Faculty Bibliography

Each year, the healthcare industry generates a substantial amount of data, and radiology stands out as a medical specialty that often produces significant volumes of data. This is due to the large amount of data contained within imaging studies such CT scans, MRIs, and radiographs. As the demand for imaging services grows, practices encounter more patients and perform more scans, leading to an ever-growing volume of generated data. To sustain the growing volume of data and to adhere to data privacy regulations, radiology groups require a thoughtfully designed data governance program to oversee data availability, usability, integrity, and security. Data governance, when effectively implemented, safeguards the consistency and trustworthiness of data, reducing risk of misuse. This review summarizes various concepts and practices related to data governance in radiology.

Effective data governance has potential to enhance clinical radiology practice and facilitate radiologic research and education. This article will review specific topics in data governance relevant to radiology, with emphasis on innovative approaches and adaptations that may advance radiology in the near future. The potential value of radiology images and reports for AI and other research opportunities is discussed, as are mechanisms to facilitate radiologic data access and exchange, including federated data access to facilitate research or large-scale quality monitoring while preserving data privacy. Data governance also has application to radiology education, including limitations and privacy/confidentiality issues related to access by radiology learners. Innovative approaches to radiology report generation and other modifications to radiology workflow could add to radiologist efficiency and maximize accuracy or other quality measures of radiology data.

Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. ^©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are relatively common breast lesions on the same spectrum of disease. Atypical ductal hyperblasia is a nonmalignant, high-risk lesion, and DCIS is a noninvasive malignancy. While a benefit of screening mammography is early cancer detection, it also leads to increased biopsy diagnosis of noninvasive lesions. Previously, treatment guidelines for both entities included surgical excision because of the risk of upgrade to invasive cancer after surgery and risk of progression to invasive cancer for DCIS. However, this universal management approach is not optimal for all patients because most lesions are not upgraded after surgery. Furthermore, some DCIS lesions do not progress to clinically significant invasive cancer. Overtreatment of high-risk lesions and DCIS is considered a burden on patients and clinicians and is a strain on the health care system. Extensive research has identified many potential histologic, clinical, and imaging factors that may predict ADH and DCIS upgrade and thereby help clinicians select which patients should undergo surgery and which may be appropriate for active surveillance (AS) with imaging. Additionally, multiple clinical trials are currently underway to evaluate whether AS for DCIS is feasible for a select group of patients. Recent advances in MRI, artificial intelligence, and molecular markers may also have an important role to play in stratifying patients and delineating best management guidelines. This review article discusses the available evidence regarding the feasibility and limitations of AS for ADH and DCIS, as well as recent advances in patient risk stratification.

Women who survived childhood cancers or cancers at a young age are at high risk for breast cancer later in life. The accentuated risk is notable among those treated at a young age with a high radiation dose but also extends to survivors treated with therapies other than or in addition to radiation therapy. The predisposing risk factors are complex. Advances in radiation therapy continue to curtail exposure, yet the risk of a second cancer has no dose threshold and a long latency period, and concurrent use of chemotherapy may have an additive effect on long-term risk of cancer. Early screening with annual mammography and MRI is recommended for chest radiation exposure of 10 Gy or greater, beginning 8 years after treatment or at age 25 years, whichever is later. However, there is a lack of recommendations for those at high risk without a history of radiation therapy. Because mortality after breast cancer among survivors is higher than in women with de novo breast cancer, and because there is a higher incidence of a second asynchronous breast cancer in survivors than that in the general population, regular screening is essential and is expected to improve mortality. However, awareness and continuity of care may be lacking in these young patients and is reflected in their poor screening attendance. The transition of care from childhood to adulthood for survivors requires age-targeted and lifelong strategies of education and risk prevention that are needed to improve long-term outcomes for these patients. ^© RSNA, 2023 See the invited commentary by Chikarmane in this issue. Quiz questions for this article are available through the Online Learning Center.

