AuntMinnie.com's 2020 Radiology Reporter by auntminnie

Radiology Reporter YOUR GUIDE TO RSNA 2020

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CT quantifies COVID-19 severity, ongoing conditions AI algorithm can detect, quantify brain infarcts AI abdominal fat analysis assesses cardiovascular risk Can AI be a second reader in breast cancer screening? Machine-learning model predicts adverse cardiac outcomes Contrast-enhanced mammo can play pivotal diagnostic role Carlos: Radiologists should become more involved in patient care Top Stories in 2020 AuntMinnieEurope.com 2021 Media Kit Preview AuntMinnie.com 2021 Media Kit Preview

Radiology Reporter, Copyright ÂŠ 2020 AuntMinnie.com

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CT quantifies COVID-19 severity, ongoing conditions By Kate Madden Yee, AuntMinnie.com staff writer

Throughout this year’s COVID-19 pandemic, chest CT has proven to be a valuable tool for diagnosing the illness in particular clinical situations -- such as when reverse transcription polymerase chain reaction (RT-PCR) testing isn’t readily available or results are delayed. But the modality has also shown value in assessing the severity of the disease and evaluating ongoing conditions that can plague recovered patients, especially when combined with artificial intelligence (AI), according to several presentations in a scientific session on chest imaging delivered Sunday at the RSNA 2020 meeting. Predicting severity In the afternoon session, a team led by Ziyue Xu, PhD, senior scientist at graphics processing unit technology developer Nvidia, shared results from a study the company conducted that combined a deep-learning algorithm with chest CT to predict whether COVID-19 patients would be admitted to the intensive care unit (ICU). The findings suggest that AI could boost CT’s performance when it comes to helping clinicians predict COVID-19 severity, Xu told session attendees. “[This deep-learning algorithm can] alert the clinician to the enhanced potential of ICU admission, when combined with other clinical features,” he said. For the study, Xu and colleagues included 632

chest CT scans from patients with COVID-19 confirmed by RT-PCR testing; of these, 69 patients were admitted to the ICU and 563 were not. The team developed a whole-lung segmentation algorithm and assessed its effectiveness when used with CT by overall accuracy, sensitivity, and specificity. The algorithm achieved high accuracy, specificity, and negative predictive value (NPV) for identifying COVID-19 and predicting ICU admission on chest CT.

“Based upon chest CT alone, AI-based deeplearning algorithms can reasonably predict clinical outcomes such as ICU admission in patients with COVID-19 who underwent CT and PCR on the day of admission,” Xu concluded. “The model is feasible with reasonable accuracy and specificity of prediction.” Predicting prevalence In a second Sunday afternoon presentation, Italian researchers looked at whether CT’s performance varied depending on the prevalence of COVID-19 disease in a region. A group led by Dr. Marcello Petrini of Guglielmo da Saliceto Hospital in Piacenza, Italy, assessed the modality’s diagnostic performance for severe illness by comparing an outbreak phase to an ensuing period of lower disease incidence (first outbreak, high prevalence: February 21 to March 7; second period, lower prevalence after 28 days of lockdown: April 6-13).

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The researchers used RT-PCR results as the reference standard to evaluate CT’s sensitivity, specificity, positive predictive value, and negative predictive value.

Chest x-ray is the go-to for evaluating this condition, but it tends to have limited sensitivity. While chest CT is more accurate, it’s not used routinely to assess airspace disease.

The high-prevalence group included 198 patients and the low-prevalence group included 146 patients. The team found that while CT had about the same sensitivity regardless of disease prevalence, specificity was lower during the time of higher disease prevalence:

Barbosa and colleagues developed a way to quantify the percentage of lung volume involved in airspace disease on chest x-rays using a convolutional neural network (CNN) algorithm based on 1,000 chest CT scans of COVID-19 patients. The researchers used a test set of 86 patients with positive RT-PCR results who had chest CT and chest x-ray less than 48 hours apart. The algorithm projected the CT exams’ 3D lung and airspace disease segmentation on reconstructed x-rays using quantitative maps of lung tissue thickness and manifestations of airspace disease.

