Road to RSNA 2024: Women’s Imaging Preview

Women’s imaging at this year’s RSNA reflects momentous changes in trends regarding personalized, targeted healthcare in 2024 for women’s health.

Research efforts toward supplemental breast imaging have ramped up in recent years and in Chicago, attendees can see results from these imaging modalities being put to the test. Modalities ranging from ultrasound and MRI to molecular breast imaging and contrast-enhanced mammography (CEM) will have their time in the spotlight.

In particular, researchers will display findings for imaging dense breasts, which continues to be a challenge for women’s health. The U.S. Food and Drug Administration (FDA) has updated its Mammography Quality Standards Act (MQSA) to require doctors to notify women of their breast density.

Women with dense breasts are recommended for supplemental imaging, which means the race is on to see which method is best. RSNA attendees can see the pros and cons of each respective modality for themselves.

Also, AI’s continuing emergence for breast imaging applications shows no signs of abating. At RSNA 2024, research will cover more ways that AI is being used in women’s imaging. These include how an MRI-based deep learning system can predict pathologic complete response for chemotherapy, how AI boosts breast cancer detection, and what women really think of AI in breast imaging.

Advanced imaging techniques such as molecular imaging and multi-system imaging will be discussed, with researchers presenting studies suggesting their respective utility in women’s health.

It may seem like a competition to see which modality can best accompany or even exceed mammography in breast cancer screening and diagnosis. But the end goal is all the same, to make way for the best patient outcomes and reduce breast cancer mortality.

Research will also showcase the latest advancements in OB/GYN imaging, such as radiogenomics for evaluating endometrial cancer features and fetal MRI to measure fetal cerebral blood flow.

RSNA attendees can also explore the technical exhibits for the many women’s imaging technologies to be highlighted by participating vendors, such as new AI software and mammography, ultrasound, and MRI scanners. Attendees can also take advantage of the many educational sessions that will put the spotlight on the complex and challenging aspects of personalized, targeted radiological care. This includes glimpses of what the future may hold for radiologists in women’s imaging and other specialties. Poster presentations will also enable attendees to explore the large, diverse research to be showcased in Chicago.

Keep reading for highlights of just some of the many presentations scheduled for this year's meeting. You can also access the complete scientific and educational program on the RSNA 2024 website.


FAPI PET/CT bests F-18 FDG PET/CT in identifying breast cancer lesions

Sunday, December 1 | 9:30 a.m.-9:40 a.m. | S1-SSNMMI01-4 | Room S405

In this presentation, researchers will discuss gallium-68 (Ga-68) FAPI PET/CT’s utility in breast cancer detection and staging.


Many women welcome use of AI in breast imaging

Sunday, December 1 | 11:00 a.m.-11:10 a.m. | S2-SSBR01-4 | Room S406A

Attendees in this session will see and hear results on women’s perceptions regarding AI’s introduction and use for breast imaging applications.


AI boosts breast cancer detection on DBT

Sunday, December 1 | 11:20 a.m.-11:30 a.m. | S2-SSBR01-6 | Room S406A

Here, results will be shown on how AI impacts breast cancer detection on digital breast tomosynthesis (DBT) exams based on various factors.


CEM phantoms show utility in quality control

Sunday, December 1 | 1:40 p.m.-1:50 a.m. | S4-SSPH02-5 | Room S404

In this session, audience members will learn about the sustainability of commercially available contrast-enhanced mammography (CEM) phantoms in quality control programs.


Breast cancers found on screening mammo have better prognosis

Monday, December 2 | 8:00 a.m.-8:10 a.m. | M1-SSBR04-1 | Room S406A

The impact of screening mammography versus clinical mode of detection on mortality will be explored in this presentation.


Contrast imaging techniques have tradeoffs in diagnosing breast cancer

Monday, December 2 | 8:00 a.m.-8:10 a.m. | M1-SSBR03-1 | Room S404

Attendees will get to see the results of supplemental imaging techniques from the Breast Screening - Risk Adaptive Imaging for Density (BRAID) Trial in this session.


MRI shines as supplemental breast imaging modality

Monday, December 2 | 8:20 a.m.-8:30 a.m. | M1-SSBR04-3 | Room S406A

Out of all of the supplemental breast imaging modalities, MRI detects the most cancers, according to this scientific presentation.


Breast masses on ultrasound mostly benign in child patients

Tuesday, December 3 | 8:30 a.m.-8:40 a.m. | T1-SSPD03-4 | Room E350

In this session, results will be shown on the prevalence and outcome of breast masses found on ultrasound evaluation and biopsy in children.


High-res PET/CT on par with MRI for diagnosing breast cancer extent

Tuesday, December 3 | 4:40 p.m.-4:50 p.m. | T8-SSNMMI04-2 | Room E353C

Here, attendees will find out whether high-resolution semiconductor PET/CT can compare with MRI in diagnosing the extent of breast cancer.


Shear-wave dispersion improves breast lesion evaluation

Wednesday, December 4 | 9:50 a.m.-10:00 a.m. | W3-SSBR08-5 | Room S406A

Audience members will learn about how ultrasound shear-wave dispersion (SWD) technology can help measure breast lesion size, depth, and breast thickness.


BACs on mammo point to heart conditions in women

Wednesday, December 4 | 3:50 p.m.-4:00 p.m. | W7-SSCA08-6 | Room E353C

In this presentation, audience members will learn about the potential correlation between breast arterial calcifications (BACs) on mammography and the prevalence of various acute and chronic cardiovascular events.


MRI-based deep learning system predicts breast cancer treatment response

Thursday, December 5 | 8:40 a.m.-8:50 a.m. | R1-SSBR10-5 | Room S406A

Attendees in this session will learn about the performance of an MRI-based fully automated deep-learning system that predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.

 

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