RATIONALE AND OBJECTIVES/OBJECTIVE:Meeting the Accreditation Council for Graduate Medical Education scholarly activity requirement can be challenging for residents. Time to engage in research is one of the commonly perceived barriers. To address this barrier, our residency program implemented a focused academic time initiative of a half day per week that can be taken while on rotation. At the end of the third year of implementation, we assessed the effectiveness of this initiative on the productivity of resident scholarly activity. MATERIALS AND METHODS/METHODS:Radiology resident scholarly activity submitted to the Accreditation Council for Graduate Medical Education web-based Accreditation Data System were reviewed and compared to the three academic years before (July 1, 2012-June 30, 2015) and three academic years after (July 1, 2015-June 30, 2018) implementing the focused research time. The types of scholarly activity, which consisted of peer-reviewed journal publications, national conference presentations, and textbook chapters were captured. PubMed-Indexed for MEDLINE (PMID) number was used to confirm publications. Descriptive statistics were used to analyze the data. RESULTS:The total number of residents per year, ranging between 37-40, was similar between the academic years 2012-2015 (116 residents total) and 2015-2018 (117 residents total). After initiating focused academic time, the number of publications increased from 45 to 75 (67%), presentations at conferences increased from 112 to 128 (14%), the number of textbook chapters increased from 4 to 15 (275%), and total number of first author publications by residents increased from 21 to 28 (33% increase). CONCLUSION/CONCLUSIONS:Longitudinal focused academic time of half a day per week increased productivity of scholarly activity among our radiology residents.

RATIONALE AND OBJECTIVES/OBJECTIVE:The Covid-19 pandemic ushered a sudden need for residency programs to develop innovative socially distant and remote approaches to effectively promote their program. Here we describe our experience using the social virtual reality (VR) platform Mozilla Hubs for the pre-interview social during the 2020-2021 radiology residency virtual recruitment season, provide results of a survey sent to assess applicants' attitudes towards the VR pre-interview social, and outline additional use-cases for the emerging technology. MATERIALS AND METHODS/METHODS:A VR Meeting Hall dedicated to the pre-interview social was designed in Mozilla Hubs. To assess applicants' impressions of the Mozilla Hubs pre-interview social, applicants were sent an optional web-based survey. Survey respondents were asked to respond to a series of eleven statements using a five-point Likert scale of perceived agreement: Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree. Statements were designed to gauge applicants' attitudes towards the Mozilla Hubs pre-interview social and its usefulness in helping them learn about the residency program, particularly in comparison with pre-interview socials held on conventional video conferencing software (CVCS). RESULTS:Of the 120 residency applicants invited to the Mozilla Hubs pre-interview social, 111 (93%) attended. Of these, 68 (61%) participated in the anonymous survey. Most applicants reported a better overall experience with Mozilla Hubs compared to CVCS (47/68, 69%), with 10% (7/68) reporting a worse overall experience, and 21% (14/68) neutral. Most applicants reported the Mozilla Hubs pre-interview social allowed them to better assess residency culture than did pre-interview socials using CVCS (41/68, 60%). Seventy-two percent of applicants reported that the Mozilla Hubs pre-interview social positively impacted their decision to strongly consider the residency program (49/68). CONCLUSION/CONCLUSIONS:Radiology residency applicants overall preferred a pre-interview social hosted on a social VR platform, Mozilla Hubs, compared to those hosted on CVCS. Applicants reported the use of a social VR platform reflected positively on the residency and positively impacted their decision to strongly consider the program.

Neoadjuvant therapy may reduce tumor burden preoperatively, allowing breast conservation treatment for tumors previously unresectable or requiring mastectomy without reducing disease-free survival. Oncologists can also use the response of the tumor to neoadjuvant chemotherapy (NAC) to identify treatment likely to be successful against any unknown potential distant metastasis. Accurate preoperative estimations of tumor size are necessary to guide appropriate treatment with minimal delays and can provide prognostic information. Clinical breast examination and mammography are inaccurate methods for measuring tumor size after NAC and can over- and underestimate residual disease. While US is commonly used to measure changes in tumor size during NAC due to its availability and low cost, MRI remains more accurate and simultaneously images the entire breast and axilla. No method is sufficiently accurate at predicting complete pathological response that would obviate the need for surgery. Diffusion-weighted MRI, MR spectroscopy, and MRI-based radiomics are emerging fields that potentially increase the predictive accuracy of tumor response to NAC.

Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson's paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison.

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.