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Any fluid-filled cavity or sac that is lined by an epithelium is a cyst, and intracranial cystic lesions are a common finding on CT and MR imaging of the brain.1,2 These lesions contain either cerebrospinal fluid, fluid that is similar to CSF, mucus, or proteinaceous fluid. They are lined by epithelial cells, inflammatory cells, or glial cells. The attenuation characteristics of the cyst on CT and MRI and the contrast enhancement patterns depend on the cyst's contents and the composition of the wall. 

Imaging modalities shed light on intracranial cysts

Emphasizing enhanced workflow efficiency for mammography exams, GE HealthCare will unveil the Pristina Via mammography platform at the Radiological Society of North America (RSNA) conference.

In addition to offering zero-click acquisition to facilitate streamlined workflow, the 

 incorporates prior image comparison, irrespective of mammography vendor, to aid in mammography assessments, according to GE HealthCare.

The company adds that the Pristina Via system offers image to image cycle time for digital breast tomosynthesis (DBT) that is up to twice as fast as other mammography platforms, as well as the lowest radiation dose for all breast thicknesses.

“Simplifying, automating, and streamlining the mammography exam to improve in-room workflows is critical to driving patient compliance and access to screening,” said John Simon, M.D., the CEO of SimonMed. “The enhancements offered by Pristina Via are part of our mission of making advanced technology accessible.”

The new mammography system reportedly offers features that promote workflow efficiency as well as patient comfort.

Topic

GE HealthCare Introduces Pristina Via Mammography System at RSNA

The use of artificial intelligence (AI) tools to provide quantitative coronary tomography (AI-QCT) and coronary plaque analysis (AI-CPA) based on coronary computed tomography angiography (CCTA) will soon be covered by four Medicare administrative contractors (MACs).

Starting on November 24, the MACs Palmetto GBA, National Government Services (NGS), CGS Administrators and WPS administrative groups will cover the use of AI-QCT and AI-CPA for Medicare beneficiaries with acute or stable chest pain, and no known coronary artery disease (CAD), according to a recently updated local coverage determination (LCD) issued by the Centers for Medicare and Medicaid Services (CMS).

The LCD noted coverage of AI tools will be provided for the testing of patients deemed to be at intermediate risk for CAD, or those classified by the Coronary Artery Disease Reporting and Data System (CAD-RADS) as having CAD-RADS 1, CAD-RADS 2 or CAD-RADS 3, based on CCTA findings. The coverage will also apply for those who have had negative or inconclusive evaluation for acute coronary syndrome (ACS), according to the LCD.

The use of the FDA-cleared AI modalities 

“Coronary artery disease is the underlying cause of about 50% of deaths,” noted Ron Blankstein, M.D., the director of cardiac computed tomography at Brigham and Women's Hospital and a professor of medicine at Harvard Medical School in Boston. “Medicare coverage of HeartFlow Plaque Analysis is a major step forward in improving coronary artery disease management. The earlier we can diagnose and use tools to provide accurate plaque quantification, the earlier we can provide tailored treatments, potentially improving outcomes for patients.”

“We are also extremely thankful to the many physicians and medical society partners who used their voices to advocate for this essential AI-QCT technology. Until Cleerly, health-care providers have not had a way to easily and completely assess the primary cause of heart attacks, atherosclerotic plaque burden and type in a non-invasive way, which is especially important for our senior population,” noted James L. Kim, M.D., the CEO and founder of Cleerly. “This decision by the MACs represents a significant step in advancing the field of cardiology and providing the best possible care for our seniors.”

(Editor’s note: For related content, see “

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1. Centers for Medicare and Medicaid Services (CMS). Artificial intelligence enabled CT based quantitative coronary topography (AI-QCT)/coronary plaque analysis (AI-CPA). Available at: 

https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=39851&ver=10

 . Published October 10, 2024. Accessed October 14, 2024.

2. Cleerly. Medicare administrative contractors approve coverage of Cleerly heart scans to Medicare recipients. Business Wire. Available at: 

https://www.businesswire.com/news/home/20241014694634/en/Medicare-Administrative-Contractors-Approve-Coverage-of-Cleerly-Heart-Scans-to-Medicare-Recipients

 . Published October 14, 2024. Accessed October 14, 2024.

3. HeartFlow. HeartFlow announces favorable coverage determination for plaque analysis by four Medicare administrative contractors. GlobeNewswire. Available at: 

https://www.globenewswire.com/news-release/2024/10/11/2962144/0/en/HeartFlow-Announces-Favorable-Coverage-Determination-for-Plaque-Analysis-by-Four-Medicare-Administrative-Contractors.html

4. Elucid. Elucid announces favorable Medicare coverage of AI coronary plaque analysis. Business Wire. Available at: 

https://www.businesswire.com/news/home/20241015489543/en/Elucid-Announces-Favorable-Medicare-Coverage-of-AI-Coronary-Plaque-Analysis

 . Published October 15, 2024. Accessed October 15, 2024.

Four Medicare administrative contractors (MACs) will provide coverage of AI-enabled coronary plaque analysis and quantitative coronary tomography assessment of coronary computed tomography angiography (CCTA) for Medicare beneficiaries starting in November 2024.

Medicare to Cover AI-Powered, CCTA-Based Coronary Plaque Analysis and Quantitative Coronary Topography 

The Food and Drug Administration (FDA) has granted 510(k) clearance for AngioFlow™, an artificial intelligence (AI)-enabled perfusion imaging software that may facilitate more timely assessments of perfusion status prior to endovascular procedures.