The study’s main finding of a high PPV rate on chest CT during the early-outbreak, highprevalence period in Italy and a high NPV rate during the second, low-disease-prevalence period should help clinicians more effectively manage patients suspected of COVID-19, Petrini concluded. “Even with a negative CT, the likelihood to have COVID-19 pneumonia is still high during the high-prevalence phase of the disease while it is very low in the low-prevalence period,” he said.

The group found that the CNN-reconstructed x-rays quantified airspace disease at least as well as the human CT exam readers: CT had a rate of 24.3%, while the CNN’s digitally reconstructed x-rays had a rate of 24.4%. “This approach may increase efficiency and consistency in chest x-ray interpretation of COVID-19 patients, especially when applied to longitudinal chest x-ray data to inform management,” Barbosa concluded.

Identifying airspace In the third Sunday afternoon talk, a team led by Dr. Eduardo Jose Mortani Barbosa of the University of Pennsylvania in Philadelphia noted that COVID-19 patients may develop airspace disease, in which alveolar air is replaced by fluid, pus, or blood and the condition continues beyond four to six weeks after treatment.

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AI algorithm can detect, quantify brain infarcts By Erik L. Ridley, AuntMinnie staff writer

Researchers discussed how they used a deeplearning algorithm to detect, quantify, and assess the severity of infarcts in the brain on diffusion-weighted MRI (DWI-MRI) exams in acute ischemic stroke patients in a Sunday presentation at the virtual RSNA 2020 meeting.

“The qualitative and quantitative results of our study show feasibility for detecting and quantifying infarcts.” – SEUNG HYUN HWANG

A team of researchers led by presenter Seung Hyun Hwang of Yonsei University in Seoul, South Korea, developed a deep-learning model that can segment and quantify brain infarcts using DWI-MRI and then assess their severity by analyzing apparent diffusion coefficient (ADC) maps of the lesions. In testing, the model achieved high sensitivity and specificity. “The qualitative and quantitative results of our study show feasibility for detecting and quantifying infarcts,” Hwang said. Due to its sensitivity for the detection of small and early infarcts, DWI-MRI is commonly

used for evaluation of acute ischemic stroke, according to Hwang. ADC maps can also be used as a reference for acute infarction. “However, it is dangerous to solely depend on ADC values, as ADC values are usually acquired at a single time point of the stroke in the course of clinical practice,” he said. Seeking to develop a deep learning-based automated infarct segmentation model, the researchers first gathered DWI and ADC maps from 394 patients with an acute infarct treated at their institution between January 2015 and May 2019. Of these datasets, 216 were used for training and 24 were utilized for validation. The remaining 154 datasets were set aside for testing of the model. The team elected to utilize a modified U-Net convolutional neural network in an ensemble approach in order to improve performance on small lesions, according to Hwang. After segmenting the infarct, the algorithm then measures infarct severity based on analysis of the ADC maps of the lesions. Infarcts are thereby classified into one of four categories: no stroke symptoms, minor stroke, moderate stroke, and severe stroke. An ADC value of more than 620 was used as the threshold for the “no stroke symptoms” category; other category thresholds were set at intervals of 100 ADC values. In testing, the algorithm yielded: • Average Dice coefficient: 0.85 • Average Dice coefficient when excluding extremely small lesions: 0.89 • Sensitivity: 83% • Specificity: 99%

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• Average volume difference for lesions calculated by model with those calculated by radiologists: 0.25 ml “Our algorithm, which is an end-to-end segmentation model, can be easily deployed and applied for other segmentation tasks as well,” Hwang said. As their algorithm has so far only been tested on internal datasets, the researchers now plan to perform external validation of their model, Hwang added. “Moreover, we plan to optimize our model in order to be more sensitive to small lesions,” he said.

