Jeff Hall

The addition of new 3D imaging capabilities to a mobile C-arm system may enhance procedure planning, improve real-time intraoperative guidance, and significantly reduce procedure times for endovascular surgery procedures.

Endovascular surgeons who use the Zenition image-guided therapy mobile C-arm system (Philips) will now have 

advanced 3D image guidance capabilities thanks to a new partnership agreement

 between Philips and the United Kingdom-based company Cydar, which specializes in cloud-based procedure map software.

Philips says combining the Zenition system’s versatility and innovative image capture with the artificial-intelligence (AI)-enabled technology and patient-specific 3D mapping of target vasculature offered with Cydar EV Maps may lead to greater efficiency, safety, and improved outcomes. Not only does the technology bolster procedure planning, Philips notes the real-time updates of the 3D mapping can provide enhanced guidance for addressing findings encountered during endovascular procedures.

The integration of these technologies could lead to a 50 percent reduction in radiation exposure and a greater than 20 percent reduction in procedure times, according to Philips.

“With Philips’ integrated portfolio, using validated AI and cloud technologies, we can facilitate collaborative care to optimize surgical pathways,” said Karim Boussebaa, the General Manager of Image Guided Therapy Systems at Philips. “Philips has a strong global network of mobile surgery systems and recognizes that Cydar’s EV Maps solution can play a key role in further improving our integrated offering for endovascular procedures.”

Articles

Emerging technology could provide key advances in pre-op planning and intraoperative management in endovascular surgery.

New AI-Enabled 3D Mapping with C-Arm System May Improve Efficiency and Outcomes of Endovascular Procedures

In a new distribution agreement with the manufacturer UE LifeSciences, Siemens Healthineers plans to launch the iBreastExam, a handheld cancer screening modality that helps clinicians identify non-palpable breast lumps in a matter of minutes.

In addition to reportedly being a painless alternative to help alleviate patient anxiety about breast examinations, the iBreastExam may serve as an effective tool to facilitate early screening for 

, a disease that is currently expected to claim the lives of approximately 43,250 women in the U.S. in 2022. The 

launch of the iBreastExam in the United States

 will begin this month, according to Siemens Healthineers.

“We’re committed to improving breast cancer-related outcomes by recognizing unmet needs in healthcare delivery, and UE LifeSciences’ iBreastExam clearly addresses a well-defined niche in primary care,” said Niral Patel, the vice president of X-ray products for Siemens Healthineers. “With iBreastExam, we’re making women’s health more equitable and inclusive for all populations.”

, researchers said the iBreastExam and clinical breast exams can be used synergistically as triage tools to “significantly reduce the numbers of patients who need additional diagnostic imaging in resource-limited areas.” The iBreastExam had a specificity of 80.3 percent and a negative predictive value of 94 percent, according to the study.

Siemens Healthineers added that the iBreastExam provides automated digital documentation that can be linked to a patient’s electronic health record.

“An objective, quick, and painless documentation of routine breast exams informs and empowers care providers and women, especially those younger than 40, with dense breasts, or with family history,” noted Brian Englander, M.D., the chairman of the Department of Radiology at Pennsylvania Hospital.

The iBreastExam, a handheld device cleared by the FDA, reportedly enhances routine clinical breast examinations.

Emerging Breast Cancer Screening Tool to See Wider Distribution in U.S.

What kind of impact do fatigue, and experience level have in the assessment of digital breast tomosynthesis (DBT) images?

 demonstrated that experience matters when it comes to interpreting DBT images as does the time of the day when they are interpreted.

In the retrospective study involving over 97,600 mammograms

 interpreted by 18 radiologists over a nearly two-year period, researchers assessed recall rates and false-positive rates for DBT imaging and digital mammography (DM). For radiologists with five years or less of post-training experience, recall rates for the use of DBT increased 11.5 percent with every additional hour in the day but researchers did not see this for patients who had DM. The study authors also found no increase in recall rates for either DBT or DM among radiologists with more than five years of post-training experience.

However, they also acknowledged considerably more detail with DBT, noting “hundreds of images per bilateral screening DBT examination, as compared with four images per bilateral screening DM examination.”

“(Due to) the higher number of images to review, DBT may require greater cognitive resources than DM, which could exacerbate the association between fatigue and radiologist performance,” wrote Ana P. Lourenco, M.D., a professor of diagnostic imaging at the Warren Alpert Medical School of Brown University, and colleagues.

