F-DCFPyL, used with PET imaging, can detect and localize recurrent prostate cancer, according to new research results.

In a phase III clinical trial, this radiotracer was able to bind selectively to prostate-specific membrane antigen (PMSA) with high affinity and identify metastatic lesions no matter the anatomic region with high positive predictive value (PPV), further indicating that PMSA-targeted radiotracers are useful in prostate cancer imaging. 

F-DCFPyL was approved by the U.S. Food & Drug Administration in May, and the trial outcomes were presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting.

In a 14-site prospective study across the United States and Canada, researchers from Johns Hopkins University examined both the PPV and detection rate of PET/CT with the radiotracer by anatomic region –specifically prostate/prostate bed, pelvic lymph nodes, and regions outside the pelvis. For the study, they included men with rising prostate-specific antigen (PSA) levels post-local therapy and negative or equivocal conventional imaging results.

Case example: A 54-year-old man with a history of RP+LND and a subsequent PSA of 1.25 ng/mL had no evidence of disease by baseline imaging. Piflufolastat F 18 (18F-DCFPyL)- PET/CT accurately detected biochemically recurrent prostate cancer with the PSMA PET/CT scan identifying positive left (left panel) and right peri-rectal lymph nodes (right panel).

Credit: Steven Rowe, M.D., Ph.D., Johns Hopkins University

For the study, the team, led by Steven Rowe, M.D., Ph.D., associate professor of radiology and radiological science, imaged patients with 

F-DCFPyL at the start of the study and, again, after 60 days to verify suspected lesions using a composite “standard of truth” that included histopathology, correlative imaging findings, and PSA response. They, then, compared the radiotracer imaging to the “standard” to determine the PPV and detection rate.

Based on their analysis, the team determined 

F-DCFPyL-PET/CT effectively detected and pinpointed metastatic lesions with high PPV no matter the location for men who had biochemically recurrent prostate cancer and standard imaging that was uninformative.

Specifically, the median detection rates for prostate and prostate bed, lymph nodes, and extra-pelvic region were 20.2 percent, 35.1 percent, and 26.4 percent, respectively. In addition, the median PPV for the anatomic regions was 79.5 percent, 70.9 percent, and 67.4 percent, respectively. The extra-pelvic lymph node and bone PPVs compared to soft tissue regions, the team said.

With these results – and with these agents becoming more widely available -- Rowe said, patients who have newly diagnosed, recurrent, metastatic prostate cancer could soon have additional therapy options.

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Recently approved PMSA-targeted radiotracer can pinpoint biochemical recurrent prostate cancer in men with unhelpful standard imaging. 

Radiotracer Identifies Metastatic Prostate Cancer Regardless of Anatomic Region

PET imaging shows lipophilic statin use by patients who are experiencing mild cognitive decline results is significant impacts on the posterior cingulate cortex, the area of the brain that is first affected by Alzheimer’s.

In research presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, investigators from the University of California at Los Angeles (UCLA) shared evidence that metabolism in this area of the brain is significantly reduced in this patient group. In fact, they face more than double the risk of developing dementia than do similar patients not taking the medication.

While statins are the most widely used drugs in the developed world for lowering cholesterol and reducing heart attack and stroke risk, many studies have pointed to a higher risk associated with lipophilic statins because they are better able to cross the blood-brain barrier. To date, though, investigators have not been able to identify a direct link between the medication and cognitive decline.

Significant metabolic decline in the posterior cingulate cortex in lipophilic statin users after five to six years (top) compared to hydrophilic statin users and non-statin users (bottom).

Credit: Image created by Prasanna Padmanabham and Daniel Silverman, UCLA

“While some claim that statins protect users against dementia, others assert that they accelerate the development of dementia,” said Prasanna Padmanabham, lead study author and project head of statins and cognition in UCLA’s molecular and medical pharmacology student research program. “Our study aimed to clarify the relationship between statin use and subject’s long-term cognitive trajectory.”

