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Radiological Society of North America (RSNA) 2021 Annual Meeting

 suggest that magnetic resonance imaging (MRI) alone is a successful method for the follow-up in women who undergo breast-conserving treatment and ensures early detection of recurrent cancer.

“Women in the follow-up after breast cancer who have access to breast MRI may safely forgo additional mammographic or digital breast tomosynthesis follow-up,” the authors wrote.

The results were presented by Vanessa Raaff, Ph.D., of the department of diagnostic and interventional radiology, RWTH Aachen University Hospital in Germany.

For follow-up after breast conserving treatment, serial two-view mammography is recommended, possibly accompanied by targeted ultrasound. As breast MRI has been shown to be useful to improve breast cancer detection of recurrence in patients after breast conserving treatment, the researchers investigated the usefulness of MRI as a standalone imaging method for this purpose.

Between January 2011 and March 2021, a total of 426 women who had undergone MRI alone for routine follow-up after breast conserving surgery were included. The women started MRI screening at a median of two years after treatment and underwent MRI screening annually. Validation of the last negative MRI study was achieved by tomosynthesis plus clinical examination in 137 women and/or by clinical follow-up for at least two years in 289 women.

The women were followed by 1,553 MRI studies, covering a total 1,727 women years. Across the entire follow-up period, MRI was positive in 108/1,553 examinations (6.9%) and in 85/426 women (19.9%). Recurrent breast cancer was confirmed in 64/108 MRI studies and in 55/85 women, for a positive predictive value of 59.3% per positive MRI study and 64.7% per patient. 

The cancer-detection-rate was 37 per 1000 women years. Recurrent cancers were detected on average 8.9 years after breast conserving treatment. Of the 64 recurrent cancers, 17.2% were ductal carcinoma in situ and 82.8% were invasive. The mean size of invasive recurrent cancer was 11.4 mm, most frequently staged as T1b, followed by T1c.

“None of the women received a diagnosis of breast cancer in between MRI screening rounds, or within the two-year follow up after the respective last negative MRI, or upon digital breast tomosynthesis after the last study MRI,” the authors wrote.

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Following breast cancer treatment, women who have access to breast MRI may safely forgo additional mammographic or digital breast tomosynthesis follow-up. 

MRI Alone Beneficial for Surveillance After Breast-Conserving Treatment

 describes a deep learning model that was developed to successfully detect intracranial hemorrhage in computed tomography (CT) scans without medical annotation.

“This method is faster because it removes the need for medical annotation, which can be added later on image errors to improve the model,” the authors wrote. “By detecting 96.6% of the normal exams, we can speed up doctor’s work, providing more time for the analysis of the slices with bleeding, increasing the precision of the diagnosis.”

Intracranial hemorrhage is a life-threatening condition that is diagnosed via CT scans. Deep learning algorithms developed to aid in the detection of bleeding within the intracranial cavity require a physician to annotate exams per slice. In this work, the researchers annotated a dataset of head CT exams using natural language processing (NLP) and applied an architecture using convolutional and recurrent neural networks to classify each exam. 

A total of 8432 head CT exams collected from six hospitals and five specialized outpatient clinics were assembled and the axial volumetric unenhanced series of scans were selected. The labels were extracted from the written reports using a previously trained NLP model. Almost 40% of exams had an intracranial hemorrhage, which were not separated by bleeding subtype. Each exam was processed using brain, blood and bone windows, resliced to 25 images. A MobileNet network pre-trained with ImageNet weights was combined with a Long short-term memory (LSTM) with categorical cross-entropy as loss function. The model’s output was as follows: hemorrhage, no finding and other pathologies. 

The model improved the classification without the need for a per slice annotation. On the validation set, an area under the curve (AUC) of 0.96, 0.87 for recall and 0.97 for precision was achieved. While on an external set, validated by a doctor, the AUC was 0.91, 0.85 and 0.86, respectively. 

The researchers also highlighted slices where the hemorrhage appears to aid diagnosis by applying Gradient Class Activation Map (GradCAM) technique. 

“Through the GradCAM method, we selected the best slices of each exam, and doctors were able to evaluate the results and indicate with more precision where a hemorrhage was found,” the authors wrote. “The identification of the most relevant slices, using the GradCAM technique, alongside clinical information, may help doctors better understand each patient case, accelerating and improving the diagnosis.”

