In Vivo Ultrasound Molecular Imaging in the Evaluation of Complex Ovarian Masses: A Practical Guide to Correlation with Ex Vivo Immunohistochemistry.

Ovarian cancer is the fifth leading cause of cancer-related deaths in women and the most lethal gynecologic cancer. It is curable when discovered at an early stage, but usually remains asymptomatic until advanced stages. It is crucial to diagnose the disease before it metastasizes to distant organs for optimal patient management. Conventional transvaginal ultrasound imaging offers limited sensitivity and specificity in the ovarian cancer detection. With molecularly targeted ligands addressing targets, such as kinase insert domain receptor (KDR), attached to contrast microbubbles, ultrasound molecular imaging (USMI) can be used to detect, characterize and monitor ovarian cancer at a molecular level. In this article, the authors propose a standardized protocol is proposed for the accurate correlation between in- vivo transvaginal KDR-targeted USMI and ex vivo histology and immunohistochemistry in clinical translational studies. The detailed procedures of in vivo USMI and ex vivo immunohistochemistry are described for four molecular markers, CD31 and KDR with a focus on how to enable the accurate correlation between in vivo imaging findings and ex vivo expression of the molecular markers, even if not the entire tumor could can be imaged by USMI, which is not an uncommon scenario in clinical translational studies. This work aims to enhance the workflow and the accuracy of characterization of ovarian masses on transvaginal USMI using histology and immunohistochemistry as reference standards, which involves sonographers, radiologists, surgeons, and pathologists in a highly collaborative research effort of USMI in cancer.

Practical Tips and a Template for Developing Your Curriculum Vitae.

The Alliance of Leaders in Academic Affairs in Radiology (ALAAR) advocates for a Universal Curriculum Vitae for all medical institutions and to that end, we have developed a template that can be downloaded on the AUR website (ALAAR CV template) that includes all of the elements required by many academic institutions. Members of ALAAR represent multiple academic institutions and have spent many hours reviewing and providing input on radiologists' curricula vitae. The purpose of this review is to help academic radiologists accurately maintain and optimize their CVs with minimal effort and to clarify common questions that arise at many different institutions in the process of constructing a CV.

Defining the Roles and Responsibilities for the Vice Chair for Academic Affairs/Faculty Development in Radiology.

RATIONALE AND OBJECTIVES: To inform the development of a job description for Vice-Chairs for academic affairs (VCAA), members of the Alliance of Leaders in Academic Affairs in Radiology (ALAAR) were surveyed to better understand their current job responsibilities and how they would ideally allocate their professional time. MATERIALS AND METHODS: Based on a survey of 33 university-affiliated radiology departments and discussion among ALAAR members, the authors developed a detailed job description for the VCAA. The 21-question survey was composed and validated by experts in the field. It was distributed to all members of ALAAR via email with an electronic link and was open for 5 months. Results of the survey were tabulated, and a job description was crafted to represent the foundational roles of academic affairs leaders in radiology. RESULTS: The response rate for institutions represented in ALAAR was 73% (33/45). All participants reported that they practiced in a university-affiliated institution. Faculty size varied from ≤49 (30.3%, 10/33), 50-99 faculty (24.2%, 8/33), and ≥100 faculty members (45.5%, 15/33). Only 24% of survey respondents had a detailed job description at the time of hire. More than 40% attested to significant oversight over faculty development programs (45%), mentorship programs (42%, and promotions (45%). Respondents ideally want increased oversight (defined as >10%) over exit interviews, faculty awards, promotions, onboarding, recruitment and hiring, and wellness programming. CONCLUSION: The aspirational mission of the VCAA is to oversee components of sequential stages in the professional lifecycle of faculty members but a common job description for this role is lacking.

Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk Stratification System Using US Cine-Clip Images.

