Incorporating Peer Learning Into Your Breast Imaging Practice

Author(s):  
Leah E Schafer ◽  
Hannah Perry ◽  
Michael DC Fishman ◽  
Bernadette V Jakomin ◽  
Priscilla J Slanetz

Abstract Traditional score-based peer review has come under scrutiny in recent years, as studies have demonstrated it to be generally ineffective at improving quality. Many practices and programs are transitioning to a peer learning model to replace or supplement traditional peer review. Peer learning differs from traditional score-based peer review in that the emphasis is on sharing learning opportunities and creating an environment that fosters discussion of errors in a nonpunitive forum with the goal of improved patient care. Creating a just culture is central to fostering successful peer learning. In a just culture, mistakes can be discussed without shame or fear of retribution and the focus is on systems improvement rather than individual blame. Peer learning, as it pertains to breast imaging, can occur in many forms and venues. Examples of the various formats in which peer learning can occur include through individual colleague interaction, as well as divisional, multidisciplinary, department-wide, and virtual conferences, and with the assistance of artificial intelligence. Incorporating peer learning into the practice of breast imaging aims to reduce delayed diagnoses of breast cancer and optimize patient care.

2020 ◽  
Vol 2 (4) ◽  
pp. 304-314
Author(s):  
Manisha Bahl

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.


Author(s):  
Sahar Mansour ◽  
Rasha Kamal ◽  
Lamiaa Hashem ◽  
Basma ElKalaawy

Objectives: to study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound aided mammograms. Methods: Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side-was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference. Results: Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions (n = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10, (n = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 471/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms. Conclusions: AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up. Advances in knowledge: Recently, the indulgence of AI in the work up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage.


2020 ◽  
Vol 93 (1108) ◽  
pp. 20190580 ◽  
Author(s):  
Heang-Ping Chan ◽  
Ravi K. Samala ◽  
Lubomir M. Hadjiiski

Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.


2020 ◽  
pp. 30-31
Author(s):  
Dinesh Sethi ◽  
Namrita Sachdev ◽  
Yashvant Singh

Breast cancer is one of the leading causes of cancer related mortality in women. Mammography is the most widely used imaging modality to detect breast cancer. Due to a large number of screening mammograms and a limited number of breast imaging radiologists available all over the world, the role of Artificial Intelligence in the form of Deep Learning algorithms is being explored to assist the radiologists in interpreting these mammograms.


2021 ◽  
Vol 28 (4) ◽  
pp. 2548-2559
Author(s):  
Andrzej Lorek ◽  
Katarzyna Steinhof-Radwańska ◽  
Anna Barczyk-Gutkowska ◽  
Wojciech Zarębski ◽  
Piotr Paleń ◽  
...  

Contrast-enhanced spectral mammography (CESM) is a promising, digital breast imaging method for planning surgeries. The study aimed at comparing digital mammography (MG) with CESM as predictive factors in visualizing multifocal-multicentric cancers (MFMCC) before determining the surgery extent. We analyzed 999 patients after breast cancer surgery to compare MG and CESM in terms of detecting MFMCC. Moreover, these procedures were assessed for their conformity with postoperative histopathology (HP), calculating their sensitivity and specificity. The question was which histopathological types of breast cancer were more frequently characterized by multifocality–multicentrality in comparable techniques as regards the general number of HP-identified cancers. The analysis involved the frequency of post-CESM changes in the extent of planned surgeries. In the present study, MG revealed 48 (4.80%) while CESM 170 (17.02%) MFMCC lesions, subsequently confirmed in HP. MG had MFMCC detecting sensitivity of 38.51%, specificity 99.01%, PPV (positive predictive value) 85.71%, and NPV (negative predictive value) 84.52%. The respective values for CESM were 87.63%, 94.90%, 80.57% and 96.95%. Moreover, no statistically significant differences were found between lobular and NST cancers (27.78% vs. 21.24%) regarding MFMCC. A treatment change was required by 20.00% of the patients from breast-conserving to mastectomy, upon visualizing MFMCC in CESM. In conclusion, mammography offers insufficient diagnostic sensitivity for detecting additional cancer foci. The high diagnostic sensitivity of CESM effectively assesses breast cancer multifocality/multicentrality and significantly changes the extent of planned surgeries. The multifocality/multicentrality concerned carcinoma, lobular and invasive carcinoma of no special type (NST) cancers with similar incidence rates, which requires further confirmation.


The Breast ◽  
2021 ◽  
Vol 56 ◽  
pp. S83-S84
Author(s):  
T. Rakchob ◽  
P. Jittawannarat ◽  
P. Moonwiriyakit ◽  
U. Seehawong

2021 ◽  
Vol 63 (3) ◽  
pp. 236-244
Author(s):  
O. Díaz ◽  
A. Rodríguez-Ruiz ◽  
A. Gubern-Mérida ◽  
R. Martí ◽  
M. Chevalier

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