A deep learning model was developed to detect intracranial hemorrhage in computed tomography scans without medical annotation.

Algorithm Can Detect Intracranial Hemorrhage in CT Without Medical Annotation 

In patients with esophageal squamous cell carcinoma (SCC), multiparametric magnetic resonance (MR) features provide useful diagnostic information of pathologic complete response after neoadjuvant therapy. This is according to a study presented at the 

“Multiparametic MR features provide useful diagnostic information of pathologic complete response among patients with esophageal SCC who undergo neoadjuvant therapy,” the authors wrote.

The results were presented by Jinrong Qu, M.D., of the department of radiology, affiliated cancer hospital of Zhengzhou University and Henan Cancer Hospital in Zhengzhou, China.

 earlier this year found that MRI has high sensitivity but low specificity for the detection of esophageal SCC and adenocarcinoma, showing promise for determining neoadjuvant therapy response. Meanwhile, a 

 of 54 patients with locally advanced esophageal SCC found that integrating parameters of diffusion-weighted MR imaging (DWI) with other imaging methods can better identify pathological response of primary tumor to neoadjuvant chemoradiotherapy.

The latest study presented by Dr. Qu included 36 patients with esophageal SCC at stage cT2-T4a/N0-N3/M0 who were retrospectively reviewed. MRI was performed before and after neoadjuvant therapy, followed by surgical resection. The lesions were analyzed for the following five MR features: changes of size, signal on DWI, apparent diffusion coefficient (ADC) map, T2-weighted turbo spin echo (TSE) BLADE and degree of enhancement. Response was assessed by using tumor regression grade (TRG).

The five MR features after neoadjuvant therapy showed strong positive correlations with TRG 0 (r = 0.7432; 

<.001). The 10 cases with TRG 0 showed either complete disappearance of the mass or decreased mass with minimal residual thickening of the esophageal wall. These TRG 0 cases also showed iso- or decreased-intensity on DWI, iso- or increased-intensity on ADC map, iso- or decreased-intensity on T2-weighted TSE BLADE, or homogeneous enhancement on high-resolution delayed StarVIBE. The five MR features were different from all the TRG 2-3 in 20 cases. 

ADC map had the highest performance in distinguishing TRG 0 from non-TRG 0 with the sensitivity of 96.1%, specificity of 76.5%, negative predictive value of 74.7% and positive predictive value of 91.6%

“MR assessment of changes in tumor size, signal on DWI, ADC map, T2-weighted TSE BLADE, and enhancement pattern may distinguish between biopsy proven TRG 0 and non-TRG 0 in patients with esophageal SCC,” the authors wrote.

Multiparametric MRI can detect pathologic complete response after neoadjuvant therapy in patients with esophageal squamous cell carcinoma.

MRI Features Help Detect Neoadjuvant Therapy Response in Esophageal SCC

The quantitative assessment of the deltoid muscle by ultrasound could accurately detect type 2 diabetes, according to a study presented at the 

“Following further studies, this process could translate into a dedicated, simple and noninvasive screening method to detect type 2 diabetes,” the authors wrote. The process could help identify “some of the 232 million undiagnosed persons globally and could prove especially beneficial in screening of underserved and underrepresented communities.”

The findings were presented by Steven Bishoy Soliman, D.O., RMSK, a musculoskeletal radiologist at Henry Ford Hospital & Health System in Michigan.

 by Dr. Soliman that included 137 patients found that the ultrasound appearance of the deltoid muscle in patients with diabetes differed from that in patients with obesity but without diabetes. In the latest study the researchers aimed to evaluate if the sonographic quantitative assessment of the deltoid muscle can be used to detect type 2 diabetes.

In the study, deltoid muscle ultrasound images from 124 patients were independently reviewed by three musculoskeletal radiologists. Patients were categorized as the following: obese type 2 diabetes, non-obese type 2 diabetes, obese non-type 2 diabetes, and non-obese non-type 2 diabetes, with 31 images in each category. The radiologists, who were blinded to the patient’s category, measured the grayscale pixel intensity of the deltoid muscle and humeral cortex to calculate a muscle/bone ratio for each patient. Over three weeks, the radiologists repeated measurements on a random 40 patients. Patient age, gender, race, body mass index, insulin usage and hemoglobin A1c level were analyzed and the difference among the four groups was compared. Inter- and intra-radiologist variability or agreement were also assessed

The results showed a statistically significant difference in muscle/bone ratios between the groups with the average ratios as follows: obese type 2 diabetes, 0.54 (

 < .001); non-obese type 2 diabetes, 0.48 (

 < .001); obese non- type 2 diabetes, 0.42 (

 = .03); and non-obese non- type 2 diabetes, 0.35.

