Kayt Sukel

AI Risk Score Outperforms Mammographic Density for Breast Cancer Prediction

Today, radiologists rely on mammographic density assessments, in conjunction with questionnaires and other clinical information, to create risk models for breast cancer. Researchers from the Karolinska Institute have now published a study in 

 that suggests a deep learning artificial intelligence (AI) model, trained on mammographic images, can provide a more accurate way to predict who will go on to develop the disease.

To compare traditional methods of assessing risk with a deep learning network, the study authors evaluated 2,283 women, 278 of which had been diagnosed with breast cancer, in a retrospective study. The network was developed using images from cases diagnosed over a four-year period, using the earliest available mammography data for each woman. The model produced a deep learning risk score, as well as dense area metrics and percentage density. When the model was, then, tested using mammographic images from both women who had breast cancer and healthy controls, they discovered:

• Age at mammography, dense area, and percentage density were higher in women who were diagnosed with breast cancer;
	• The odds ratios and areas under the receiver operating characteristic curve were higher for age-adjusted DL-risk scores;
	• and the use of the model resulted in few false-negative results, particularly in more aggressive forms of the disease.

Taken together, the authors concluded that, compared with density-based models of risk assessment, the use of AI algorithms, deep learning networks in particular, may help clinicians better predict which women may be at highest risk for developing breast cancer in the future.

CT Radiomic Features of Pancreas Can Aid in Diagnosing Abdominal Pain

, demonstrates that quantitative radiomic features of the pancreas in CT scans can help radiologists differentiate which patients have functional abdominal pain, recurrent acute pancreatitis, or chronic pancreatitis.

When a patient presents in the emergency room with strong abdominal pain, it can be difficult to assess what the clinical issue may be. Traditionally, when the patient says the pain is recurrent, and pancreatic enzymes are at elevated levels, doctors may diagnose those patients with recurrent acute pancreatitis, regardless of any imaging studies being done.

Researchers from the Johns Hopkins University, however, wondered if radiologic features, present on contrast-enhanced CT scans, could help physicians provide more accurate diagnoses. To test the idea, the study authors did a retrospective analysis of 56 patients who had been seen in the pancreatitis clinic with recurrent acute pancreatitis, functional abdominal pain, or chronic pancreatitis. After outlining the pancreas on images, the authors were able to extract 54 distinct radiomic features to compare between the patient groups. They also relied on a one vs. one Isomap and Support Vecture Machine (IsoSVM) classifier to help sort the groups based on radiomic features.

Careful analysis revealed that 11 radiomic features were significantly different between the three patient groups – and using those features, the ISOSVM classifier was able to predict the appropriate diagnosis with an 82.1 percent accuracy rate. The results led the authors to conclude that such radiomic features can help radiologists provide more accurate diagnoses of the cause of abdominal pain with CT scans in the future.

Combined Intelligence: Radiologists and AI Improve Pneumonia Diagnosis

 suggests the combination of a radiologist and an artificial intelligence (AI) platform can better diagnose pneumonia from chest radiographs.

Too often, the rise of AI is framed as a doctor vs. machine type of debate. Certainly, most studies published, to date, have directly compared the performance of seasoned radiologists with these deep learning algorithms. Yet, new studies are evaluating ways to measure how AI systems may augment radiologists’ abilities – and prove to be more accurate and efficient than either doctor or algorithm alone.

Researchers from Stanford University and Duke University developed a “human in the loop” method of AI design, which uses a combination of automated detection and human checks to identify pneumonia on standard chest X-rays. When they placed this AI system on Swarm, a platform that allows multiple radiologists to work together, the accuracy of their diagnoses significantly increased.

The researchers recruited 13 experienced radiologists, split them into two groups, and had them estimate the likelihood of pneumonia on a data set of 50 chest radiographs. They did so alone and, then, again using the Swarm platform with embedded AI. In addition, the researchers utilized two deep-learning models, CheXNet and CheXMax, to review the images. When the researchers compared the radiologists alone, the AI alone, and then a combination of real-time Swarm platform, the researchers discovered that the combination of AI and human experts provided a “symbiotic” effect, outperforming any one method used in isolation. The study authors concluded that finding ways to use AI to augment, rather than replace, the abilities of radiologic experts will help improve diagnostics in the future.