Padmanabham’s team sought to clarify this relationship between a patient’s medication use and their long-term cognitive path. To do so, they compared 

F-FDG-PET scans that showed blood-brain barrier penetration in patients who used statins with moderate (atorvastatin) or high (simvastatin) lipophilicity to patients who either didn’t use statins or who used different ones. They also examined eight years of clinical data.

The team divided 392 patients with mild cognitive impairment into groups based on three parameters: baseline cognitive status, baseline cholesterol levels, and type of statin used.

During the eight-year timeframe, the team said, more patients in the lipophilic group progressed to dementia than did patients who were not taking statins – 24 percent and 10 percent, respectively. They saw no significant difference between patients taking other statins and those who didn’t.

Most significantly, they said, they were only able to identify a decline in posterior cingulate metabolism in patients using the lipophilic statins. Specifically, Padmanabham said, these results point to a more than double risk among lipophilic statin users for developing dementia.

Overall, the team said, their findings could point to a new use of PET imaging that could augment the knowledge base around the link between these medications and the aging brain.

“Findings from these scans could be used to inform patients’ decisions regarding which statin would be most optimal to use with respect to preservation of their cognition and ability to foundation independently,” Padmanabham said.

Patients with mild cognitive impairment who take lipophilic statins have a more than doubled risk of progressing to dementia.

PET Imaging Shows Increased Dementia Risk with Lipophilic Statins

A new PET radiotracer can effectively target a biomarker associated with abdominal aortic aneurysms (AAA), potentially predicting when they will rupture.

This research, presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, is significant because using this radiotracer can improve AAA diagnosis and provide information that can further AAA treatment development. Currently there are no treatments for this largely asymptomatic, life-threatening degenerative vascular disease.

The results of this study can go a long way in improving care for patients affected by AAA, investigators said.

Cu-DOTA-ECL1i PET/CT image of a patient with an abdominal aortic aneurysm. CTA and CT images show an aneurysmal abdominal aorta (arrow). PET and PET/CT images demonstrate specific tracer uptake within the aneurysm, with elevated localization to the aortic wall (arrow).

CREDIT: Image created by Gyu Seong Heo, Lisa Detering, Deborah Sultan, Hannah P. Luehmann, Richard Laforest, Robert J. Gropler, Yongjian Liu, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Sergio Sastriques, Batool Arif, Santiago Elizondo Benedetto, Sean English, Section of Vascular Surgery, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri; Chieh-Yu Lin, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.

“Right now, clinical diagnosis of AAA relies on anatomic measurements of AAA diameter, which is a poor marker for the prediction of rupture,” said Gyu Seong Heo, Ph.D., a post-doctoral researcher at Washington University School of Medicine in St. Louis, Mo. “Thus, there is an unmet clinical need for a novel molecular biomarker to determine the underlying processes that lead to aneurysm expansion and rupture and to serve as a therapeutic target for better management of AAA patients.”

Heo’s team identified chemokine receptor type 2 (CCR2) as a possible, novel biomarker for AAA. For their study, they developed 

Cu-DOTA-ECL1i. It has been confirmed as safe and effective, and it has been used for first-in-AAA patient imaging of CCR2.

They used the tracer in pre-clinical animal AAA rupture models to evaluate CCR2-targeted treatments and found the imaging was highly suggestive impending AAA ruptures. They also demonstrated, in a specific group of animals, CCR2 inhibitor is an effective in preventing AAA rupture.

Despite only being tested so far in animals, the radiotracer holds promise in human trials, the team said.

“Given the availability of CCR2 inhibitors for human uses, our work holds great potential to assess AAA vulnerability, screen AAA patients for CCR2-targeted treatment, and determine treatment response for optimal outcomes,” Heo said.

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Tested so far in animal models, this tracer could further the development of treatments for this life-threatening disease for which there are currently no therapies.

Novel PET Tracer Capable of Predicting Abdominal Aortic Aneurysm Rupture

Using PET imaging, as well as optical imaging, can help providers visualize the effectiveness of a new cancer treatment approach that uses Cerenkov radiation energy in a different way.