Further, excellent inter-observer agreement (ICC 0.87) and intra-observer agreements (ICC 0.92, 0.95, and 0.94) were found. The sensitivity for detecting type 2 diabetes was 80% with a specificity of 63%.

“Quantitative deltoid muscle ultrasound can detect type 2 diabetes with the potential for a highly sensitive noninvasive screening method,” the authors wrote.

Sonographic quantitative assessment of the deltoid muscle could translate into a dedicated, simple and noninvasive screening method to detect type 2 diabetes.

Deltoid Muscle Ultrasound May Detect Type 2 Diabetes 

Patients with suspected axial spondyloarthropathy (AxSpA) not taking non-steroidal anti-inflammatory drugs (NSAIDs) are more likely to have MRI proven inflammation compared with those taking NSAIDs, according to study results presented at the 

“Undiagnosed AxSpA can have significant impact on patients’ physical and mental health but also their quality of life and livelihood,” the authors wrote. “It is possible that NSAID use at the time of scanning reduces the diagnostic yield of MRI scanning.”

The findings were presented by Prasanna Partha Sarathy, MBChB, BMedSc, of Singleton Hospital in Swansea, UK.

Early AxSpA diagnosis, which depends on HLA-B27 testing and MRI imaging, is essential. While NSAIDs can lead to improvement in MRI appearances, patients suspected of having AxSpA are often prescribed these drugs for symptomatic relief before imaging. In this study, the researchers assessed the use of MRI in patients with suspected AxSpA and the effect of NSAIDs on the diagnostic yield of MRI scanning.

The six-month, multicenter, retrospective cohort study included 74 patients referred for MRI of the spine for suspected AxSpA. At the time of MRI, the patients were asked about NSAID use. The MRI results were assessed for evidence of active inflammation. 

MRI showed signs of AxSpA in 22% of the patients. Those not using NSAIDs at the time of the scan were more likely to have MRI proven inflammation (

 = .017). After adjusting for age and gender, the study found that patients not taking NSAIDs were more likely to have MRI proven inflammation compared with those taking NSAIDs (OR 8.3; 95% CI, 1.9-36; 

= .005). This finding was mainly driven by the no NSAID versus regular NSAID use subgroups (OR 14; 95% CI, 2.1-97; 

 = .006). No difference was seen between patients not taking NSAIDs and those taking NSAIDs as required, or between patients taking NSAIDs as required versus those taking regular NSAIDs.

“This study suggests suspected AxSpA patients not taking NSAIDs were more likely to have MRI proven inflammation compared to patients taking NSAIDs especially those on regular NSAIDs,” the authors wrote.

They suggested that more prospective research is needed to explore this relationship further.

Findings suggest that patients with suspected axial spondyloarthropathy not taking NSAIDs were more likely to have MRI proven inflammation compared with patients taking NSAIDs.  

NSAID Use May Affect Axial Spondyloarthritis Diagnosis via MRI

 demonstrates the importance of using racially diverse datasets while training artificial intelligence (AI) systems to ensure fair outcomes.

“As the rapid development of deep learning in medicine continues, there are concerns of potential bias when interpreting radiological images,” the authors wrote. “As future medical AI systems are approved by regulators, it is crucial that model performance on different racial/ethnic groups is shared to ensure that safe and fair systems are being implemented.”

The findings were presented by Brandon Price, a medical student at Florida State University College of Medicine in Tallahassee.

Many studies have shown that deep learning systems are subjective in their interpretation of data. Bias is often accidentally introduced into the training data, or a racial or ethnic group is under sampled causing susceptible models to develop bias. In this study, the researchers investigated how a deep learning model trained on a dataset lacking racial diversity could impede the detection of pathology in underrepresented minority patients.