The practice of medicine is fraught with ethical dilemma, including end-of-life decisions and appropriate doctor-patient interactions. While radiologists, historically, may not be involved in these kinds of situations, there are still many ethical issues to consider in this specialty. Yet, past studies have found that many radiology residency programs do not offer ethical education or seminars for trainees. Now, researchers from Penn State Health Milton S. Hershey Medical Center, Johns Hopkins Medical Institutions, University of Pennsylvania, and University of Washington recently published the results of a questionnaire regarding common perceptions and experiences regarding ethical concerns in the 

The study authors recruited 424 radiologists and trainees through email invitations, social media postings, and requests for participation on radiology-related websites and web blogs. Fifty-six percent of those respondents stated they had witnessed an ethical dilemma, generally regarding how errors were handled. Trainees were more likely to report any mistakes, either on their part or on the part of colleagues, to the patient than more established radiologists. When it came to negligence, however, while 81 percent stated they would report a negligent act to a supervisor, practicing radiologists were more likely to say they would do so than those still in training.

The authors concluded that there are many common ethical dilemmas that radiologists face and that these dilemmas should be addressed in some manner during residency and fellowship training to provide a more “uniform” approach to managing them when encountered.

Moving Closer to Personalized Medicine in Radiology

Many cancer patients are prescribed immunotherapy to help them combat the disease. Yet, it can be difficult for radiologists to select the appropriate therapy, and then, later, to determine whether that therapy is working without invasive tissue sampling or repeatedly taking blood samples. Now, a new technique called 89Zr-immuno-PET, a whole-body molecular imaging technique, allows clinicians a way to quantify the uptake of antibodies in patients’ tissue so they can better predict the effectiveness of a particular therapy. The results were published in the 

Researchers from the Cancer Center in the Netherlands used the technique on 36 cancer patients who were injected with 

Zr-labeled antibodies. The technique allowed them to not only visualize the tumors throughout the body but also to measure non-specific uptake of those antibodies in both normal patient tissue and tumors, including in the kidney, liver, lung, and spleen. This, the researchers argued, can help clinicians better understand what therapies will be more effective in treating cancers, as well as understand which drugs may lead to toxic effects on the body. The study authors plan to look at uptake as a function of time in a future study.

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The latest roundup of radiology news and studies.

AI Risk Score Predicts Breast Cancer Risk Better Than Mammographic Density

How do Radiology Trainees View the Rise of AI?

Over the past year, dozens of studies have demonstrated the value of artificial intelligence (AI) across the field of radiology. Yet, in a recent survey published the 

, experienced radiologists, surgeons, and medical students may view those advances in differing light.

Researchers from Switzerland’s University of Bern recruited 170 participants-a mix of 59 radiologists, 56 surgeons, and 55 medical students- to complete an online survey regarding the future of radiology, including how AI may affect future career choices and work environments.

Perhaps unsurprisingly, most participants, regardless of position, agreed that AI had value as a support system for radiologists and would have the power to make their workflows more accurate and efficient. Yet, there were wide concerns about its adoption. For example, survey responses suggested participants saw AI as a potential threat to careers in the field. When questioned about perceived “turf losses” to other specialties and disciplines due to advances in AI, radiologists agreed it was a concern. And more than one-quarter of the students who stated they were looking outside radiology for their careers cited AI as being one of the reasons.

While the researchers admit it is difficult to generalize the findings due to the small, specific sample, they concluded that some medical trainees may choose careers outside of radiology. As such, they recommend that better education regarding AI and how it might affect the field would be in order.

Tracking the progression of multiple sclerosis, a debilitating autoimmune condition with symptoms including muscle weakness, balance issues, and cognitive problems, is a challenge for clinicians. The disease has traditionally been characterized by small demyelinating lesions in the brain’s white matter. Yet, recent work suggests small lesions in the gray matter precede the demyelination-and the ability to follow the development of those gray matter lesions over time can provide important prognostic information about related neurological disability.