The study authors noted that other researchers had found that DBT had an 

between a 15 to 40 percent reduction in recall rates

. In the new study, the authors also found that patients in the DM group had a higher false-positive rate in comparison to those who had DBT (9.8 percent vs. 8.6 percent) and a higher overall recall rate (10.2 percent vs. 9.0 percent).

“Although DBT achieves a superior (true positive) rate, more junior radiologists appeared to compensate for their fatigue later in the day when using DBT by recalling a broader range of mammograms, more of which were (false positive) findings,” wrote Dr. Lourenco and colleagues.

The study authors noted limitations of the study, including a small sample size of radiologists as well as a lack of randomization for the imaging technology, patients, and radiologists. They also acknowledged that using the 5-year mark to distinguish experience is a rough approximation that may not account for work volume or the varied effect of experience for each person.

While digital breast tomosynthesis is reportedly more effective at detecting cancer, new research suggests a higher recall rate due to fatigue and less experience in interpreting the images. 

Study Looks at Recall Rates for Digital Breast Tomosynthesis and Digital Mammography

, researchers found that artificial intelligence (AI)-enabled assessment of combined findings from positron emission tomography (PET), computed tomography (CT) and clinical data may provide “substantial improvement” in predicting heart attack risk for people with coronary artery disease.

In the study involving 293 people with coronary artery disease, the authors noted that 22 study participants suffered a myocardial infarction during the 53-month follow-up period. Employing a machine-learning model, researchers assessed the combination of 

F-NaF) PET and quantitative plaque analysis via CT angiography along with clinical findings and compared it to these individual diagnostic measures for identifying myocardial infarction risk.

• Clinical characteristics: (c-statistic 0.64, 95% CI 0.53-0.76)

• Quantitative plaque analysis: (c-statistic 0.72, 95% CI 0.60-0.84)

F-NaF coronary uptake: (c-statistic 0.76, 95% CI 0.68-0.83)

• Combination of all measures: (c-statistic 0.85, 95% CI 0.79-0.91)

For patients with advanced coronary atherosclerosis, the study authors maintained that predicting risk for myocardial infarction doesn’t depend on cardiovascular risk scores, stenosis severity or CT calcium scoring. They said the study showed that disease activity assessment via 

F-NaF PET and analysis of plaque type and burden by coronary CT angiography are primary determinants in risk stratification of this patient population.

“Our machine-learning approach has overcome the challenges posed by collinearity of these variables and, for the first time, has demonstrated that this information is complementary and additive with the combination of both providing the most robust outcome prediction,” wrote Piotr Slomka, PhD, FACC, FASNC, FCCPM, a professor of medicine and cardiology within the Division of Artificial Intelligence in Medicine at Cedars-Sinai Medical Center in Los Angeles, and colleagues. “If confirmed in future studies, this comprehensive approach holds major promise in refining risk stratification of patients with established coronary artery disease, a population for which such prediction is currently challenging.”

Conceding a limited number of patients and myocardial infarction events in this study, the authors noted larger studies are necessary to validate the findings. They said one ongoing prospective study is assessing the aforementioned modalities for the prediction of recurrent events in patients who had a recent heart attack and multivessel disease.

New research suggests an emerging machine learning model that combines findings from advanced imaging with clinical data may improve risk stratification in people with coronary artery disease.

AI Assessment of PET/CT May Hold 'Major Promise' for Predicting Heart Attack Risk

The American Medical Association (AMA) has reportedly issued a new Category III Current Procedural Terminology (CPT) code that radiologists and other clinicians may employ for quantitative CT tissue characterization, according to Nanox and Nanox.AI.

 may provide a pathway for broader use of their artificial intelligence (AI)-enabled HealthCCSng, a cardiac imaging modality which radiologists may use adjunctively with chest computed tomography (CT) to help quantify coronary artery calcium (CAC), a key biomarker of cardiovascular disease.

While assessing CAC with chest CT alone can be a time-consuming endeavor, Nanox noted the FDA-cleared HealthCCSng facilitates efficient analysis of non-gated CT scans, quantifies the amount of CAC and subsequently provides risk stratification for patients into three categories based on the CAC detected.

