◽

Marcos Takeo Obara ◽

Marcos Roberto Gomes de Queiroz ◽

Rodrigo Gobbo Garcia ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Prostate Cancer ◽

Magnetic Resonance ◽

Peripheral Zone ◽

Transitional Zone ◽

Imaging Features ◽

Imaging Findings ◽

Resonance Imaging ◽

Imaging Methods ◽

Multiparametric Magnetic Resonance Imaging

ABSTRACT Objective: To describe the imaging findings of prostatic tumors nonadenocarcinoma on multiparametric magnetic resonance imaging. Methods: A total of 200 patients underwented multiparametric magnetic resonance imaging of the prostate for screening for prostate cancer, from August 2013 to September 2014, followed by biopsy with ultrasound/magnetic resonance imaging fusion. Results: We found three pathologic proved cases of prostatic pure leiomyomas (0.02%) in our series and described the multiparametric magnetic resonance imaging features of these prostatic leiomyomas. The imaging findings had similar features to lesions with moderate or high suspicion for significant cancer (Likert 4 or 5) when localized both in the transitional zone or in the peripheral zone of the gland. Conclusion: Pure prostatic leiomyomas had imaging findings on multiparametric magnetic resonance imaging that mimicked usual adenocarcinomas on this test. Radiologists, urologists and pathologists must be aware of this entity and its imaging features.

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