Now, new research from Brigham & Women’s Hospital suggests 7T MRI offers an easier way for clinicians to monitor disease progression for the relapsing-remitting form the disease by focusing on leptomeningeal enhancement (LME). The study was published in the 

Previous work suggested that 7T MRI, with nearly twice the magnetic field strength of conventional 3T scanners, offers radiologists the ability to better visualize MS lesions and track their evolution over time. Because MS is an autoimmune disorder, some have suggested that inflammation of the meninges may play a role in the development of the disease’s hallmark gray matter lesions. It was then proposed that LME, a radiographic finding which shows an enhanced image of the meninges and spinal cord, might provide a diagnostic biomarker of inflammation that can help determine when a case of relapsing-remitting MS (RRMS) progresses to secondary progressive MS (SPMS).

To test the idea, the researchers used 7T MRI to scan the brains of 30 individuals diagnosed with RRMS, as well as 15 healthy matched controls. They observed:

20 of the 30 RRMS patients showed signs of LME, making it significantly more common than the 1 in 15 LME seen in controls;

RMMS patients with LME typically had four to five times the number of lesions in cortex and in the thalamus than those without;

LME was linked to higher incidence of gray matter lesions.

Based on those findings, the study authors concluded that cerebral LME is common in RMMS when imaged using 7T MRI-and is linked to increased gray matter injury. This suggests that cerebrospinal fluid-related inflammation may lead to MS’ gray matter lesions-and could provide a marker to help clinicians better track MS disease progression in the future.

PET Imaging Helps Distinguish TBI-Related Ischemia

 suggests oxygen positron emission tomography (PET) can detect ischemia within the first 24 hours after a traumatic brain injury (TBI).

After a TBI, the brain undergoes a cytotoxic cascade of events. There is the trauma itself to manage, as well as several secondary insults including swelling, ischemia, and intracranial hypertension. Unfortunately, ischemia, a reduction in blood supply to the brain’s tissues, is difficult to visualize using conventional imaging methods-but its presence can significantly change the prognosis after injury.

Researchers from the University of Cambridge tested the utility of a special type of PET, 

oxygen PET, to see if it could better characterize cerebral physiology after TBI. They looked at the resulting images from 68 patients with TBI who required intracranial pressure monitoring as well as 27 control participants. They discovered:

Patients with TBI showed lesser global cerebral brain flow (CBF) and cerebral oxygen metabolism (CMRO

) measures than controls, indicating classical ischemia;

Ischemic brain volume was elevated in TBI patients even in the absence of intracranial hypertension and was at its highest within the first 24 hours after injury;

ratios were higher in TBI patients than in controls, suggesting abnormal flow-metabolism and vascular reactivity;

Taken together, the study authors argue that 

oxygen PET may be a tool to help clinicians better detect ischemia in patients immediately after TBI, given that methods like jugular or brain tissue oximetry offer inconsistent results.

Recently, there have been a deluge of studies suggesting artificial intelligence (AI) algorithms can help radiologists better detect cancer or other medical issues in a variety of imaging study types. A new analysis published in 

 suggests it may offer as much value in helping to triage patients, especially in areas where radiologists may be lacking.

The author of the analysis argued that AI tools could help prioritize which images should be placed at the top of a worklist for review-indicating which reads may require more time or a consult. Furthermore, in areas where there may be a shortage of radiologists, she said such algorithms might help determine which cases should be referred to a different hospital or health system for appropriate care. While there would be challenges in designing algorithms that could minimize false-positives or negatives, the author concluded that it would be valuable for AI developers to design and test such algorithms and then pilot such platforms in randomized clinical trials to prove their efficacy.

New Imaging Techniques Offers View into Brain’s “Glymphatic” System

A novel imaging technique allowed scientists to visualize how the brain rides itself of waste during slow-wave sleep. The research was published in 

For centuries, clinicians and scientists wondered why the brain was not connected to the lymph nodes, the body’s waste disposal system. Over the past few years, studies in animal models suggested that, during sleep, specialized vessels within the brain used cerebrospinal fluid (CSF) to remove waste and toxins. The discovering scientists named it the “glymphatic” system, as it used glial cells and the brain’s vasculature to move the CSF through the brain. However, despite this exciting finding, conventional imaging techniques did not allow scientists to visualize the same process in humans.