In a presentation during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, researchers from the University of Wisconsin-Madison revealed that the light from decaying radiopharmaceuticals, known as Cerenkov luminescence, can be an internal energy source that activates semi-conducting polymer nanoparticles to target and attack cancer.

Photodynamic therapy, the use of external light to activate nanomaterials for cancer treatment, isn’t new, but it is limited. Its efficacy is capped by its ability to penetrate tissue. Using Cerenkov luminescence is an effective alternative, the investigators said, because it spontaneously erupts as certain radiopharmaceuticals decay in the body, meaning it can fight cancer from the inside.

The light source itself is generally weak, but there are ways to strengthen it.

“The good news is that the light source can be amplified with semi-conducting polymers, which greatly increases its potential to target and destroy cancer cells,” said Zachary Rosenkrans, a University of Wisconsin-Madison graduate research assistant. “In our study, we aimed to determine how to best utilize radiopharmaceuticals and nanoparticles to create the ideal cancer theranostics nanosystem.”

Semiconducting polymer nanoparticles amplify and harness Cerenkov luminescence for cancer theranostics.

Credit: Image created by ZT Rosenkrans, et al. University of Wisconsin-Madison, Madison, WI.

In their study, the team discovered that using semi-conducting polymer nanoparticles that are optimized with photosensitizers dramatically intensifies Cerenkov luminescence’s ability to kill cancer cells. Using PET and optical imaging, they were able to see clear tumor uptake of the optimized nanoparticles.

Based on these results, Rosenkrans said, this approach has substantial potential as a cancer theranostics nanosystem without any tissue penetration limits.

“This work is an important step toward translating nanomaterials that are activated by light, using radiopharmaceuticals as an activation source,” Rosenkrans said. “The basic concept, using semi-conducting polymers to harness and amplify light produced from radiopharmaceuticals, is also very exciting and could have many interesting applications in the future.”

New therapy uses light from decaying radiopharmaceuticals to attack cancer cells from inside the body.

PET Imaging Reveals Efficacy of Novel Cancer Treatment

A new PET imaging technique uses the head motions that usually cause problems during brain imaging to produce scans that have the potential to detect neurological disorders, including Alzheimer’s disease, at their earliest stages.

In a poster presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, investigators from the Gordon Center for Medical Imaging in Boston showed how combining PET with an external motion tracking device can produce highly detailed brain images. It’s a process dubbed super-resolution, they said, and it could lead to faster diagnosis and treatment.

Typically, unwanted patient movements during scanning degrades image quality. But, the team, led by Yanis Chemli, MSc, Ph.D. candidate at the Gordon Center, determined focusing on and measuring those movements can actually improve resolution.

“This work shows that one can obtain PET images with a resolution that outperforms the scanner’s resolution by making use, counterintuitively perhaps, of usually undesired patient motion,” Chemli said. “Our technique not only compensates for the negative effects of head motion on PET image quality, but it also leverages the increased sampling information associated with imaging of moving targets to enhance the effective PET resolution.”

Result of the Hoffman brain phantom study. Top row: same PET slice reconstructed with A) 2mm static OSEM, B) 1mm static OSEM, C) proposed SR method and D) corresponding CT slice (note that the CT image can be treated as a high-resolution reference). Middle row: zoom on region of interest for corresponding images. Bottom row: Line profiles for corresponding data.

Credit: Image created by Y Chemli, et al., Gordon Center for Medical Imaging: Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA.

For the study, the team performed moving phantom and non-human primate experiments on a PET scanner with an external motion tracking device that continuously captured extremely precise measurements of head movements. They also completed static reference PET acquisitions without induced movements.

The researchers combined data from the PET scanner and the external tracker, and they recovered PET images that had noticeably higher resolution than what the static reference scans were able to achieve.

Although this super-resolution technique has only been tested in pre-clinical environments, work is underway to extend it to human patients. Ultimately, Chemli said, the technique could have a significant impact on brain disorders, specifically Alzheimer’s.