The researchers used a dataset with over 300,000 chest X-ray images and 14 labeled findings. A low sample size of other races/ethnicities meant that only images of Black and White patients were included. One training dataset included only White patients and the other training dataset comprised 26% Black and 74% White patients. An equal distribution of labeled findings was shared between the datasets and a DenseNet model was trained on each dataset 25 times. The receiver operating characteristics (ROC) area under the curve (AUC) and sensitivity, with a specificity threshold of 0.75, were compared for each of the 14 labeled findings.

Compared with a model trained on only White patients, the model trained with a diverse dataset had a significantly better ROC-AUC performance at identifying six of the 14 labeled findings in a test dataset of only Black patients (

 <0.05). Additionally, compared with a model trained on only White patients, the model trained with a diverse dataset found a significant increase in sensitivity performance for six of the 14 labeled findings on a test dataset of only Black patients (

“As more AI systems are developed, it is imperative that they are fair and perform equally well with groups that have been historically underserved,” the authors wrote.

Deep learning models trained on a dataset lacking racial diversity could hinder the detection of pathology in underrepresented minority patients. 

AI Trained on a Diverse Dataset Perform Better Chest X-ray Analysis

 suggest that breast ultrasound/magnetic resonance imaging (MRI) fusion technology may help reduce the need for MRI-guided biopsies.

“Breast ultrasound/MRI fusion significantly improves localization of incidentally detected MRI lesions which are occult on an initial ultrasound survey alone,” the authors wrote. “The ultrasound/MRI fusion technology will reduce the need for MRI guided biopsies and improve patients experience and save costs.” 

The findings were presented by Adrian Kuok Pheng Lim, M.D., FRCR, consultant radiologist and honorary lecturer at Imperial College imaging department, Charing Cross Hospital in London, UK. 

Breast ultrasound/MRI fusion can be challenging especially with a supine MRI sequence, which is not possible on all scanners. However, a new system has improved image synchronization working with any MRI scan and patient position. This study tested the feasibility of this new system with advanced fusion technology in characterizing incidental MRI detected breast lesions. 

The ongoing, prospective study has enrolled 30 patients with a known newly diagnosed breast cancer. All patients required a second look ultrasound following an incidental MRI detected lesion and these were performed within one week. All MRI studies were performed in the prone position using a 3T Verio scanner (Siemens). Dr. Lim performed all the fusion studies with an Aplio i800 scanner (Canon). He initially undertook a baseline ultrasound survey and recorded if the lesion could be confidently identified. The ultrasound was then fused with MRI sequences showing the hypervascular lesion. Ultrasound guided biopsies were undertaken as per routine clinical care. The area corresponding to that seen on the MRI study as indicated by the fusion system was biopsied when there was no defined lesion. The results were compared with histology.

A total of 11 lesions could be detected with ultrasound alone. Ultrasound/MRI fusion was needed to identify the lesion in 17 cases. Of these, 14 were biopsied affecting clinical management and three were normal lymph nodes and were not biopsied. In two cases, the lesion could not be seen with ultrasound or fusion and therefore had MRI guided biopsies. 

“The ultrasound/ MRI fusion system obviated the need for MRI guided biopsy in a significant number of cases. The ultrasound/MRI fusion was quick and easy to use with improved image registration when there was another identifiable lesion (e.g., a cyst), in addition to the nipple to provide accurate synchronization,” the authors wrote. “The system allows fusion with up to three sequences at once and a color-coded option to help denote the lesion of interest.”

Breast ultrasound/MRI fusion significantly improves localization of incidentally detected MRI lesions which are occult on an initial ultrasound survey alone.  

Breast Ultrasound/MRI Fusion May Help Reduce MRI-Guided Biopsies 

Radiological Society of North America (RSNA) Annual Meeting

 on November 28th covered the cardiovascular imaging manifestations of COVID-19.

“SARS-CoV-2 is a novel coronavirus which primarily affects the respiratory system, but has also been shown to impact other systems, especially with the hypercoagulable state,” said Margaret Revzon, M.D., an associate professor of radiology and biomedical imaging at Yale School of Medicine, in New Haven, Conn. “The hypercoagulable state and resulted vascular complications are not uncommon in patients with COVID-19.”

COVID-19 can result in cardiac complications including myocarditis, arterial and venous thromboembolism, and cardiomyopathy. While ultrasound is the first imaging choice for these patients, computerized tomography (CT) can be selected, but clinicians must note that the COVID-19 patient is often in the prone position. Magnetic resonance imaging (MRI) can be used for complex cases. Dr. Revzon highlighted that awareness and recognition of imaging manifestations of COVID-19 induced thromboembolic complications will ensure optimal treatment for these patients.  