Given that the build-up of excess proteins is associated with neurodegenerative conditions like Alzheimer’s disease, being able to visualize how a healthy brain manages waste is of great interest. And now, researchers from Boston University, Harvard University, Massachusetts General Hospital, and Beth Israel Deaconess Medical Center, have shown that they can do just that.

The researchers recruited 13 individuals to fall asleep inside an MRI scanner. Using accelerated neuroimaging techniques coupled with the brain waves collected from from a conventional electroencephalogram (EEG) cap, the researchers were able to capture a pattern of neural slow waves and hemodynamic oscillations, which, together, allow CSF to flow throughout the brain and clear the brain of debris.

The researchers concluded that CSF dynamics are associated with neural and hemodynamic rhythms and this visualizing method offers a new window for studying how such processes go awry in disease states.

What Do Trainees Think of AI?

AI Offers “Expert-Level” Detection of Brain Hemorrhage 

Acute intracranial hemorrhage (ICH), sometimes referred to as a “brain bleed,” shares symptoms with several other neurological conditions. Today, emergency departments rely on CT scans to detect this life-threatening condition-and even the most experienced radiologists can sometimes miss the subtle signs of the condition on such lower resolution images. Now, researchers from the University of California, San Francisco and the University of California, Berkeley have demonstrated that a deep learning artificial intelligence (AI) algorithm can provide “expert-level” detection of brain hemorrhage in a new study published in the 

Proceedings of the National Academy of Sciences

not only performing at the same standard as expert radiologists but finding tiny brain bleeds that those experts overlooked.

The researchers used a single-stage, end-to-end, fully convolutional deep learning neural network in order to help identify what are usually very small abnormalities that must been detected on an image known for poor soft tissue contrast and low signal-to-noise issues. They trained the algorithm on a data set of over 4,000 CT exams where ICH abnormalities were manually highlighted at the pixel level.

While the authors said this data set was small, they argue that using this pixel-level supervision approach allowed for the kind of joint classification and segmentation to allow the network to better detect potential issues on the testing data. In addition, the authors used a technique called PatchFCN, where the researchers contextualized the training images with those that came both immediately before and after each one in the stack.

When the researchers compared the algorithm’s performance with four experienced, American Board of Radiology-certified radiologists on a test set of 200 head CT exams, they found that the algorithm was able to successfully detect ICH, achieving 100% sensitivity at specificity levels approaching 90%. Furthermore, the algorithm also identified some abnormalities missed by the expert reviewers – five positive cases were judged as negative by two of the four radiologists.

The researchers concluded that their algorithm could be used to augment radiologists’ abilities to detect ICH in the emergency department and plan to continue testing it in follow-on studies.

Radiology Organizations Push Ethics Discussions Regarding AI

In a show of solidarity, the American College of Radiology (ACR), European Society of Radiology (ESR), Radiological Society of North America (RSNA), Society for Imaging Informatics in Medicine (SIIM), European Society of Medical Imaging Informatics (EuSoMII), Canadian Association of Radiologists (CAR) and American Association of Physicists in Medicine (AAPM) have come together to publish a guidance statement to provide appropriate ethical scaffolding regarding the continued development of AI applications in radiology. The statement was simultaneously published in four leading radiology journals, including the 

Journal of the American College of Radiology

Canadian Association of Radiologists Journal.

The statement authors highlighted three particular areas of ethical concern as the field moves forward, including the generation, sharing, and use of data that fuels such algorithms; the types of algorithms used, which can often have a “black box” deep learning aspect; and how the different platforms created with those data and algorithms will actually be utilized in radiologic practice. They argue that AI currently lacks appropriate standards to govern its development and use in the field and it is up to radiologists themselves to help provide the appropriate guidance.

The statement authors hope that the statement will help radiologists become more informed about both the promise and the potential perils of AI use-and where, how, and when they might get involved to help shape policy, practice, and clinical standards regarding its use in the future.