“Alzheimer’s disease is characterized by the presence of tangles composed of tau protein. These tangles start accumulating very early on in Alzheimer’s disease – sometimes decades before symptoms – in very small regions of the brain,” he said. “The better we can image these small structures in the brain, the earlier we may be able to diagnose and, perhaps in the future, treat Alzheimer’s disease.”

Using an external tracking device can lead to super-resolution that can potentially improve neurological disorder detection.

PET Technique Harnesses Head Movement to Improve Resolution

Using a specific radiotracer with PET imaging gives providers a non-invasive method to pinpoint and monitor pulmonary fibrosis, according to new research.

In a poster presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, researchers from the University of Wisconsin-Madison showed that by binding to activated fibroblasts in affected lungs, 

Ga-labeled fibroblast activation protein inhibitor (FAPI) enables direct imaging of the disease process with PET.

Idiopathic pulmonary fibrosis (IPF) makes breathing difficult by extensively scarring the lungs. Every year, it accounts for more than 40,000 deaths in the United States. Consequently, having a better, more specific method to diagnose affected patients could improve their overall management.

A) Axial CT images through the mouse lungs at 7 and 14 days after intratracheal administration of bleomycin or saline (as a control), demonstrating increased lung fibrosis in the bleomycin group (white arrows). (B) CT attenuation histograms in Hounsfield units (HU) after lung segmentation demonstrate increased attenuation in the lungs in the bleomycin group than the control group (p <0.05), consistent with increasing fibrosis (n=3). (C) Representative axial PET/CT fusion images at 20 and 60 min demonstrating increased FAPI uptake in the lungs of the bleomycin group (white arrows) with no significant uptake in the control group (yellow arrows). (D) Time-activity curve of lung uptake ROI analysis demonstrating higher FAPI uptake in the lungs of the bleomycin group than the control (p < 0.05), 14 days after bleomycin (n=3). (E) Ex vivo biodistribution data of lung tissue demonstrating higher radiotracer uptake in the lungs of the bleomycin group than the control (n=3). *p<0.05, **p<0.01.

Credit: Image created by CA Ferreira et al., University of Wisconsin-Madison, Madison, WI.

“CT scans can provide physicians with information on anatomic features and other effects of IPF, but not its current state of activity,” said Carolina de Aguiar Ferreira, Ph.D., a research associated at the University of Wisconsin-Madison. “We sought to identify and image a direct non-invasive biomarker for early detection, disease monitoring, and accurate assessment of treatment response.”

Fibroblast activation protein (FAP) is over-expressed in IPF, making it a good target as a potential biomarker, the team said. For their study, the induced pulmonary fibrosis into one group of mice and used another group as a control. They scanned both cohorts with the FAPI-based PET/CT 

Ga-FAPI-46 across several time points. When compared, the mice with induced pulmonary fibrosis had a much higher uptake of the radiotracer. This result allowed the investigators to successfully pinpoint and assess areas of the lungs affected by IPF.

But there is still more work to be done, they said.

Ga-FAPI-46 for the detection and monitoring of pulmonary fibrosis would make this molecular imaging tool the first technique for early, direct, and non-invasive detection of disease,” they said. “It would also provide an opportunity for molecular imaging to reduce the frequency of lung biopsies, which carry their own inherent risks.”

These findings, she added, show that functional imaging can play a vital role in evaluating the disease process.

Use enables direct imaging of disease process.

PET Radiotracer Can Detect, Assess Lung Fibrosis

Deep learning does a better job in predicting which patients will experience major adverse cardiac events than the current standard imaging protocols used to make these determinations, according to new research.

In a poster presented during the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting, researchers from Cedars-Sinai Medical Center in Los Angeles created a novel deep learning algorithm that can give patients a personalized assessment of their annualized risk for these types of events, such as heart attack of death.

For more SNMMI 2021 Annual Meeting coverage, click 

“These findings show that artificial intelligence could be incorporated in standard clinical workstations to assist physicians in accurate and fast risk assessment of patients undergoing SPECT MPI scans,” said Ananya Singh, MS, a research software engineer in the Slomka Lab at Cedars-Sinai. “This work signifies the potential advantage of incorporating artificial intelligence techniques in standard imaging protocols to assist readers with risk stratification.”