Brian Burns Ghoshhajra, M.D., associate professor of radiology at Harvard medical School in Boston, Mass., said that “there is systematic inflammation, endothelial activation and direct myocardial effects of SARS-CoV-2.”

While cardiovascular imaging, including CT and MRI, play a key role, context and pre-test probably likely matter more than ever. Dr. Ghoshhajra suggested that radiology services should be optimized for cardiac imaging.

“COVID-19 doesn’t occur in a vacuum, so remember your basics and cast a wide diagnostic net of there is not an established COVID-19 diagnosis yet,” Dr. Ghoshhajra said.

There have also been infrequent emerging reports of myocarditis and myocardial injury in a minority of patients after COVID-19 vaccination.

Kate Hanneman, M.D., FRCPC, an associate professor in the department of medical imaging at Women's College Hospital in Toronto, Canada, explained that there are limited data on what cardiac imaging findings are expected in these patients. However, the pattern on imaging appears to be like myocarditis from other causes, but less extensive compared to other causes of myocarditis.

“The extent on imaging, based on the reports we have to date, appears to be relatively mild. So less extensive compared to some other causes of myocarditis,” Dr. Hanneman said.

COVID-19 can result in cardiac complications including myocarditis, arterial and venous thromboembolism, and cardiomyopathy.

Cardiovascular Imaging Manifestations of COVID-19 to Watch For

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Lung cancer continues to be the leading cause of cancer-related deaths globally, with 

5-year survival rates still hovering around 20%.

 Tobacco use continues to be the major risk factor for lung cancer. However, in nonsmokers, exposure to some industrial toxins such as arsenic, certain organic compounds, radon, asbestos, radiation, air pollution, and environmental tobacco smoke also increases the chance of developing lung cancer. Although the incidence of these primary risk factors is decreasing, the implementation of comprehensive tobacco control and the reduction of occupational chemical exposure are not the only measures for decreasing lung cancer mortality.

Artificial intelligence has been shown to be beneficial in the discovery of prognostic biomarkers for lung cancer diagnosis, treatment, and response evaluation, putting it at the forefront of the next phase of personalized medicine. Imaging is critical to all aspects of lung cancer care and has the potential to be a critical component of AI applications.

AI Tool Predicts Lung Cancer Combining Lung Screening CT and Clinical Data

, a novel algorithm that combines lung screening CT information with clinical data can more accurately predict lung cancer without the need for manual interpretation. The deep learning tool incorporates CT characteristics such as nodule size as well as cancer-related risks such as age, pack-years smoked, cancer history, occupational exposure history, and other variables. This model, which was developed and evaluated using tests from more than 23,000 patients, surpassed risk models that were based only on clinical or imaging data, including the famous Brock model, according to the findings. Riqiang Gao, a Ph.D. student in the Department of Computer Science, Vanderbilt University, Nashville, Tennessee, and colleagues anticipate their strategy of co-learning will be particularly beneficial as more patients become eligible for screening checks.

With the forthcoming extension of lung screening criteria to cover individuals who are deemed lesser risk only on the basis of their age and history of tobacco use, risk estimates among lung screening participants will become even more critical. According to Gao and co-authors, insights from this tool may reveal that otherwise low-risk people are really high-risk. In order to construct their deep learning method, the researchers used data from the National Lung Screening Trial (which included 23,505 patients) and a five-fold cross-validation technique. Approximately 150 patients who took part in an internal program were screened, and the results were utilized to do external testing.

In this study, deep learning outperformed previously published models based only on imaging data (0.86) and clinical risk variables (0.69), scoring 0.88 on the area under the receiver operating characteristic curve. One of the most significant advantages of this method is that it automatically extracts high-risk areas from CT scans without the need for radiologist intervention. At the same time, Gao et al. emphasized that obtaining clinical data still requires manual labor on the part of radiographers and other specialists. Gao et al. concluded that the radiologist's role in searching for and reporting clinically relevant pulmonary abnormalities - pulmonary fibrosis, rounded atelectasis, emphysema, among many others - remains essential.