“AI-enabled technologies that are built into existing radiologist workflows can help them identify at-risk patients and provide enhanced clinical insights,” said Erez Meltzer, the chief executive officer for Nanox. “We believe this (CPT code) is an important milestone that will enable radiologists and physicians in the United States to provide their patients with better care and a promising step toward realizing … the growing importance of AI-based imaging technologies in value-based care.”

Code for quantitative CT tissue characterization goes into effect on July 1.

New CPT Code May Lead to Wider Use of AI Technology for Quantifying Coronary Artery Calcium on Chest CT

Emerging research shows that magnetic resonance spectroscopic imaging (MRSI) may have advantages over morphologic magnetic resonance imaging (MRI) in identifying subtle pathology in normal-appearing white matter (NAWM) and cortical gray matter (CGM) associated with multiple sclerosis (MS).

The prospective study, recently published in 

, compared 65 patients with multiple sclerosis (MS; mean age, 34 years) to 20 healthy controls (mean age, 32 years). The use of MRSI identified a higher signal intensity of myo-inositol in normal-appearing white matter (NAWM) as well as a higher ratio of myo-inositol to total creatine (TCr) in the NAWM of the centrum semiovale in all patients with MS in comparison to healthy controls. Researchers also identified a lower ratio of N-acetylaspartate (NAA) to TCr in the NAWM and CGM of those with MS-related disabilities.

Noting that previous studies have cited elevated myo-inositol as a consistent finding in people with MS, the study authors said the use of single-voxel MRSI in these studies prevented researchers from determining whether this finding is pervasive in the NAWM. In contrast, the authors explained that the use of 7.0-T free-induction decay MRSI with 2x2 mm in-plane spatial resolution facilitated visualization of “focal and widespread pathologic manifestations” in NAWM, CGM and white matter lesions (WML).

“Our approach with an approximately 2x2 mm nominal voxel size and full in-section brain coverage not only improves the characterization of the WML, but also eliminates unwanted bias in the measurement of metabolic markers in the NAWM and, additionally, allows the visualization of this pathologic finding for the first time,” wrote Wolfgang Bogner, PhD, who is affiliated with the Department of Biomedical Imaging and Image-guided Therapy at the Medical University of Vienna in Austria, and colleagues. “ … Our results confirm that NAWM NAA/TCr is significantly reduced in participants with disability, and that axonal pathologic manifestations appear early, before the conversion into progressive MS.”

, Peter B. Barker, DPhil, a professor of radiology and oncology at the Johns Hopkins University School of Medicine, praised the study’s stepwise progression in metabolite ratios as well as the quality of the metabolite maps that demonstrated “diffuse abnormalities” in white matter regions and CGM involvement.

“It is suggested that 7.0-T (MRSI) measurements may reflect the presence of mild or early cortical atrophy in patients, which is believed to be one of the important factors governing cognitive decline and long-term outcome,” wrote Dr. Barker, who has performed research on the use of MRSI and MRI for more than 30 years. “ … It is also suggested that 7.0-T (MRSI) may have a role in the early diagnosis of MS.”

The study authors conceded limitations to the study, including the lack of assessing whether spinal cord lesions were a contributing factor to neurologic disability as well as a limited MRSI protocol of obtaining one, two-dimensional 8 mm section. They also noted that due to unknown relaxation times and water content in lesions, they did not obtain absolute quantification of metabolite concentrations.

Researchers found the use of magnetic resonance spectroscopic imaging revealed pathologic evidence of multiple sclerosis that is not evident on T1 or T2-weighted MRI. 

 

New Study Suggests MR Spectroscopy May Detect Brain Changes Linked to Disability in Multiple Sclerosis

The Food and Drug Administration (FDA) has reportedly approved new labeling that removes limits on lead tip allocation and the amount of radiofrequency power employed with MRI scans in conjunction with the Proclaim XR Spinal Cord Stimulation (SCS) System with Octrode™ leads.

 enables clinicians to place the Octrode lead tips anywhere along the spine without hampering the use of MRI scans for approved conditions, according to Abbott, the manufacturer of the Proclaim XR SCS System. The company said the new labeling also removes restrictions on the use of radiofrequency energy for patients with the Proclaim XR SCS System if the MRI device is in normal operating mode.