Prediction performance of DL compared to quantitative measures and Kaplan-Meier curves for quartiles of DL.

Credit: Image created by Singh et al., Cedars-Sinai Medical Center, Los Angeles, CA.

To build their deep learning network, the team pulled data from 20,401 patients included in the largest available multi-center SPECT dataset – REgistry of Fast myocardial perfusion Imaging in NExt generation SPECT (REFINE SPECT). All patients had undergone SPECT MPI. The team used their network to score each patient on their individual likelihood to have a major adverse cardiac event during the average follow-up period of 4.7 years.

According to their analysis, the network was able to pinpoint the areas of the heart that were associated with an elevated risk of these events and return a risk score in less than 1 second. They determined that the patients with the highest deep learning scores had a 9.7-percent annual major cardiac event rate – a 10.2-fold increased risk over patients with the lowest scores.

Using advanced artificial intelligence can give patients a personalized assessment of their risk for significant events, such as heart attack or death.

SPECT + Deep Learning = Accurate Major Cardiac Event Prediction

With so many artificial intelligence (AI) products flooding the imaging industry, the radiology space has taken on a gold rush feel. Vendors are in a frenzied battle to position themselves as the leader with the best tools available – but, there’s still a lack of over-arching standardization on how to implement and integrate these products.

Society for Imaging Informatics in Medicine 

(SIIM) 2021 Virtual Annual Meeting, Florent Saint-Clair, executive vice president at Dicom Systems, outlined four major challenges facilities must address before they can successfully adopt AI algorithms and integrate enterprise imaging solutions.

“We can’t put our heads in the sand as the use of AI is going to bet more and more intensive,” he said. “This ecosystem is going to make sure the gold rush becomes a very well-structured industry.”

To reach that goal, he suggested you consider these obstacles.

Challenge No. 1: Insufficient Use of Industry Standards

In many instances, AI developers have developed their algorithms using basic JPG images. Products developed like this cannot take full advantage of the metadata that images contain. When you’re talking with AI developers, make sure their algorithm has been trained with and makes full use of DICOM data, he said, and clarify if they use DICOM, HL7, or DICOM Web, etc. In addition, make sure your workflow isn’t going to create an expectation that prior images will be pulled in with the primary image.

Challenge No. 2: To Cloud or Not to Cloud

Technology can be made available to you in a variety of ways – does this vendor offer it strictly in the cloud, such as Google, Amazon, or Azure, or can it also be deployed on premises? If data cannot fit in Docker in your own server, you will need to address the need for encrypted communication that can move the images safely, as well as data de-identification.

Challenge No. 3: Limited Workflow Knowledge

DICOM, HL7, and PACS workflow have been around for quite a while, but using them involves jargon that is unfamiliar to many AI developers. Before selecting a tool, check to see the algorithm has been developed with workflow in mind – not just the core technology that is part of the machine learning itself, he said.

You should also be aware that not everyone is 100-percent comfortable with using DICOM Web yet. Find out where your vendor stands and whether you will need to plan for additional hurdles when trying to communicate with them.

Challenge No. 4: Inconsistent Results Delivery

This is, perhaps, the biggest, most important challenge surrounding AI adoption and enterprise imaging integration, he said. You must be sure your data can be delivered to the right place in the right format. For example, results that should come in as a DICOM overlay can come through as a blob on top of the image if the vendor is not conforming to the right industry standards. You can also run into HL7 problems if the proper HL7 connection isn’t in place. If the system is set up correctly, he said, results can be shared seamlessly.

“Every piece of the workflow has different things that the AI vendor needs to think about before they develop their AI,” Saint-Clair said. “They need to know what to do at what point in the workflow to add value without having to re-invent workflow.”

Ultimately, he said, addressing these four challenges will help you identify your needs and what you should pursue from a vendor.

SIIM

What you need to address for successful artificial intelligence adoption and enterprise imaging integration.