“This work shows the promise of AI systems to add value to clinical care by extracting new information from existing imaging data,” said presenter Dr. Kirti Magudia, PhD, in a statement from the RSNA. “The deployment of AI systems would allow radiologists, cardiologists and primary care doctors to provide better care to patients at minimal incremental cost to the health care system.” Magudia, currently an abdominal imaging and ultrasound fellow at the University of California, San Francisco, shared the results of research performed while she was a radiology resident at Brigham and Women’s Hospital and part of a multidisciplinary group that developed the deep-learning algorithm.

AI abdominal fat analysis assesses cardiovascular risk By Erik L. Ridley, AuntMinnie staff writer

Body composition metrics automatically calculated from abdominal CT exams by an artificial intelligence (AI) algorithm are significantly associated with patient risk for future major cardiovascular events, according to a Wednesday morning presentation at the RSNA 2020 virtual meeting. After testing their deep-learning model on abdominal CT exams from over 12,000 patients, researchers from Brigham and Women’s Hospital in Boston found that its visceral fat measurements were independently associated with subsequent myocardial infarction and subsequent stroke. Body mass index (BMI), however, was not.

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Shortcomings of BMI Body composition is largely thought of as a crude surrogate for BMI, but that measure has its shortcomings. Patients with the same BMI can have markedly different ratios of subcutaneous fat to skeletal muscle, Magudia said. Subcutaneous fat, visceral fat, and skeletal muscle can be visualized on a single axial CT slice from the L3 spine vertebra, but it’s timeintensive and costly to manually measure these individual areas. Consequently, utilization of this technique has been limited to small cohorts and well-funded research studies, she said. To help, the group developed a deep learningbased automated body composition analysis tool. With this approach, axial abdominal CT series are selected via series selection logic from abdominal CT studies in their PACS.

“These results demonstrate that precise measures of body muscle and fat compartments achieved through CT outperform traditional biomarkers for predicting risk for cardiovascular outcomes.” – DR. KIRTI MAGUDIA, PhD

After a slice selection network is applied to select the L3 CT slice, a segmentation network then creates a segmentation image mask, which can be used to calculate body composition areas, Magudia said. During validation, the model’s automated segmentation results correlated very highly (r = 0.99) with radiologist segmentations for all body compartments.

Example of body composition analysis of an abdominal CT slice with subcutaneous fat in green, skeletal muscle in red, and visceral fat in yellow. Image and caption courtesy of Dr. Kirti Magudia, PhD, and the RSNA.

The researchers next used the algorithm to retrospectively determine body composition areas for all outpatient abdominal CT exams performed at Partners HealthCare in Boston in 2012. After subjects with major cardiovascular disease or cancer were excluded, a total of 12,128 patients were included in the study. Automated analysis Automated analysis was performed on the first abdominal CT scan acquired from each subject in 2012. Quality assurance analysis showed that the algorithm had a 2.5% overall segmentation error rate, Magudia said.

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After race-, sex-, and age-specific reference curves were used to determine demographicspecific z-scores for each parameter, the researchers then evaluated the association between composition z-scores and subsequent myocardial infarction and stroke. Patients were divided into four quartiles for each normalized body composition parameter. The researchers performed univariate analysis, as well as multivariate analysis that encompassed all body composition parameters and weight, height, and cardiovascular risk factors. Association with risk For visceral fat, individuals in the fourth quartile -- those with the highest proportion of visceral fat area -- were more likely to have future myocardial infarctions following the abdominal CT scan than patients in other quartiles (see figure 1).

Figure 2. Chart courtesy of Dr. Kirti Magudia, PhD, and the RSNA.

However, BMI was not associated with future heart attack or stroke, according to the researchers. â&#x20AC;&#x153;These results demonstrate that precise measures of body muscle and fat compartments achieved through CT outperform traditional biomarkers for predicting risk for cardiovascular outcomes,â&#x20AC;? Magudia said in a statement.

Figure 1. Chart courtesy of Dr. Kirti Magudia, PhD, and the RSNA.