Purpose: To develop a deep learning-based risk stratification system for thyroid nodules using US cine images. Materials and Methods: In this retrospective study, 192 biopsy-confirmed thyroid nodules (175 benign, 17 malignant) in 167 unique patients (mean age, 56 years ± 16 [SD], 137 women) undergoing cine US between April 2017 and May 2018 with American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS)-structured radiology reports were evaluated. A deep learning-based system that exploits the cine images obtained during three-dimensional volumetric thyroid scans and outputs malignancy risk was developed and compared, using fivefold cross-validation, against a two-dimensional (2D) deep learning-based model (Static-2DCNN), a radiomics-based model using cine images (Cine-Radiomics), and the ACR TI-RADS level, with histopathologic diagnosis as ground truth. The system was used to revise the ACR TI-RADS recommendation, and its diagnostic performance was compared against the original ACR TI-RADS. Results: The system achieved higher average area under the receiver operating characteristic curve (AUC, 0.88) than Static-2DCNN (0.72, P = .03) and tended toward higher average AUC than Cine-Radiomics (0.78, P = .16) and ACR TI-RADS level (0.80, P = .21). The system downgraded recommendations for 92 benign and two malignant nodules and upgraded none. The revised recommendation achieved higher specificity (139 of 175, 79.4%) than the original ACR TI-RADS (47 of 175, 26.9%; P < .001), with no difference in sensitivity (12 of 17, 71% and 14 of 17, 82%, respectively; P = .63). Conclusion: The risk stratification system using US cine images had higher diagnostic performance than prior models and improved specificity of ACR TI-RADS when used to revise ACR TI-RADS recommendation.Keywords: Neural Networks, US, Abdomen/GI, Head/Neck, Thyroid, Computer Applications-3D, Oncology, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing.

Purpose: To automatically identify a cohort of patients with pancreatic cystic lesions (PCLs) and extract PCL measurements from historical CT and MRI reports using natural language processing (NLP) and a question answering system. Materials and Methods: Institutional review board approval was obtained for this retrospective Health Insurance Portability and Accountability Act-compliant study, and the requirement to obtain informed consent was waived. A cohort of free-text CT and MRI reports generated between January 1991 and July 2019 that covered the pancreatic region were identified. A PCL identification model was developed by modifying a rule-based information extraction model; measurement extraction was performed using a state-of-the-art question answering system. The system's performance was evaluated against radiologists' annotations. Results: For this study, 430 426 free-text radiology reports from 199 783 unique patients were identified. The NLP model for identifying PCL was applied to 1000 test samples. The interobserver agreement between the model and two radiologists was almost perfect (Fleiss κ = 0.951), and the false-positive rate and true-positive rate were 3.0% and 98.2%, respectively, against consensus of radiologists' annotations as ground truths. The overall accuracy and Lin concordance correlation coefficient for measurement extraction were 0.958 and 0.874, respectively, against radiologists' annotations as ground truths. Conclusion: An NLP-based system was developed that identifies patients with PCLs and extracts measurements from a large single-institution archive of free-text radiology reports. This approach may prove valuable to study the natural history and potential risks of PCLs and can be applied to many other use cases.Keywords: Informatics, Abdomen/GI, Pancreas, Cysts, Computer Applications-General (Informatics), Named Entity Recognition Supplemental material is available for this article. © RSNA, 2022See also commentary by Horii in this issue.

Can the use of deception be justified in medical education research? A point/counterpoint and case study.

Deception is a common feature of behavioral research design, although not commonly employed in the medical literature. It can promote scientific validity but is ethically controversial because it compromises subject autonomy and incurs additional costs.  In this Point/Counterpoint monograph, we review the nature of deception in research and present arguments for and against its ethical use as a research methodology in behavioral studies.  We describe the necessary guidelines, safeguards, and oversight, when deceptive methodology is considered, and report our experiences and lessons learned from conducting a multi-institutional audit study that relied upon deception of academic radiology faculty.

Review of Learning Tools for Effective Radiology Education During the COVID-19 Era.