Offering color-coded perfusion mapping that identifies regions with reduced cerebral blood flow and blood volume, 

 provides results within minutes of a cone-beam computed tomography (CT) scan, according to RapidAI, the manufacturer of AngioFlow.

In addition to potential cost savings by reducing redundant imaging, RaidAI emphasized that AngioFlow’s timely assessments may reduce long-term disability risks and facilitate life-saving interventions for patients in need of acute stroke care.

“AngioFlow by RapidAI will allow physicians to assess the need for additional imaging immediately in the interventional suite. By avoiding unnecessary scans, stroke patients can receive the timely care that can be the difference between being able to walk out of a hospital to their homes versus being discharged to a skilled nursing facility,” said Abhishek Singh, MD, DABPN, DUCNS, who is affiliated with the Creighton University School of Medicine in Omaha, Nebraska.

The company added that AngioFlow has been validated in more than 10 clinical trials.

Reportedly validated in more than 10 clinical trials, the AngioFlow perfusion imaging software enables timely identification of brain regions with cerebral blood flow reduction and those with significant hypoperfusion.

FDA Clears AI-Powered Qualitative Perfusion Mapping for Cone-Beam CT

A 50-year-old patient presented for screening mammogram. Extremely dense breast tissue was noted.

), a 1-cm spiculated mass (arrow) was identified within the left breast, best demonstrated on the lateral view (

) demonstrated an 8-mm hypoechoic mass in the 10:00 position of the left breast and, in addition, ultrasound identified a 1.1-cm hypoechoic mass at the 1:00 position (

). A needle core biopsy of the 10:00 mass was performed resulting in a diagnosis of invasive ductal carcinoma. The patient was referred for breast magnetic resonance imaging (MRI) for extent of disease evaluation. Breast MRI (

) demonstrated a new enhancing mass in the contralateral breast. MRI of the ipsilateral breast (

) demonstrated two enhancing masses consistent with the two lesions identified on ultrasound. Biopsy of new lesion in the contralateral breast was recommended.

50-year-old patient presented for screening mammogram. Extremely dense breast tissue was noted. 

Image IQ: Contralateral Breast after Cancer Diagnosis

A 47-year-old female presents for her annual screening mammogram. The patient has a history of having “multiple moles,” and is otherwise healthy.

Dysplastic nevus syndrome/familial melanoma syndrome

Image IQ: 47-year-old with Multiple Moles, Screening Mammogram

An emerging deep learning (DL) model, which assesses magnetic resonance imaging (MRI) and clinical data, demonstrated a nine percent higher C-index for predicting prostate cancer (PCa) progression than two established risk calculators drawn from the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the Prostate Cancer Prevention Trial (PCPT).

For the multicenter retrospective study, recently published in 

, the researchers assessed the DL model in 1,143 patients (median age of 64) and 1,607 MRI scans. In addition to evaluation of baseline MRI exams, the DL model reviewed clinical parameters including patient age, prostate-specific antigen (PSA) level, PSA density and prostate volume, according to the study. The internal test set was comprised of 686 patients and a total of 829 MRI scan, and external validation testing was conducted in 457 patients (778 MRI scans).

The researchers found that internal testing showed a higher C-index for the DL model (68 percent) in contrast to 59 percent for the ERPC and PCPT risk calculator models for predicting the progression of low-risk PCa to clinically significant 

 (csPCa). The study authors noted no significant differences between the tools with respect to the C-index in external validation testing with the DL model being equivalent to the PCP risk calculator (56 percent) and lower than the ERSPC risk calculator model (61 percent).

However, the researchers also determined in external validation testing that the DL model was associated with a hazard ratio (HR) of 1.32 for predicting progression to csPCa in comparison to 1.24 for the ERSPC (1.24) and PCPT models (1.09).

Employing the DL scoring model, the study authors said external validation testing showed that not having follow-up imaging for two years in the DL risk group 

1 had less than 10 percent risk of missing csPCa development. The absence of follow-up imaging for three years in this patient population was associated with a 13 percent risk of having csPCa, according to the researchers.

“In our analysis, follow-up schedules based on DL risk scores would have allowed a substantial proportion of patients to forego follow-up for 3 years with 13% or lower risk of missing disease progression,” wrote lead study author Christian Roest, MSc, who is affiliated with the Department of Radiology at University Medical Center Groningen in Groningen, the Netherlands, and colleagues.

(Editor’s note: For addition content on prostate cancer imaging, click 

When assessing the DL risk scoring model to predict PCa progression to International Society of Urological Pathology (ISUP) grades of 

 3, the researchers noted the DL model had a 1.44 HR.

“This result is particularly relevant because some active surveillance protocols may allow patients who have PCa with an ISUP grade of 2 to enroll under specific conditions (eg, the absence of cribriform growth or use less strict criteria (eg, ISUP grade ≥ 3) to trigger active treatment,” added Roest and colleagues. “ … The model’s ability to estimate the risk of progression to PCa with ISUP grade 3 or higher suggests the utility of DL risk scores across diverse surveillance protocols.”

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In regard to study limitations, the authors acknowledged differences between the two institutions contributing to the cohort of reviewed cases with one facility opting to conduct prostate biopsies prior to initial MRI. They also conceded that the intended population for the PCPT risk indicator differed from that of the study cohort for the MRI-based deep learning tool for estimating risk for PCa progression.

Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.

Sheena Saleem, MBBS

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