Meanwhile, the first quartile of patients was protected against stroke in the years following the abdominal CT exam (see figure 2).

Radiology Reporter, Copyright ÂŠ 2020 AuntMinnie.com

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Can AI be a second reader in breast cancer screening? By Erik L. Ridley, AuntMinnie staff writer

An artificial intelligence (AI) algorithm could obviate the need for a second radiologist to review a screening mammogram in more than four out of five exams performed for a national breast cancer screening program, according to a presentation on Monday morning at the virtual RSNA 2020 meeting. A team of researchers led by Dr. Nisha Sharma of Leeds Teaching Hospitals NHS Trust in the U.K. simulated the use of AI as a second reader by retrospectively applying an AI algorithm to over 40,000 screening mammograms and then comparing the software’s results with that of the initial radiologist interpretation. They found that over 80% of the exams could have been accurately categorized as either normal or abnormal without requiring any additional interpretation. “Our study shows that an AI algorithm is a viable option to replace the second human reader in the double reading of screening mammograms,” Sharma said. Standard of care Although the practice isn’t common in the U.S., double reading of screening mammography studies is the standard of care in many countries around the world. In the U.K., women ages 50 to 70 are eligible to receive breast screening under the National Health System (NHS) National Breast Screening program every three years. Screening mammograms are

interpreted independently by two radiologists. If the readers agree on the findings, this consensus result is used to categorize a mammogram as normal and the woman is then invited back for routine screening in three years. Women will be recalled for second-stage screening if their screening mammogram is considered to be abnormal by both readers. If the two readers have discordant findings, then a third reader or readers will review the study to determine the diagnosis. This model is labor-intensive, however, and difficult to achieve due to the ongoing workforce crisis in the U.K., according

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to Sharma. Furthermore, 26% of breast radiologists in the U.K. are expected to retire in the next five years. Replacing second human reader As a result, the researchers sought to explore the potential role of utilizing AI software to replace the second human reader. They gathered 40,588 anonymized mammograms acquired at three breast screening centers from January 2012 to 2019. All of these mammograms had been interpreted via double reading with either a single human reader or group of readers providing arbitration for discordant opinions, Sharma said. Next, the authors obtained the original human reading opinions and the patients’ outcomes for recalled cases with pathology from the National Breast Screening information system. Sharma noted that the mammograms used in the study were a random sample and had not been used in developing or training the AI algorithm. Of the 40,588 mammograms in the study, 1,216 (3%) had a discordant opinion that required arbitration. Overall, there were 358 biopsy-proven cancers and 40,230 normal exams. The recall rate was 4%, with a cancer detection rate of 8.5 per 1,000. Simulated double reading To simulate double reading, the AI algorithm’s opinion -- normal or cancer -- was paired with the opinion of the first human reader to simulate a double reading process. The researchers then calculated the sensitivity, specificity, and discordant opinion rate. After applying the AI algorithm to the test set, the researchers found that it agreed with the

radiologist on whether to recall or not to recall the patient in 33,255 (81.9%) of the cases. The remaining 7,333 (18%) exams had discordant opinions.

The combination of AI and reader 1 yielded a cancer detection rate of 8.4 per 1,000 and a recall rate of 4%, according to Sharma. Using an AI algorithm to replace the second human reader would have allowed 81.9% of the women to obtain a definitive diagnosis of normal or abnormal, according to Sharma. “Only 18.1% of cases would need the input of an additional human reader, providing a feasible solution to combat the workforce crisis within breast imaging,” Sharma said. “This solution would allow the opportunity to create efficiency within the workforce.” A definitive diagnosis Under their proposed model, screening mammograms that have matching normal interpretations by the human reader and AI would require no further human reads. These women would then go back to routine screening, while those considered to have abnormal studies would be required to attend second-stage screening, Sharma said. Cases that have discordant opinions would need to be reviewed by another human reader or a group of readers to decide on the final diagnosis of normal or abnormal. Sharma acknowledged a number of limitations of their study, including its retrospective nature and relatively small number of cases. Also, interval cancer data was not included, she said.