Coronavirus disease 2019 (COVID-19) has significantly disrupted medical education around the world and created the risk of students missing vital education and experience previously held within actively engaging in-person activities by switching to online leaning and teaching activities. To retain educational yield, active learning strategies, such as microlearning and visual learning tools are increasingly utilized in the new digital format. This article will introduce the challenges of a digital learning environment, review the efficacy of applying microlearning and visual learning strategies, and demonstrate tools that can reinforce radiology education in this constantly evolving digital era such as innovative tablet apps and tools. This will be key in preserving and augmenting essential medical teaching in the currently trying socially and physically distant times of COVID-19 as well as in similar future scenarios.

Imaging Assessment of Pancreatic Cancer Resectability After Neoadjuvant Therapy: AJR Expert Panel Narrative Review.

Despite important innovations in the treatment of pancreatic ductal adenocarcinoma (PDAC), PDAC remains a disease with poor prognosis and high mortality. A key area for potential improvement in the management of PDAC, aside from earlier detection in patients with treatable disease, is the improved ability of imaging techniques to differentiate treatment response after neoadjuvant therapy (NAT) from worsening disease. It is well established that current imaging techniques cannot reliably make this distinction. This narrative review provides an update on the imaging assessment of pancreatic cancer resectability after NAT. Current definitions of borderline resectable PDAC, as well as implications for determining likely patient benefit from NAT, are described. Challenges associated with PDAC pathologic evaluation and surgical decision making that are of relevance to radiologists are discussed. Also explored are the specific limitations of imaging in differentiating the response after NAT from stable or worsening disease, including issues relating to protocol optimization, tumor size assessment, vascular assessment, and liver metastasis detection. The roles of MRI as well as PET and/or hybrid imaging are considered. Finally, a short PDAC reporting template is provided for use after NAT. The highlighted methods seek to improve radiologists' assessment of PDAC treatment response after NAT.

Factors Influential in the Selection of Radiology Residents in the Post-Step 1 World: A Discrete Choice Experiment.

OBJECTIVES: Reporting of United States Medical Licensing Examination Step 1 results will transition from a numerical score to a pass or fail result. We sought an objective analysis to determine changes in the relative importance of resident application attributes when numerical Step 1 results are replaced. METHODS: A discrete choice experiment was designed to model radiology resident selection and determine the relative weights of various application factors when paired with a numerical or pass or fail Step 1 result. Faculty involved in resident selection at 14 US radiology programs chose between hypothetical pairs of applicant profiles between August and November 2020. A conditional logistic regression model assessed the relative weights of the attributes, and odds ratios (ORs) were calculated. RESULTS: There were 212 participants. When a numerical Step 1 score was provided, the most influential attributes were medical school (OR: 2.35, 95% confidence interval [CI]: 2.07-2.67), Black or Hispanic race or ethnicity (OR: 2.04, 95% CI: 1.79-2.38), and Step 1 score (OR: 1.8, 95% CI: 1.69-1.95). When Step 1 was reported as pass, the applicant's medical school grew in influence (OR: 2.78, 95% CI: 2.42-3.18), and there was a significant increase in influence of Step 2 scores (OR: 1.31, 95% CI: 1.23-1.40 versus OR 1.57, 95% CI: 1.46-1.69). There was little change in the relative influence of race or ethnicity, gender, class rank, or clerkship honors. DISCUSSION: When Step 1 reporting transitions to pass or fail, medical school prestige gains outsized influence and Step 2 scores partly fill the gap left by Step 1 examination as a single metric of decisive importance in application decisions.

Ultrasound Liver Imaging Reporting and Data System (US LI-RADS) Visualization Score: a reliability analysis on inter-reader agreement.

