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Case History: 24-year-old female with pain while chewing and locking of jaw.

Jaw Pain While Chewing

Case History: 62-year-old male with proven case of monoclonal gammopathy of unknown significance with shoulder swelling, pain.

MGUS, Shoulder Pain, Swelling

Case History: 45-year-old male with history of alcoholism with history of four seizures one day earlier.

45 y/o, History of Alcoholism, Seizures

Case History: Patient presented with vision difficulty for one year, which increased in severity over last two months.

Increasing Vision Difficulty

Case History: 70-year-old male with left-sided weakness and history of myocardial infarction four years prior. 

Left Side Weakness

Case History: 42-year-old male with pain in left ear with occasional bleeding for one year. 

Ear Pain

Case History: 20-year-old male with originally painless swelling of right cheek that has become painful.

Swollen Cheek

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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Coronary CT Angiography Study Shows Link Between Major Cardiovascular Events and High Lipid Core Burden

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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

Magnetic resonance imaging (MRI) may play a significant role in determining whether patients with early-stage endometrial or ovarian cancer are appropriate candidates for fertility-sparing treatments.

In a new literature review, published by the 

 researchers discussed insights on MRI from recent guidelines disseminated by multiple European medical societies, including the European Society for Radiotherapy and the European Society for Radiotherapy and Oncology. Here are seven key takeaways from the literature review.

1. For assessment of tumor size in patients with cervical cancer, the researchers cautioned that 

 can underestimate craniocaudal size by up to 10 mm with 3 mm being the average underestimation. They recommended T2-weighted MRI for ascertaining tumor measurements and diffusion-weighted imaging (DWI) MRI for determining the extent of the tumor.

2. Axial T1-weighted pelvic MRI with a large field of view (FOV) is recommended for the exclusion of bone lesions and enlarged pelvic lymph nodes (LNs) in patients with cervical cancer and those with endometrial cancer, according to the review authors.

3. In order to facilitate comparison with high-resolution, small FOV T2-weighted MRI, the researchers recommend obtaining axial oblique, small FOV pelvic DWI in patients with cervical cancer or endometrial cancer.

(Editor's note: For additional content on MRI, click 

4. Emphasizing that parametrial invasion is a contraindication for fertility-sparing treatment in patients with cervical cancer, the review authors noted that the combination of T2-weighted MRI and DWI has an 82 percent sensitivity and 97 percent specificity for detecting parametrial invasion.

5. In order to rule out myometrial invasion in patients with endometrial cancer, the researchers point out the combination of T2-weighted MRI, DWI and dynamic contrast-enhanced (DCE) MRI offers pooled sensitivity and specificity rates of 81 and 91 percent respectively.

6. Coronal or axial abdominal T2-weighted MRI with a large FOV can exclude hydronephrosis and enlarged para-aortic LNs in patients with endometrial or cervical cancer, according to the review authors.

7. For patients with endometrial cancer, the researchers said the use of high-resolution T2-weighted MRI — including turbo-spin echo (TSE) and fast-spin echo (FSE) sequences) — can help exclude myometrial invasion, cervical stromal invasion, and ovarian involvement.

In a literature review that includes insights from recently issued guidelines from multiple European medical societies, researchers discuss the role of magnetic resonance imaging (MRI) in facilitating appropriate patient selection for fertility-sparing treatments to address early-stage endometrial and cervical cancer.

Can MRI Have an Impact with Fertility-Sparing Treatments for Endometrial and Cervical Cancers? 

New research showed that simulated contrast-enhanced MRI provided a high multiscale structural similarity index and robust inter-reader agreement with contrast-enhanced MRI in diagnosing clinically significant prostate cancer.

For the retrospective study, recently published in 

, researchers assessed a deep learning algorithm’s use of a conditional generative adversarial network (CGAN) to provide simulated contrast-enhanced MRI derived from four non-contrast multiparametric MRI (mpMRI) sequences for T1 and T2-weighted MRI, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study authors compared the simulated contrast-enhanced MRI scans to actual contrast-enhanced MRI in 567 patients (mean age of 66) with 

The researchers found that simulated contrast-enhanced MRI demonstrated an average 70 percent multiscale structural similarity index with actual contrast-enhanced 

in two sets of external validation testing. There was also a 96 percent Cohen k assessment for inter-reader agreement between three reviewing radiologists on PI-RADS scoring based on T2-weighted MRI and DWI sequences, according to the study.

“Excellent concordance of lesion conspicuity in the peripheral zone and transition zone was observed between acquired and simulated contrast-enhanced imaging, even in patients with insufficient DWI quality,” wrote lead study author Hongyan Huang, M.D., who is affiliated with the Department of Radiology at Shenzhen Nanshan People’s Hospital and Shenzhen University in Guangdong, China, and colleagues.

The study authors added that changes from initial PI-RADS 3 assessments to PI-RADS 4 scoring occurred in 10.5 percent of patients after the addition of simulated contrast-enhanced MRI scans to biparametric MRI.