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Machine-learning model predicts adverse cardiac outcomes By Erik L. Ridley, AuntMinnie staff writer

A machine-learning algorithm for analysis of coronary CT angiography (CCTA) exams was able to predict major adverse cardiac events at a higher level of accuracy than other traditional risk scores and risk factors, according to a presentation on Tuesday morning at the virtual RSNA 2020 meeting. In a retrospective study, researchers led by Dr. Christian Tesche of the Medical University of South Carolina found that their machinelearning model could improve risk stratification for major cardiac adverse events compared with conventional risk scores and clinical information. It also outperformed conventional regression analysis. “Machine learning may improve the integration of patient information to improve risk stratification,” Tesche said. CCTA-based risk scores mostly reflect the coronary plaque burden by the location, extent, and severity of coronary artery disease (CAD), according to Tesche. Previous studies have shown that these risk scores have prognostic value, yielding superior outcome predictions to traditional clinical risk scores. As machine learning can yield improved time efficiency and diagnostic accuracy for optimizing predictions based on various input features, the researchers sought to use

the technology to evaluate the long-term prognostic value of CCTA-derived plaque measures and clinical parameters on major adverse cardiac events, Tesche said.

“RUSBoost is particularly well-suited for imbalanced datasets which are typically observed in the context of [major adverse cardiac event] prediction.” – DR. CHRISTIAN TESCHE

The researchers retrospectively analyzed a dataset of 361 patients who were suspected to have CAD and who received a CCTA exam. Next, they recorded the occurrence of major adverse cardiac events more than 90 days after the CCTA study. These events included cardiac death as well as unstable angina leading to coronary revascularization with more than six weeks between CCTA and revascularization procedure. Of the 361 patients, 31 (8.6%) had a major adverse cardiac event over the median follow-up period of 5.4 years. The authors then assessed several CCTAderived plaque measures, including lowattenuation plaque, napkin-ring sign, spotty calcifications, remodeling index, segment stenosis score, and segment involvement score. In addition, they obtained cardiovascular risk factors and the Framingham risk score from medical records.

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A total of 28 of these variables were then provided to a machine-learning model that’s based on RUSBoost, an ensemble classification algorithm that utilizes decision trees. “RUSBoost is particularly well-suited for imbalanced datasets which are typically observed in the context of [major adverse cardiac event] prediction,” Tesche said. The researchers then compared the performance of the algorithm for predicting major adverse cardiac events with that of conventional regression analysis, as well as individual variables such as conventional CCTA risk scores, plaque measures, and clinical information.

Furthermore, the algorithm also outperformed conventional regression analysis, which produced an area under the curve of 0.92. That difference was also statistically significant (p = 0.024). Tesche acknowledged a number of limitations of the study, including the lack of an external validation cohort. In addition, its retrospective design also could induce a selection bias, he said. As patient follow-up was performed using electronic medical record searches and patient phone calls, major adverse cardiac events may also be underreported.

Contrast-enhanced mammo can play pivotal diagnostic role By Theresa Pablos, AuntMinnie staff writer

Contrast-enhanced spectral mammography (CESM) can play a pivotal role in diagnosing some types of breast cancer, according to research presented at the RSNA 2020 virtual meeting. A new study found CESM yielded 93% accuracy for identifying high-grade ductal carcinoma in situ (DCIS). In the study of 510 cases, high-grade DCIS and invasive cancers almost exclusively showed enhancement on contrast-enhanced mammography. The findings reflect the pivotal role that CESM can play in further assessing suspicious microcalcifications on standard mammography, noted presenter Dr. Mohammed Mohamed Gomaa. “The presence of associated underlying nonmass enhancement is an indicator of highgrade DCIS or invasiveness,” said Gomaa, head of the radiology department at Baheya Charity Women’s Cancer Hospital in Giza, Egypt, in an on-demand presentation. All women in the study were between the ages of 27 and 77 and underwent standard mammography, which revealed suspicious microcalcifications. Gabaa and colleagues then used contrast-enhanced mammography to further visualize the suspicious microcalcifications.