Four Challenges of the AI Gold Rush

Wearing a surgical mask while using speech recognition to complete a radiology report does lead to a higher dictation error rate – but, don’t worry. Most mistakes are minor are unlikely to negatively impact care.

Masks have become a necessary and ubiquitous part of healthcare throughout the COVID-19 pandemic, and some in the industry have worried that wearing a mask while using speech-recognition software could open the door for unintended errors.

(SIIM) 2021 Virtual Annual Meeting, Abiola Femi-Abodunde, M.D., a diagnostic radiology resident from the University of North Carolina at Chapel Hill School of Medicine, revealed that masks do cause a 25-percent increase is dictation errors, but they’re small.

“There is a near-significant increase in the rate of dictation errors in unedited radiologist reports created with speech-recognition software, a difference which may be accentuated in some groups of radiologists,” she said. “However, most errors are minor single-word errors and are unlikely to result in a medically relevant misunderstanding.”

For this study, the team created 40 model radiology reports that simulated radiographic/fluoroscopic, ultrasound, CT, and MRI imaging reports that are typically captured in academic medical centers. Six radiologists, who were randomly assigned to masked and unmasked groups, used speech-recognition software to dictate those reports. Their versions were, then, compared to the models, and the team classified any errors by type and severity.

According to their analysis, the overall dictation error rate was 25-percent higher when radiologists wore masks. Specifically, masked providers made 27.1 +/- 2.2 errors per 1,000 words compared with the 21.7 +/- 4.9 errors per 1,000 words from unmasked providers. But, after excluding one trainee with an accented speech pattern, the overall error rate fell to a 19-percent increase while masked – 20.1 +/- 2.2 errors per 1,000 words during masking and 16.9 +/- 1.9 errors per 1,000 words unmasked. Of the unmasked errors, she said, 58 percent were minor, as were 56 percent of those occurring while masked.

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Wearing a mask does cause an increase in report errors with speech-recognition software, but the mistakes are small and unlikely to cause a negative impact.

The Mask Effect: More, but Minor, Radiology Report Dictation Errors

Identifying patients who have osteoporosis can be easier on abdominal CT scans with automated methods that have improved generalizability.

It is well known that measuring the Hounsfield unit (HU) in the L1 vertebral body (VB) is an opportunistic way to screen patients for osteoporosis. But, the HU thresholds may not generalize well between different groups based on population and imaging protocol differences.

To see whether it is possible to augment opportunistic osteoporosis screening, Steven Rothenberg, M.D., assistant professor of diagnostic radiology at the University of Maryland School of Medicine, compared two deep learning model approaches to an external institution. His team first applied previously established thresholds to automatically extracted L1 HU, and they also trained a locally fine-tuned model using automatically extracted HUs, bone mineral densities (BMD), and demographic variables.

Rothenberg presented the team’s results in a poster during the 

For the study, the team used a deep learning model that had been trained on images from one institution to automatically segment lumbar VBs on abdominal CTs and to extract trabecular HUs from each body in order to assess the external dataset from their local site.

They gathered 200 consecutive abdominal CTs from their hospital that had undergone DEXA examination within six months over a two-year timeframe. They, first, applied L1 VB HU cut-offs to diagnose potential osteoporosis, and, then, they used age and sex data alongside automatically-extracted L1 and L2 HU and BMD to train both decision tree and random forest models for osteoporosis diagnosis.

Based on their analysis, the locally fine-tuned random forest and decision tree models had average F1 scores of 0.84 and 0.8, respectively. These results outperformed the 0.54 and 0.46 scores achieved by L1 HU thresholds of 80 and 120, respectively. In addition, they team determined the average sensitivity for the fine-tuned models were greater than 80 percent – far outpacing the 40 percent and 57 percent achieved by the two HU thresholds.

Overall, the team said, their results indicate that local models that are fine-tuned on demographic variables and automatically-extracted VB HU and BMD data do result in improved generalization for opportunistic osteoporosis screening.

Augmenting generalizability of automated methods with local fine-tuning can help providers diagnosis osteoporosis with these scans.

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