There may be increased efficiency and safety as well. While the FDA usually requires radiologists to pause MRIs for 30 to 60 minutes between scan cycles due to potential concerns about radiofrequency energy heating up an implant and damaging surrounding tissue, Abbott noted the expanded MRI labeling allows radiologists to wait only 30 minutes between scan cycles of 30 minutes when assessing patients who have an implanted Proclaim XR SCS System.

“The expanded labeling and reduced scan time address two major issues we face with these implantable devices. By improving the quality of imaging, as well as the experience for both the patient and the radiologist, I can now enable more of my existing and new patients to benefit from MRI scans improving their care,” explained Steven Falowski, M.D., a neurosurgeon who is affiliated with Neurosurgical Associates of Lancaster in Lancaster, Pa.

“Now more clinicians can utilize MRIs to improve quality of care among a patient population with complex needs,” added Timothy Deer, M.D., the president and chief executive officer with the Spine and Nerve Centers of the Virginias in West Virginia. “It allows clinicians to get to the source of a particular issue in less time and without fear of compromising safety or creating complications.”

Expanded MRI labeling for Abbott’s Proclaim™ XR Spinal Cord Stimulation System may facilitate increased efficiency and quality of MRI scans.

FDA Approves Expanded MRI Compatibility for Emerging Spinal Cord Stimulation Modality

Koios DS, an artificial intelligence (AI)-based software platform that reportedly improves the accuracy and efficiency of diagnosing breast and thyroid cancer, 

from the Food and Drug Administration (FDA)

, according to the software manufacturer Koios Medical.

A recent multicenter retrospective review

 involving the assessment of 900 breast lesions showed that the addition of Koios DS Breast to ultrasound imaging improved correct BI-RADS classification of sonographic breast lesions by 14 of the 15 physicians participating in the study. 

For thyroid cancer, Koios Medical reported a 14 percent increase in detection rates with its Thyroid DS platform and more than a 35 percent reduction in false positive biopsy orders. The company also noted a greater than 50 percent reduction in interpretation variability.

Built with ultrasound data from a worldwide, 48-site network, the AI-generated findings with Koios DS are directly aligned with the BI-RADS and TI-RADS rating systems from the American College of Radiology, according to Koios Medical. The company added that the software is also aligned with the American Thyroid Association’s system for tissue classification and scoring. 

“ … This novel software demonstrates that (by) using AI for decision support, physicians can make clinically meaningful shifts in performance, improving interpretation efficacy and diagnostic performance, improving sensitivity and reducing false positives,” noted Lev Barinov, VP of clinical excellence and product management at Koios Medical. 

On the reimbursement front, Koios Medical pointed to new CPT Category 3 codes from the American Medical Association that clinicians may employ to bill for use of the Koios DS software in interpreting, classifying and reporting findings from traditional ultrasound examinations.

Will this artificial intelligence technology improve diagnostic accuracy and reduce false positive biopsy orders? 

 

Emerging AI Software Gets FDA Clearance for Diagnosing Breast and Thyroid Cancer

In a recently published systematic review and meta-analysis involving 60 studies and over 10,600 patients, researchers found that contrast-enhanced mammography (CEM) provided high sensitivity and specificity in diagnosing breast cancer.

, the use of CEM with low-energy and recombined images demonstrated slightly higher sensitivity (95 percent) than the use of recombined images alone (94 percent) and 10 percent higher specificity (81 percent vs. 71 percent).

The study authors noted that a potential preference to employ CEM as opposed to contrast-enhanced MRI may stem “from its ability to correlate low-energy mammographic findings with enhancement.” They indicated this could be beneficial when it comes to assessing suspicious findings detected with mammography and supplemental breast cancer screening for patients with dense breasts. In subgroup analyses, CEM had a 95 percent pooled sensitivity and a 78 percent pooled specificity in the assessment of patients with dense breasts, and a 92 percent pooled sensitivity and an 84 percent pooled specificity in studies looking at mammography-detected suspicious findings.

Another benefit of CEM may be the screening of women who have undergone thoracic radiation therapy, according to lead author Andrea Cozzi, M.D., who is affiliated with the Department of Biomedical Sciences at Universita degli Studi di Milano in Milan, Italy, and colleagues. The study authors noted that these women have a higher risk of ductal carcinoma in situ with low neoangiogenesis, a finding that may be missed on CE-MRI but could be detected through the presence of calcifications on CEM.