“This score change may have increased the depiction of clinically significant prostate cancer; reduced unnecessary biopsies; or enabled additional targeted biopsy, which is important for management decisions in patients who have not undergone biopsy, particularly given the trend toward avoiding biopsy in patients with low-risk PI-RADS 3 scores,” emphasized Huang and colleagues.

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

, Radhouene Neji, Ph.D., and Vicky Goh, M.D., said prospective multicenter studies are necessary for further illumination of the study findings, and questioned whether the input data for generative AI models would have sufficient clinical and/or physiologic information to facilitate simulated contrast-enhanced MRI.

However, noting that contrast-enhanced MRI is not considered a primary defining sequence for PI-RADS scoring, Drs. Neji and Goh suggested that generative AI may have merit in this context.

“Omitting intravenous contrast material may reduce scanning time, may be more cost effective, and may allow more male patients access to MRI, an important consideration with the projected incidence of prostate cancer. Amid ongoing debate and with results of prospective trials yet to be published, research into generative artificial intelligence (AI) to synthesize contrast-enhanced MRI scans is timely and relevant,” noted Dr. Neji, a senior lecturer in magnetic resonance physics and computing at the School of Biomedical Engineering and Imaging Sciences at King’s College in London, and Dr. Goh, a professor and head of the Department of Cancer Imaging at the aforementioned institution.

Could MRI-Based AI Offer Better Risk Stratification for Prostate Cancer than PI-RADS?

MRI Study Suggests Deep Learning Model Offers Equivalent Detection of csPCa as Experienced Radiologists

MRI-Based AI Model Facilitates 50 Percent Reduction in False Positives for Prostate Cancer

In regard to study limitations, the study authors conceded potential patient selection bias with the retrospective nature of the research. They pointed out that bowel and smooth muscle movements may have comprised co-registration among MRI sequences and added that the requirement of T1-weighted imaging for simulated contrast enhancement may adversely affect broader application of the model.

Deep learning synthesis of contrast-enhanced MRI from non-contrast prostate MRI sequences provided an average multiscale structural similarity index of 70 percent with actual contrast-enhanced prostate MRI in external validation testing from newly published research.

Clinical

Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI?

Based on biparametric magnetic resonance imaging (bpMRI) scans, a deep learning radiomic model may provide a significantly higher area under the curve (AUC) and sensitivity rate for the prognosis of prostate cancer in comparison to PI-RADS scoring.

, researchers compared the aforementioned deep learning radiomic model to a conventional radiomic model, a clinical imaging model, PI-RADS scoring and biopsy pathology in 878 patients with prostate cancer (PCa) who underwent radical prostatectomy procedures. The training set was comprised of 345 patients with the remaining 533 patients being assessed in external validation testing, according to the study.

The researchers found that the deep learning radiomic model had an overall AUC range between 70 to 79.1 percent in external validation testing for predicting PCa progression in comparison to a range of 59.7 to 71.8 percent for the clinical imaging model and a range of 55.4 to 61.3 percent for PI-RADS scoring.

For the prognostic assessment of International Society of Urological Pathology (ISUP) grade 3-5 lesions, external validation testing revealed nearly equivalent average specificity between the deep learning radiomics model (59.7 percent), the clinical imaging model (59.9 percent) and PI-RADS scoring (58.1 percent).

(Editor's note: For related content on MRI, click 

However, for these patients, the study authors noted over a 10 percent higher sensitivity for the deep learning radiomics model (76.4 percent) in contrast to the clinical imaging model (65.7 percent) and PI-RADS scoring (62.8 percent).

“Our study showed that the radiomics model based on deep learning is promising for providing an objective and non-invasive method for evaluating the invasiveness of PCa, which is helpful for further reducing overtreatment and avoiding unnecessary biopsy and has a certain generalizability,” wrote study co-author ZiXuan Kong, M.D., who is affiliated with the Department of Radiology at the Second Affiliated Hospital of Dalian Medical University in Dalian, China, and colleagues.

The study authors also found the deep learning radiomics model showed higher AUC consistency in external validation at the three reviewing hospitals for ISUP grade 2 lesions (range of 69.6 to 72.5 percent) and ISUP grade 3 to 5 lesions (range of 71.3 to 77 percent).

“In this study, there was no statistically significant difference in the prediction of ISUP grouping of PCa lesions by the in-depth learning radiomics model among the three external hospitals, indicating that the model has strong generalizability among different hospitals, different MRI scanners, different field strengths, and different scanning parameters,” posited Kong and colleagues.

Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI

Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?

Study Emphasizes PSMA PET Staging of High-Risk, Hormone Sensitive Prostate Cancer

In regard to study limitations, the authors acknowledged a relatively small cohort size and a lack of categorization of PCa lesions based on peripheral zone or transitional zone location.

An emerging deep learning radiomics model based on biparametric MRI (bpMRI) offered a 14 to 17 percent higher AUC range than PI-RADS scoring for predicting the aggressiveness of prostate cancer, according to new research findings.

Akash Jain

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