A Silent MRI for Pediatric Myelination Measures

MRI has become a radiologic mainstay, helping to diagnose and track the progression of a number of medical conditions. However, it is quite loud and uncomfortable even for mature patients. Pediatric patients, especially infants, are usually sedated before a scan to help tolerate the noise, which can be as loud as 130 decibels of gradient clicks, clanks, and whirs. But despite that measure, many patients wake during the procedure-and move-which can skew and invalidate the results.

A new study from China’s Yangzhou University suggests a technique called “silent” MRI, which significantly reduces the noise of the scanning platform, offers clinicians a better option for child patients to measure myelination. The study was published in 

Silent MRI technology is specifically designed to reduce MRI scanning noise to normal background noise levels. However, studies suggest the methods used to muffle the noise can result in a longer read-out time and result in blurry images. When it comes to diffusion-weighted imaging, there is even less known.

As such, the researchers at Yangzhou University decided to compare the image quality for measuring myelination in pediatric patients, between the ages of 3 to 36 months, between conventional and silent MRI. The researchers discovered:

The success rate of silent MRI was higher than conventional methods, with 52 more children completing the silent MRI session;

The acoustic noise, as measured by a decibel meter, was significantly lower in the silent MRI;

The resulting images from silent MRI showed a decreased signal-to-noise ratio but an increased contrast-to-noise ratio;

Silent MRI showed higher lesion visibility and gray-white differentiation than conventional methods;

Experts rated that overall diagnostic usefulness of silent MRI was higher.

The researchers concluded that silent MRI is appropriate for pediatric diffusion-weighted imaging studies, as ambient noise levels are reduced, and the images are more diagnostically useful than conventional MRI techniques.

New PET Tracer Can Help Detect Cancer, Lung Disease

With the right “tracer,” or binding peptide, PET imaging remains an important diagnostic tool for a variety of different cancers and medical conditions. A new study in 

 highlights the development of a new tracer, a cystine knot peptide that selectively recognizes integrin Î±Î½Î²

, a protein receptor known to be overexpressed in number of medical conditions including: oral squamous cell carcinoma, pancreatic ductal adenocarcinomas (PDAC), intestinal gastric carcinomas, ovarian cancer, and stage III basal cell carcinoma, as well as a marker of metastasis in a variety of other cancer types. Furthermore, integrin Î±Î½Î²

is also known to play a role in the development of idiopathic pulmonary fibrosis (IPF).

Researchers from Stanford University engineered the new peptide to better identify pancreatic cancer in PET imaging studies. Their work quickly showed, however, that it could also detect other types of cancers, as well as IPF. To best demonstrate its effectiveness, the group conducted a small clinical trial to compare the use of their new peptide in both healthy individuals and patients who had been diagnosed with cancer of IPF. While it was a small trial, they were able to evaluate the tracer’s safety and pharmacokinetics in the healthy volunteers, as well as its ability to visualize multiple cancer types in study participants who had already been diagnosed with disease.

The researchers plan to do further, extended testing to prove the efficacy of the tracer but they believe it such cystine knot tracers can have great clinical utility in the future, given that so many conditions show an overexpression of the integrin Î±

AI-Radiologist Team More Accurate Than Either Alone

As more studies show that AI algorithms can match (or sometimes even surpass) the accuracy of trained radiologists on a variety of conditions, many have warned that deep learning models may replace radiology readers. However, a new study published in 

 suggests that having an experienced radiologist whose reads can be enhanced by AI techniques may be the best option for identifying breast cancer screenings.

Researchers at New York University, hailing both from the NYU School of Medicine and the NYU Center for Data Science, developed a deep convolutional neural network program to screen mammography images for breast cancer. The system was trained and evaluated on a data set of more than 1,000,000 images and could accurately detect cancer at a rating of approximately 89.5%.

When they compared the AI program to a group of 14 experienced radiologists, they showed that the model was as accurate as the doctors, which falls in line with many of the AI vs. radiologist studies currently flooding the literature. However, beyond validating the model, the researchers wanted to see the benefits of a hybrid reading model, where the program could average the probability of a malignancy predicted by the radiologist with the prediction from the neural network. When both the AI and the radiologist worked together, the accuracy of reads increased to 90%-suggesting medicine may benefit from pairing experienced readers with proven AI tools.