BACKGROUND & AIM: The American College of Radiology Ultrasound Liver Imaging Reporting and Data System (ACR US LI-RADS) Visualization Score conveys the expected level of sensitivity of screening and surveillance ultrasound exams in patients at risk for hepatocellular carcinoma (HCC). We sought to determine inter-reader agreement of the Visualization Score which is currently unknown. METHODS: Consecutive 6998 ultrasound HCC screening and surveillance studies in 3115 patients from 2017 to 2020 were retrospectively retrieved. Of these, 6154 (87.9%) studies were Visualization A (No or minimal limitations), 709 (10.1%) were Visualization B (Moderate limitations), and 135 (1.9%) were Visualization C (Severe limitations). Randomly sampled 90 studies, with 30 studies in each Visualization category, were included for analysis. Nine radiologists (3 senior attendings, 3 junior attendings and 3 body imaging fellows) blinded to the original categorization independently reviewed each study and assigned a Visualization Score. Intraclass correlation coefficient (ICC) was used to quantify inter-reader agreement. RESULTS: ICC among all 9 radiologists was 0.70 (95% CI 0.63-0.77). ICCs among senior attendings, junior attendings and body imaging fellows were 0.68 (CI 0.58-0.76), 0.72 (CI 0.62-0.80) and 0.76 (CI 0.68-0.83), respectively. Subgroup analysis by liver parenchyma was further performed. ICC was highest in the patient group with normal liver parenchyma (0.69, CI 0.56-0.81), followed by steatosis (0.66, CI 0.54-0.79) and cirrhosis (0.58, CI 0.43-0.73), respectively. CONCLUSIONS: US LI-RADS Visualization Score is a reliable tool with good inter-reader agreement that can be used to indicate the expected level of sensitivity of a screening and surveillance ultrasound examination for detecting focal liver observations.

Inclusion of Thyroid Nodule Location in American College of Radiology TI-RADS Scoring: Impact on System Performance.

Emerging data suggest that the location of thyroid nodules influences malignancy risk. The purpose of this study was to explore the impact of including location in American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) scoring. Four of five revised scoring algorithms that added 1 or 2 points to higher-risk locations were associated with lowered accuracy due to lower specificity. However, an algorithm that added 1 point to isthmic nodules did not differ significantly from ACR TI-RADS in accuracy; one additional isthmic cancer was diagnosed for each 10.3 additional benign nodules recommended for biopsy.

Analysis of Malignant Thyroid Nodules That Do Not Meet ACR TI-RADS Criteria for Fine-Needle Aspiration.

OBJECTIVE. Compared with other guidelines, the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) has decreased the number of nodules for which fine-needle aspiration is recommended. The purpose of this study was to evaluate the characteristics of malignant nodules that would not be biopsied when the ACR TI-RADS recommendations are followed. MATERIALS AND METHODS. We retrospectively reviewed a total of 3422 thyroid nodules for which a definitive cytologic diagnosis, a definitive histologic diagnosis, or both diagnoses as well as diagnostic ultrasound (US) examinations were available. All nodules were categorized using the ACR TI-RADS, and they were divided into three groups according to the recommendation received: fine-needle aspiration (group 1), follow-up US examination (group 2), or no further evaluation (group 3). RESULTS. Of the 3422 nodules, 352 were malignant. Of these, 240 nodules were assigned to group 1, whereas 72 were assigned to group 2 and 40 were included in group 3. Sixteen of the 40 malignant nodules in group 3 were 1 cm or larger, and, on the basis of analysis of the sonographic features described in the ACR TI-RADS, these nodules were classified as having one of five ACR TI-RADS risk levels (TR1-TR5), with one nodule classified as a TR1 nodule, eight as TR2 nodules, and seven as TR3 nodules. If the current recommendation of no follow-up for TR2 nodules was changed to follow-up for nodules 2.5 cm or larger, seven additional malignant nodules and 316 additional benign nodules would receive a recommendation for follow-up. If the current size threshold (1.5 cm) used to recommend US follow-up for TR3 nodules was decreased to 1.0 cm, seven additional malignant nodules and 118 additional benign nodules would receive a recommendation for follow-up. CONCLUSION. With use of the ACR TI-RADS, most malignant nodules that would not be biopsied would undergo US follow-up, would be smaller than 1 cm, or would both undergo US follow-up and be smaller than 1 cm. Adjusting size thresholds to decrease the number of missed malignant nodules that are 1 cm or larger would result in a substantial increase in the number of benign nodules undergoing follow-up.

Effect of Decreasing the ACR TI-RADS Point Assignment for Punctate Echogenic Foci When They Occur in Mixed Solid and Cystic Thyroid Nodules.