A Boston-based Institution Found AI Tools Can Predict Emphysema Severity as Accurately as Professional Radiologists

Millions of people suffer from chronic respiratory ailments that affect their quality of life. Every year, more than 

 worldwide die prematurely as a result of severe respiratory illnesses. Long-term exposure to potentially dangerous, toxic, or irritating particles, gases, or even temperature conditions may have long-lasting effects on the lungs, even after the exposure has ceased. These particles may stem from a variety of sources, with certain occupations being more at risk than others. Based on the substantial connections discovered between working in particular industries and 

 due to lowered lung function, the researchers say that the most at-risk sectors include mining, steel, textile, and automotive.

Characteristics of the life-threatening illness Chronic Obstructive Pulmonary Disease (COPD) include partially damaged alveoli and lungs that are overinflated - emphysema. In clinical practice, diagnosis, and assessment of emphysema are based on the patient's history, lung function, and abnormal chest radiographs, among other variables. In most cases, chest radiographic findings cannot be used to make a diagnosis of mild emphysema, however, when emphysema has progressed to a more advanced stage, standard radiographic abnormalities are usually detected. Routine chest radiographs may show indications of emphysema, but this is not a sensitive diagnostic method. Chest radiography, on the other hand, is effective in detecting problems during acute exacerbations and in ruling out other illnesses such as superimposed infection or lung cancer.

When it comes to forecasting the severity of emphysema in patients, artificial intelligence can match - sometimes even surpass - the accuracy of trained radiologists.

, artificial intelligence techniques evaluated at a Boston-based institution were capable of matching professional radiologists in predicting patients' emphysema severity. Visual assessments of this bronchial anomaly, which is a source of clinical concern, may be subjective and susceptible to variation.

The researchers at the Massachusetts General Hospital sought to automate lung evaluation in order to remove differences and improve the reliability of chest CT in identifying the severity of COPD. Researchers analyzed 113 patients who had a non-contract CT scan and a lung function test during a two-month period. The severity of the disease was graded as mild, moderate, or severe according to the amount of air that patients could exhale during a forced breath. Each individual's emphysema was graded on a five-point scale by two thoracic radiologists.

Mannudeep Kalra, M.D., of Mass General's Department of Radiology, and colleagues published on Oct. 14 that the performance of the tested AI prototypes was equivalent to radiologists' subjective interpretation of emphysema severity. Although the outputs from the tested AI prototypes were similar across various scanner types, scan parameters, and reconstruction settings, the scientists noted that owing to insufficient radiation exposure, there is a lack of consistent performance among individuals with greater body habitus.

Between 2018 and 2020, patients diagnosed with COPD had chest CT at a single healthcare facility. The research included CT scanners from three different manufacturers, with pictures analyzed using Siemens Healthineers' AI-Rad Companion Chest prototype. Philips evaluated bronchial abnormalities using trained radiologists and a completely automated program.

Using forced expiratory volume (FEV), AI and thoracic radiologists were successful in differentiating mild, moderate, and chronic emphysema, as measured by area under the curve ratings of 0.77 and 0.76, respectively. They observed a considerable positive correlation between them. Additionally, the combination of radiologist-assessed emphysema and bronchial abnormality quantification and AI evaluations may distinguish between various severity levels.

Artificial intelligence has been shown to be beneficial in the discovery of prognostic biomarkers for lung cancer diagnosis, treatment, and response evaluation.

Integrating AI Into Radiology Practice to Enhance Lung Disease Prediction and Diagnosis

RSNA 2021 is quickly approaching as members of the greater radiology healthcare community prepare to immerse themselves in five days of scientific sessions, key lectures, and exciting product demonstrations. 

 sat down with Canon Medical's David Hashimoto, Senior Director, Full Line BUs and Solutions Marketing, to learn more about what Canon has planned for their virtual participation in RSNA 2021. 

Canon Medical will offer attendees--both in-person and virtual--an immersive experience that includes live product demonstrations and scheduled briefings with Canon Medical experts across the breadth of their portfolio, including CT, molecular imaging, MRI, ultrasound, X-ray, interventional radiology, and healthcare IT. 

For the latest coverage of RSNA 2021, click here. 

In addition, Canon Medical will also be hosting a roundtable discussion, "State of AI in Radiology Today," featuring a distinguished panel of speakers who will discuss the financial, technical, clinical, and regulatory considerations of applying AI in today's radiology practice and in the future. 