The latest studies and news, from AI research to a new tracer.

Artificial Intelligence: The Ethics and Abilities

As radiology sees more types of artificial intelligence (AI) algorithms come online to help with diagnosis, there has been a looming fear that such programs could be used as cyberweapons, making cancerous lesions appear in imaging studies where none exist. Recent research published in the 

, however, suggests that radiologists still have the upper hand and can distinguish real lesions from AI-generated ones.

Researchers from Switzerland’s University Hospital ZÃ¼rich used a special type of AI program called a generative adversarial network (GAN) to see if they could fool radiologists.  The GAN algorithm consists of two distinct networks: a generator network that can add a false lesion to different images and a discriminator network that is programmed to detect those malicious additions. The two networks act in competition, trying to learn from the other in order to improve their own performance. In theory, with enough time and a large sample size of images, the generator should be able to fool the discriminator. But what the researchers did not know was whether the generator could also fool trained radiologists.

To test the idea, the researchers trained a cycle-consistent GAN model (CycleGAN) on 860 mammographic images from more than 300 patients, and then tested the model using images of 302 images showing cancerous lesions as well as 590 normal scans. The researchers then had three radiologists read both original images, as well as those that had been modified by the GAN, in both a lower and higher resolution, and then had them rate whether a suspicious lesion was present as well as their opinion regarding whether that lesion was a manipulated image using 1-to-5 scales.

The study authors discovered that, at a low resolution, the radiologists had difficulty distinguishing between original and modified images. At the higher resolution of 512 x 408 pixels, however, the radiologists not only showed a significantly lower detection rate on the modified images, but they also could more easily identify that they had been modified. The researchers concluded that CycleGAN can be used to insert suspicious features into existing images but is not yet a threat as a cyberweapon because of technical limitations.

Using AI to better detect vertebral fractures

Small vertebral fractures, which can be common in diseases like osteoporosis, are often missed by traditional imaging techniques. New research, published in 

, suggests a deep learning algorithm can assist with detection and diagnosis, helping clinicians identify these small breaks before they become a more significant medical issue.

Researchers from the University of Manitoba developed an AI algorithm called a convolutional neural network (CNN) to help identify these small fractures. The group trained and tested the CNN on more than 12,000 dual x-ray absorptiometry images to assess whether a fracture was present. They learned that the CNN was able to correctly detect vertebral fractures under the receiving operating characteristic curve of 0.94, with a sensitivity of 87.4% and a specificity of 88.4%. When compared to expert readers, with at least 10 years of experience in reading such images, the CNN had an agreement rate of 0.76, leading the researchers to concluded that such algorithms could augment radiologist’s abilities to detect vertebral fractures, helping to increase accuracy of diagnosis and improve patient outcomes in the future.

A new study suggests selective mammography screening for men at high-risk for the disease can result in earlier detection-and more lives saved. The research was published in 

Mammography has proved itself to be a valuable tool in detecting breast cancer in women. As incidence rates of breast cancer in males is rising, researchers from New York University Langone Medical Center, New York University School of Medicine, and New York University’s Center for Advanced Imaging Innovation and Research wondered if it would be as effective in identifying breast cancer in males as well. To date, there are no formal screening guidelines or recommendations for men, even those who have a family history of the disease or specific genetic mutations that put them at higher risk.

In a retrospective study, the researchers reviewed male breast imaging studies, as well as the disease outcomes, in nearly 2000 men, between the ages of 18 and 96 years, with a personal or family history of breast cancer or associated genetic mutations.  That review revealed that mammography helped to detect 2,304 breast lesions in those patients.  Of those, only 149 were biopsied, but 27.5% were shown to be malignant. Mammography screening yielded a cancer detection rate of approximately 18 per 1000 examinations. This is in stark contrast to women, where the average detection rate stands at approximately 3-5 cases per 1000 examinations in average risk women. Furthermore, mammography screening in men led to earlier detection of cancer, before the disease had spread, improving the chance of survival.