OBJECTIVE. Using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS), this study sought to determine whether decreasing the point assignment for punctate echogenic foci in mixed solid and cystic nodules would reduce the number of benign mixed solid and cystic nodules that were biopsied and would not substantially increase the number of missed mixed carcinomas MATERIALS AND METHODS. A multiinstitutional database of 3422 pathologically proven thyroid nodules was evaluated to identify all mixed solid and cystic nodules with punctate echogenic foci. We determined the numbers of mixed benign and malignant nodules that would receive ACR TI-RADS recommendations of fine-needle aspiration, follow-up, and no further evaluation if the points assigned to punctate echogenic foci were changed from 3 points to 1 or 2 points. RESULTS. A total of 287 mixed nodules were adequately characterized for evaluation. When the number of points assigned to punctate echogenic foci was changed from 3 points to 1 point, the point categories changed for 198 mixed nodules. Seven carcinomas would not undergo biopsy, but six of those seven would receive follow-up, and 44 benign nodules would not undergo biopsy. When 2 points were assigned to punctate echogenic foci, the point categories changed for 66 mixed nodules. Three carcinomas would not undergo biopsy, but all three of these would receive follow-up, and eight benign nodules would not undergo biopsy. CONCLUSION. Consideration should be given to decreasing the number of points assigned to punctate echogenic foci in mixed solid and cystic thyroid nodules, given the substantial decrease in the number of benign nodules requiring biopsy and the recommendation of follow-up for any carcinoma 1 cm or larger that did not undergo biopsy.

Awareness of implicit bias mitigates discrimination in radiology resident selection.

OBJECTIVES: Implicit bias is common and is thought to drive discriminatory behaviour. Having previously demonstrated discrimination against specific applicant demographics by academic radiology departments in a simulated resident selection process, the authors sought to better understand the relationship between implicit bias and discrimination, as well as the potential and mechanisms for their mitigation. METHODS: A total of 51 faculty reviewers at three academic radiology departments, who had participated in a 2017 audit study in which they were shown to treat applicants differently based on race or ethnicity and physical appearance, were invited to complete testing for implicit racial and weight bias using the Implicit Association Test in 2019. Respondents were also surveyed regarding awareness of their own personal racial and weight biases, as well as any prior participation in formal diversity training. Comparisons were made between implicit bias scores and applicant ratings, as well as between diversity training and self-awareness of bias. RESULTS: A total of 31 out of 51 faculty reviewers (61%) completed and submitted results of race and weight Implicit Association Tests. A total of 74% (23/31) reported implicit anti-obese bias, concordant with discrimination demonstrated in the resident selection simulation, in which obese applicants were rated 0.40 standard deviations (SDs) lower than non-obese applicants (P < .001). A total of 71% (22/31) reported implicit anti-Black bias, discordant with application ratings, which were 0.47 SDs higher for Black than for White applicants (P < .001). A total of 84% (26/31) of participants reported feeling self-aware of potential racial bias at the time of application review, significantly higher than the 23% (7/31) reporting self-awareness of potential anti-obese bias (P < .001). Participation in formal diversity training was not associated with implicit anti-Black or anti-fat bias, nor with self-reported awareness of potential racial or weight-based bias (all P > .2). CONCLUSIONS: These findings suggest that implicit bias, as measured by the Implicit Association Test, does not inevitably lead to discrimination, and that personal awareness of implicit biases may allow their mitigation.

Quantitative Framework for Risk Stratification of Thyroid Nodules With Ultrasound: A Step Toward Automated Triage of Thyroid Cancer.

