scholarly journals Artificial Intelligence: A Primer for Breast Imaging Radiologists

2020 ◽  
Vol 2 (4) ◽  
pp. 304-314
Author(s):  
Manisha Bahl
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Breast Cancer Detection ◽  
Computer Algorithms ◽  
Prevention Strategies ◽  
Future Directions ◽  
Optimal Patient Care ◽  
Screening And Prevention

Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.

Imaging Approaches and Findings in the Reconstructed Breast: A Pictorial Essay

2011 ◽  
Vol 62 (1) ◽  
pp. 60-72 ◽  
Author(s):  
Anabel M. Scaranelo ◽  
Bridgette Lord ◽  
Riham Eiada ◽  
Stefan O. Hofer
Keyword(s):  
Breast Cancer ◽  
Breast Reconstruction ◽  
Cancer Detection ◽  
Reconstructive Surgery ◽  
Genetic Screening ◽  
Breast Imaging ◽  
Breast Cancer Detection ◽  
Imaging Findings ◽  
Pictorial Essay ◽  
Reconstructed Breast

Advances in breast imaging over the last 15 years have improved early breast cancer detection and management. After treatment for breast cancer, many women choose to have reconstructive surgery. In addition, with the availability of widespread genetic screening for breast cancer, an increasing number of women are choosing prophylactic mastectomies and subsequent breast reconstruction. The purpose of this pictorial essay is to present the spectrum of imaging findings in the reconstructed breast.

Salivary metabolomics with artificial intelligence-based methods for breast cancer detection and subtype prediction

2020 ◽  
Vol 138 ◽  
pp. S18
Author(s):  
T. Murata ◽  
T. Yanagisawa ◽  
T. Kurihara ◽  
M. Kaneko ◽  
S. Ota ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Breast Cancer Detection

scholarly journals Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool

2020 ◽  
Vol 2 (6) ◽  
pp. e190208
Author(s):  
Serena Pacilè ◽  
January Lopez ◽  
Pauline Chone ◽  
Thomas Bertinotti ◽  
Jean Marie Grouin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Detection Accuracy ◽  
Concurrent Use

Implementation of a direct patient callback system improves compliance for additional breast imaging and early breast cancer detection

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6073-6073
Author(s):  
D. Richard-Kowalski ◽  
D. Termeulen ◽  
M. Reed ◽  
R. Reyes ◽  
M. Kuliga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Recall Rate ◽  
Screening Mammography ◽  
Breast Cancer Detection ◽  
Detection Methods ◽  
Diagnose Breast Cancer ◽  
Additional Imaging

6073 Background: Existing patient recall systems usually involve contacting the referring physician who then notifies the patient to schedule a return visit for further imaging. We set out to determine whether a direct patient callback system would improve patient compliance in returning for additional imaging including magnification, spot compression, and ultrasound, and whether that would translate to an improvement in early breast cancer detection. Methods: Beginning on 4/1/2004, we prospectively identified all patients whose screening mammograms were read as having an incomplete assessment that required additional imaging (ACR BIRADS 0). Those patients were contacted directly via telephone to return for additional views. Results: Between 11/1/2002 and 3/31/3004, 1142 patients with incomplete screening mammography were identified and the referring physicians were contacted. 956 of 1142 (84%) patients returned and underwent additional breast imaging. Between 4/1/2004 and 12/31/2005, 1,336 patients with incomplete screening mammography were contacted directly to return for additional imaging. 1,307 of 1,336 (98%) patients returned and underwent additional breast imaging. (p < 0.0001, Fisher’s exact test). 125 of the 1,307 (8.5%) of the subsequent exams were found to be suspicious and biopsy was recommended (ACR BIRADS 4 or 5). Conclusions: Our new system of contacting patients with incomplete mammography has significantly increased our recall rate. Implementation of this system has enabled us to identify those patients whose mammograms are suspicious and ultimately diagnose breast cancer earlier. Direct patient callback has become standard policy and we are recommending this system for all radiology recall examinations. No significant financial relationships to disclose.

Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence

2020 ◽  
pp. 505-515
Author(s):  
Bifta Sama Bari ◽  
Sabira Khatun ◽  
Kamarul Hawari Ghazali ◽  
Md. Moslemuddin Fakir ◽  
Wan Nur Azhani W. Samsudin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Wide Band ◽  
Breast Cancer Detection ◽  
Ultra Wide Band

scholarly journals Incoherent Radar Imaging for Breast Cancer Detection and Experimental Validation against 3D Multimodal Breast Phantoms

2021 ◽  
Vol 7 (2) ◽  
pp. 23
Author(s):  
Antonio Cuccaro ◽  
Angela Dell’Aversano ◽  
Giuseppe Ruvio ◽  
Jacinta Browne ◽  
Raffaele Solimene
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Experimental Validation ◽  
Reconstruction Algorithm ◽  
Breast Cancer Detection ◽  
Beam Forming ◽  
Clutter Rejection ◽  
Breast Phantom ◽  
Set Up

In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare them with classical coherent beam-forming. Those methods have the remarkable advantage of not requiring antenna characterization/compensation, which can be problematic in view of the close (to the breast) proximity set-up usually employed in breast imaging. Moreover, we proceed to an experimental validation of one of the incoherent methods, i.e., the I-MUSIC, using the multimodal breast phantom we have previously developed. While in a previous paper we focused on the phantom manufacture and characterization, here we are mainly concerned with providing the detail of the reconstruction algorithm, in particular for a new multi-step clutter rejection method that was employed and only barely described. In this regard, this contribution can be considered as a completion of our previous study. The experiments against the phantom show promising results and highlight the crucial role played by the clutter rejection procedure.

scholarly journals Recent Advances in Microwave Imaging for Breast Cancer Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-26 ◽  
Author(s):  
Sollip Kwon ◽  
Seungjun Lee
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Time Domain ◽  
Breast Imaging ◽  
Imaging System ◽  
Microwave Imaging ◽  
Breast Cancer Detection ◽  
Promising Alternative ◽  
Recent Advances ◽  
Scan Time

Breast cancer is a disease that occurs most often in female cancer patients. Early detection can significantly reduce the mortality rate. Microwave breast imaging, which is noninvasive and harmless to human, offers a promising alternative method to mammography. This paper presents a review of recent advances in microwave imaging for breast cancer detection. We conclude by introducing new research on a microwave imaging system with time-domain measurement that achieves short measurement time and low system cost. In the time-domain measurement system, scan time would take less than 1 sec, and it does not require very expensive equipment such as VNA.

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