Transpara Archives - The Imaging Wire

Momentum is building toward routine clinical use of AI for breast cancer screening. Several new studies offer even more support for mammography AI, including research published today in Nature Medicine in which AI reduced radiologist workload by over 60% by excluding low-risk studies from human review.

Breast screening has become one of the most promising use cases for AI, with the potential to reduce radiologists’ workload while improving their ability to detect cancer.

For example, the recent MASAI study found that ScreenPoint Medical’s Transpara AI algorithm could replace the second human reader in a double-reading protocol, reducing workload by 44% and improving cancer detection rates by 28%.

The new research in Nature Medicine also used Transpara, as part of the AITIC study in Spain with the goal of seeing if AI could triage low-risk studies so they don’t require review by human radiologists.

AITIC had a prospective design, involving 31k women with screening exams split between 2D mammography (17k) and digital breast tomosynthesis (14k).

Women in the control arm of the study got conventional double reading by two radiologists – the standard mammography paradigm in Europe.

The intervention arm used a partially autonomous AI approach: cases that AI interpreted as low risk were classified as normal and were not reviewed by radiologists, while all other cases were double-read by radiologists using AI support.

In analyzing the results, researchers found…

Workload in the AI arm was 64% lower than conventional double reading.
AI’s workload reduction was similar between DBT and conventional digital mammography (-66% and -62%, respectively).
The AI arm’s cancer detection rate per 1k women was 15% higher (7.3 vs. 6.3 cancers).
But the recall rate was also 15% higher.

It’s worth noting that the AITIC study differed from MASAI in its inclusion of DBT screening exams, whereas MASAI only included 2D digital mammography.

While 2D mammography is the norm in Europe, much of the U.S. has switched to DBT for breast screening, so the AITIC results offer good news for U.S. breast imaging practices considering AI adoption.

The Takeaway

The AITIC study’s new results are powerful confirmation of findings from the recent MASAI trial and support broader clinical deployment of mammography AI. Taken together with positive findings from last week’s Nature Cancer articles (see The Wire section in this newsletter), they paint a picture of a technology that’s ready for prime time.

One of the biggest radiology stories of 2023 was the release of impressive interim results from the MASAI study, a large-scale trial of AI for breast screening in Sweden. At ECR 2024, MASAI researchers put an emphatic cap on the conference by presenting final data indicating that AI could have an even bigger impact on mammography screening than we thought.

If you remember, MASAI’s interim results were published in August in Lancet Oncology and showed that ScreenPoint Medical’s Transpara AI algorithm was able to reduce radiologist workload by 44% when used as part of the kind of double-reading screening program that’s common in Europe.

Another MASAI finding was that AI-aided screening had a 20% higher cancer detection rate than conventional double-reading with human radiologists, but the difference was not statistically significant.

That’s all changed with the final MASAI results, presented at ECR on March 2 by senior author Kristina Lång, MD, of Lund University.

Lång presented data from 106k participants who were randomized to either screening with Transpara V. 1.7 or conventional double reading without AI.

Transpara triaged mammograms by giving them a risk score of 1-10, and only those classified as high risk received double reading; lower-risk mammograms got a single human reader. In the final analysis, AI-aided screening …

Had a 28% higher cancer detection rate per 1k women (6.4 vs. 5.0), a difference that was statistically significant (p=0.002)
Detected more cancers 10-20 mm (122 vs. 79)
Detected more cancers of non-specific histologic type (204 vs. 155)
Detected 20 more non-luminal A invasive cancers and 12 more DCIS grade 3 lesions

The Takeaway

When combined with the Lancet Oncology data, the new MASAI results indicate that AI could enable breast radiologists to have their cake and eat it too: a lower workload with higher cancer detection rates.

Get every issue of The Imaging Wire, delivered right to your inbox.