In addition, the researchers found the following:

A mammographic screening sensitivity of 100% (95% CI: 50%, 100%);

A mammographic screening specificity of 95% (95% CI: 93.1%, 98%);

And a mammographic screening positive predictive value of biopsy at 50% (95% CI: 22.2%, 77.8%).

Taken together, the researchers concluded that there may well be benefit in screening men who meet high-risk criteria for developing breast cancer-and that more research should be undertaken to develop specific recommendations for doing so.

If you’ve taken a CPR course, you’ve likely heard about the F.A.S.T. mnemonic. The idea is simple: if you see a person with face drooping (F), arm weakness (A), and speech difficulties (S), it’s time (T) to call emergency services as they are likely experiencing a stroke. Yet, not all strokes follow such a particular pattern of symptoms. Some patients may just experience numbness or dizziness, likely because it is only a minor stroke or transient ischemic attack (TIA), which may lead physicians to pursue an alternate diagnosis-and perhaps miss the window of opportunity for early treatment or identify the patient as being at higher risk for a later stroke.

, however, suggests that emergency physicians may be able to rely on MRI to help them better identify minor stroke and TIAs.  The researchers wanted to establish the frequency of acute infarct detected by diffusion-weighted MRI scans. Across multiple hospital sites, 1,028 patients, 522 females and 506 males, with minor symptoms including nonmotor or nonspeech minor focal neurologic events were given a thorough neurological assessment as well as an MRI within 8 days of symptom onset. The researchers discovered:

MRI results demonstrated that 139 patients (13.5%) had experienced an acute stroke, much higher than expected in such a low-risk population;

The MRI results led physicians to revise their final diagnoses in 30% of patients;

A DWI-positive MRI was associated with a higher risk of recurrent stroke one year later;

Multivariate modeling showed that older age, male sex, motor or speech symptoms, ongoing symptoms at the time of assessment, and abnormal results for the initial neurological examination were also associated with MRI evidence of stroke.

The researchers concluded that MRI scans can offer predictive value in determining not only whether a person with unclear symptoms has experienced a stroke, but also if they are at higher risk for having one later in time.  As such, it should be considered when clinical assessment alone cannot reliably confirm or deny an ischemic episode.

CT may help detect peripheral prostate cancer

Screening for prostate adenocarcinoma in healthy men remains a controversial subject. To date, clinicians rely on digital rectal exams or a prostate-specific antigen test to identify early stage cancer. But a new study, published in the 

, suggests that there may be diagnostic value in using CT scanning to detect cancer in the peripheral zone of the prostate.

Researchers from St. Michael’s Hospital’s Department of Medical Imaging in Toronto wanted to identify the sensitivity of contrast-enhanced CT in detecting prostate cancer. The study authors undertook a retrospective analysis of 100 patients with biopsy-proven prostate cancer who underwent a staged contrast-enhanced CT of the abdomen and pelvis within 3 months of diagnosis. They compared those scans to 100 randomly selected age-matched male controls with no history of prostate cancer who had also undergone similar CT scanning. They then recruited two blind readers to independently assess the likelihood of prostate adenocarcinoma based solely on the CT scans.

The blind readers correctly identified 83 out of the 100 patients with prostate cancer, as well as 93 of the 100 matched control participants with a sensitivity of 0.83, a specificity of 0.92, a PPV of 0.91, and NPV of 0.84). Interrater agreement was at 0.76. 

Based on these results, the study authors concluded that contrast-enhanced CT may allow radiologists to see a focal area of increased enhancement in the periphery of the prostate that has diagnostic value. Clinicians could use such scans to order further work-up, leading to earlier detection of this deadly disease.

From the efficacy of AI as a cyberweapon to MRI for diagnosing strokes, the latest radiology news and studies you need to know.

Kayt Sukel

Articles

AI Risk Score Predicts Breast Cancer Risk Better Than Mammographic Density

What Do Trainees Think of AI?

Artificial Intelligence: The Ethics and Abilities

Radiologists Tricked by AI (or Not), and Other Radiology News and Studies