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The microcalcifications varied greatly in distribution and morphology on CESM. Grouped distribution and amorphous morphology were the most common presentations, but neither accounted for more than half of all cases. Three-quarters of the 510 patients were eventually diagnosed with cancer. Of the 405 malignant lesions, 330 were DCIS and 75 were invasive cancers. The majority of DCIS cases (70%) were high grade, while 20% were low grade and 10% were intermediate grade.

“Lack of enhancement is favorable to diagnose nonmalignant lesions or noninvasiveness or low-grade DCIS,” Gomaa said. CESM looked especially promising for identifying invasive or high-grade DCIS from suspicious microcalcifications. For high-grade DCIS, the modality yielded an accuracy of 93% and sensitivity of 98%. It also netted an accuracy of 88% and sensitivity of 99% for both invasive cancer and high-grade DCIS. Despite its promising performance for higherrisk cancers, CESM only netted a positive predictive value of 62% and sensitivity of 79% for all lesion types combined. Still, Gomaa was excited by the modality’s diagnostic performance potential. He discussed several cases where CESM helped to distinguish between microcalcifications signaling cancer versus benign findings. In one case, a 44-year-old woman showed bilateral fibrocystic changes and rounded and punctate microcalcifications on mammography.

“Lack of enhancement is favorable to diagnose nonmalignant lesions or noninvasiveness or low-grade DCIS.” – DR. MOHAMMED MOHAMED GOMAA

Enhancement on CESM varied by the type of lesion. No benign or low-grade DCIS lesions showed enhancement on CESM, while all invasive cancers showed enhancement. Similarly, all but four high-grade DCIS lesions showed enhancement on CESM.

Although the microcalcifications appeared similar in each breast on standard mammography, further CESM revealed important differences. The patient’s right breast showed nonmass enhancement on CESM, which corresponded to grade III invasive breast cancer. But the microcalcifications in the patient’s left breast showed no enhancement on CESM and instead represented proliferative fibrocystic disease with usual duct hyperplasia. “Contrast-enhanced spectral mammography has a pivotal role in assessment of grading and invasiveness of suspicious microcalcifications,” Gabaa concluded.

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Carlos: Radiologists should become more involved in patient care Radiologists should work to reintegrate themselves into patient care, according to Dr. Ruth Carlos, the winner of the 2020 Minnies award for Most Effective Radiology Educator. Carlos, editor in chief of the Journal of the American College of Radiology, discusses her career and her thoughts on where radiology is going with AuntMinnie.com. Carlos has been involved in efforts to change the public’s perception of radiologists and reintegrate them into patient care, from screening to treatment with innovative new imaging-based technologies like interventional oncology. She has also moved JACR into covering emerging issues like health equity and the financial toxicity of medical interventions, including imaging exams.

In this video interview, Carlos talks about her career and her areas of interest with AuntMinnie.com Editor-in-Chief Brian Casey. Now in their 21st year, the Minnies awards are AuntMinnie.com’s annual event recognizing excellence in radiology, with over 200 candidates competing in 15 categories, ranging from Most Influential Radiology Researcher to Radiology Image of the Year. Minnies candidates are nominated by AuntMinnie.com members, with winners selected by an expert panel of radiology luminaries in two rounds of voting. Winners are recognized each year at the annual RSNA meeting. A full list of winners in the 2020 edition of the Minnies is available on AuntMinnie.com.

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AuntMinnie.com's 2020 Radiology Reporter

Articles inside

Contrast-enhanced mammo can play pivotal diagnostic role

AI abdominal fat analysis assesses cardiovascular risk

Machine-learning model predicts adverse cardiac outcomes

AI algorithm can detect, quantify brain infarcts

CT quantifies COVID-19 severity, ongoing conditions

Can AI be a second reader in breast cancer screening?