With the Advent of Tomosynthesis in the Workup of Mammographic Abnormality, is Spot Compression Mammography Now Obsolete? An Initial Clinical Experience

Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography

Purpose

To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month.

Methods

This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC).

Results

Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD.

Conclusion

Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.

Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography

Background

Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views.

Purpose

To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view.

Material and Methods

From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

Results

Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance ( P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P  < 0.001) significantly improved with AI assistance.

Conclusion

AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.

Comparison of Narrow-angle and Wide-angle Digital Breast Tomosynthesis Systems in Clinical Practice

Digital breast tomosynthesis (DBT) is a pseudo 3D mammography imaging technique that has become widespread since gaining Food and Drug Administration approval in 2011. With this technology, a variable number of tomosynthesis projection images are obtained over an angular range between 15° and 50° for currently available clinical DBT systems. The angular range impacts various aspects of clinical imaging, such as radiation dose, scan time, and image quality, including visualization of calcifications, masses, and architectural distortion. This review presents an overview of the differences between narrow- and wide-angle DBT systems, with an emphasis on their applications in clinical practice. Comparison examples of patients imaged on both narrow- and wide-angle DBT systems illustrate these differences. Understanding the potential variable appearance of imaging findings with narrow- and wide-angle DBT systems is important for radiologists, particularly when comparison images have been obtained on a different DBT system. Furthermore, knowledge about the comparative strengths and limitations of DBT systems is needed for appropriate equipment selection.

A review of mammographic lesion localisation and work up imaging in Australia in the digital era

OBJECTIVES

To investigate the use of coned compression with and without magnification in contemporary Australian digital imaging. To describe lesion localisation techniques used for mammographic work up.

KEY FINDINGS

As digital breast tomosynthesis becomes mainstream, the need for coned compression imaging has reduced, however the need for coned compression with fine focus magnification for assessment of microcalcification remains. Adapting film screen lesion localisation techniques to the digital setting is limited by the need for "true size" 1:1 ratio images for ease of measurement. Both the digital ruler and a grid technique can be used as an alternate.

CONCLUSION

Advances in image acquisition has evidenced a change in imaging protocols for suspicions lesions within the breast with breast tomosynthesis superseding the need for non-magnified coned compression views of the breast. Adaptation of the approaches to localising these lesions in the digital setting has also been necessary.

Masses in the era of screening tomosynthesis: Is diagnostic ultrasound sufficient?

METHODS:

All masses recalled from screening digital breast tomosynthesis between July 1, 2017 and December 31, 2017 that were sent either to diagnostic mammography or ultrasound were compared. Size, shape, margins, visibility on ultrasound, diagnostic assessment and pathology of all masses along with breast density were evaluated.

RESULTS:

102/212 digital breast tomosynthesis screen-detected masses were worked up with diagnostic mammography initially and 110/212 were worked up with ultrasound directly. There was no significant difference in ultrasound visibility of masses sent to diagnostic mammography first with those sent to ultrasound first (p = 0.42). 4 (4%) masses sent to mammogram first and 2 (2%) masses sent to ultrasound first were not visualized. There was a significant difference in size between masses that were visualized under ultrasound versus those that were not (p = 0.01), when masses in both groups were assessed cumulatively.

CONCLUSIONS:

98% of digital breast tomosynthesis screen-detected masses sent to ultrasound directly were adequately assessed without diagnostic mammography.

ADVANCES IN KNOWLEDGE:

There is potential for avoiding a diagnostic mammogram for evaluation of majority of digital breast tomosynthesis screen-detected masses.