OBJECTIVE. The purpose of this study was to explore whether a quantitative framework can be used to sonographically differentiate benign and malignant thyroid nodules at a level comparable to that of experts. MATERIALS AND METHODS. A dataset of ultrasound images of 92 biopsy-confirmed nodules was collected retrospectively. The nodules were delineated and annotated by two expert radiologists using the standardized Thyroid Imaging Reporting and Data System lexicon of the American College of Radiology. In the framework studied, quantitative features of echogenicity, texture, edge sharpness, and margin curvature properties of thyroid nodules were analyzed in a regularized logistic regression model to predict malignancy of a nodule. The framework was validated by leave-one-out cross-validation technique, and ROC AUC, sensitivity, and specificity were used to compare with those obtained with six expert annotation-based classifiers. RESULTS. The AUC of the proposed method was 0.828 (95% CI, 0.715-0.942), which was greater than or comparable to that of the expert classifiers, for which the AUC values ranged from 0.299 to 0.829 (p = 0.99). Use of the proposed framework could have avoided biopsy of 20 of 46 benign nodules in a curative strategy (at sensitivity of 1, statistically significantly higher than three expert classifiers) or helped identify 10 of 46 malignancies in a conservative strategy (at specificity of 1, statistically significantly higher than five expert classifiers). CONCLUSION. When the proposed quantitative framework was used, thyroid nodule malignancy was predicted at the level of expert classifiers. Such a framework may ultimately prove useful as the basis for a fully automated system of thyroid nodule triage.

Bladder cancer and its mimics: a sonographic pictorial review with CT/MR and histologic correlation.

Bladder cancer is the most common cancer of the urinary system and often presents with hematuria. Despite its relatively high incidence, bladder cancer is often under-recognized sonographically. Moreover, even when bladder abnormalities are identified, numerous other entities may mimic the appearance of bladder cancer. Given the incidence and prevalence of bladder cancer, it is important to recognize its variable appearance sonographically and distinguish it from its common mimics. We review the sonographic appearance of bladder cancer and its mimics, providing correlative CT/MR imaging as well as pathology. We stress the importance and advantage of ultrasound as a dynamic imaging modality, with the ability to optimize distinguishing bladder cancer from similar-appearing entities.

Bias in Radiology Resident Selection: Do We Discriminate Against the Obese and Unattractive?

PURPOSE: To evaluate for appearance-based discrimination in the selection of radiology residents. METHOD: A deception study simulating the resident selection process examined the impact of attractiveness and obesity on resident selection. Seventy-four core faculty from 5 academic radiology departments reviewed mock residency applications in September and October 2017. Each application included demographic information and a photograph, representing a prespecified distribution of facial attractiveness and obesity, combined with randomized academic and supporting variables. Reviewers independently scored applications for interview desirability. Reviewer scores and application variables were compared using linear mixed fixed- and random-effects models. RESULTS: Reviewers evaluated 5,447 applications (mean: 74 applications per reviewer). United States Medical Licensing Examination Step 1 scores were the strongest predictor of reviewer rating (B = 0.35 [standard error (SE) = 0.029]). Applicant facial attractiveness strongly predicted rating (attractive vs unattractive, B = 0.30 [SE = 0.056]; neutral vs unattractive, B = 0.13 [SE = 0.028]). Less influential but still significant predictors included race/ethnicity (B = 0.25 [SE = 0.059]), preclinical class rank (B = 0.25 [SE = 0.040]), clinical clerkship grades (B = 0.23 [SE = 0.034]), Alpha Omega Alpha membership (B = 0.21 [SE = 0.032]), and obesity (vs not obese) (B = -0.14 [SE = 0.024]). CONCLUSIONS: Findings provide preliminary evidence of discrimination against facially unattractive and obese applicants in radiology resident selection. Obesity and attractiveness were as influential in applicant selection for interview as traditional medical school performance metrics. Selection committees should invoke strategies to detect and manage appearance-based bias.

Ultrasound Assessment of Acute Kidney Injury.

Ultrasound assessment of the kidneys in patients with renal impairment has been described in various ways in the critical care, nephrology, and radiology literature, resulting in a somewhat heterogeneous picture of the gray-scale and Doppler ultrasound manifestation of acute kidney injury (AKI). Given that ultrasound assessment can potentially identify reversible causes of AKI or identify underlying chronic kidney disease, it is important for radiologists to be aware of the common etiologies of AKI and the spectrum of ultrasound findings. We review the definition and etiologies of renal injury and introduce the ultrasound SERVeillance framework-assessment of renal size, echogenicity, renal hilum, and vascularity-for the imaging assessment of AKI.