“Interest in CEM stems from its potential to achieve the high sensitivity of a modality like breast MRI but without the associated limitations of MRI-limited accessibility and high cost,” wrote Manisha Bahl, M.D., M.P.H., a radiologist at Massachusetts General Hospital and Harvard Medical School, 

While CEM does not have a specific Food and Drug Administration (FDA)-approved indication for breast cancer screening, Dr. Bahl wrote that large multicenter trials are currently underway to assess the screening capabilities of CEM.

Dr. Cozzi and colleagues conceded limitations of the study, including high heterogeneity, the small number of multicenter studies in their meta-analysis and a lack of randomized studies.

A new meta-analysis examines the diagnostic benefit of this emerging complementary modality. 

Can Contrast-Enhanced Mammography Provide a Viable Option for Breast Cancer Detection?

In a recently published study, researchers found that male brain donors with autopsy-confirmed chronic traumatic encephalopathy (CTE) had more severe frontotemporal atrophy and an increased chance of having a cavum septum pellucidum (CSP) on antemortem MRIs in comparison to similarly aged men with normal cognition. The study authors also found an association between greater atrophy on MRI and increased hyperphosphorylated tau (p-tau) pathology in those who had CTE.

“The inability to accurately diagnose CTE during life has presented as a significant challenge to clinicians. It has also precluded our ability to conduct research on treatment and prevention,” noted Michael L. Alosco, Ph.D., the lead author of the 

. “Although these issues remain, findings from this study provide a significant advancement in our ability to accurately detect CTE during life, bringing hope to clinicians and patients.”

For the study, researchers reviewed antemortem MRIs in 55 deceased males with autopsy-confirmed CTE and 31 males (11 of whom were deceased) with normal cognition. Employing the McKee CTE staging scheme, the study authors found that 45 of the 55 CTE brain donors had either stage III or stage IV CTE. Fifty-two of the brain donors with CTE had played American football and two had played professional ice hockey. In comparison to those who had normal cognition, the brain donors with CTE had more severe atrophy of the frontotemporal regions as well as the medial temporal lobes (MTLs), according to Dr. Alosco, an associate professor of neurology and co-director of the Alzheimer’s Disease Center Clinical Core at the Boston University School of Medicine.

“Perhaps the most surprising part of this study was how closely the patterns of atrophy overlapped with the neuroanatomical distribution of pathology observed in CTE,” said Dr. Alosco.

Dr. Alosco pointed out that similar patterns of atrophy can be seen with Alzheimer’s disease and other neurodegenerative diseases. He also noted that some of the brain donors with CTE did have comorbid Alzheimer’s disease, prion disease and frontotemporal lobar degeneration (FTLD).

However, in subsequent sensitivity analyses to exclude comorbidities, the study authors found similar effect sizes for the frontal and anterior temporal lobes after excluding brain donors who had comorbid FTLD. When looking at MTL and after excluding comorbid Alzheimer’s disease, the researchers noted an 11.3 percent increase in the effect size estimate. They also reported 10.4 percent and 10.8 percent increases, respectively, in effect size for the superior frontal and MTL when excluding prion disease.

“The fact that the effects remained provides further evidence that the patterns of atrophy we observed were related to CTE and not these other neurodegenerative diseases,” pointed out Dr. Alosco.

If the overall study findings are validated with subsequent prospective studies, Dr. Alosco said MRI could be utilized to help differentiate CTE from other tauopathies.

“As part of any memory disorder workup for a neurodegenerative disease, brain MRI is routine in order to look at patterns of atrophy,” noted Dr. Alosco. “While there is certainly overlap, different diseases have different patterns of atrophy and when combined with other clinical data (e.g., history, neuropsychological testing), it is incredibly helpful for accurate diagnosis. This approach is currently used to support diagnoses of Alzheimer’s disease, frontotemporal dementia, and others.”

Dr. Alosco said another intriguing part of the study is the association between the tau of CTE and the atrophy on MRI.

“This study provides some of the first evidence that the tau of CTE might precipitate neurodegeneration (i.e., atrophy), however, longitudinal studies are certainly needed to clarify this,” noted Dr. Alosco.

The study authors did note limitations to the study, including restricted and variable sample sizes as well as differences in the time between antemortem MRI and death due to the severity or presence of pathology being unknown at the time of MRI.

Emerging research suggests that frontotemporal atrophy on magnetic resonance imaging may be a key finding for chronic traumatic encephalopathy (CTE). 