MRI with DWI improves detection of liver metastasis and selection of surgical candidates with pancreatic cancer: a systematic review and meta-analysis.

OBJECTIVE: To perform a systematic review and meta-analysis to evaluate if magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI) adds value compared to contrast-enhanced computed tomography (CECT) alone in the preoperative evaluation of pancreatic cancer. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched for relevant published studies through October 2022. Studies met eligibility criteria if they evaluated the per-patient diagnostic performance of MRI with DWI in the preoperative evaluation of newly diagnosed pancreatic cancer compared to CECT. Our primary outcome was the number needed to treat (NNT) to prevent one futile surgery using MRI with DWI, defined as those in which CECT was negative and MRI with DWI was positive for liver metastasis (i.e., surgical intervention in metastatic disease missed by CECT). The secondary outcomes were to determine the diagnostic performance and the NNT of MRI with DWI to change management in pancreatic cancer. RESULTS: Nine studies met the inclusion criteria with a total of 1121 patients, of whom 172 had liver metastasis (15.3%). The proportion of futile surgeries reduced by MRI with DWI was 6.0% (95% CI, 3.0-11.6%), yielding an NNT of 16.6. The proportion of cases that MRI with DWI changed management was 18.1% (95% CI, 9.9-30.7), corresponding to an NNT of 5.5. The per-patient sensitivity and specificity of MRI were 92.4% (95% CI, 87.4-95.6%) and 97.3% (95% CI, 96.0-98.1). CONCLUSION: MRI with DWI may prevent futile surgeries in pancreatic cancer by improving the detection of occult liver metastasis on preoperative CECT with an NNT of 16.6. CLINICAL RELEVANCE STATEMENT: MRI with DWI complements the standard preoperative CECT evaluation for liver metastasis in pancreatic cancer, improving the selection of surgical candidates and preventing unnecessary surgeries. KEY POINTS: • The NNT of MRI with DWI to prevent potential futile surgeries due to occult liver metastasis on CECT, defined as those in which CECT was negative and MRI with DWI was positive for liver metastasis, in patients with pancreatic cancer was 16.6. • The higher performance of MRI with DWI to detect liver metastasis occult on CECT can be attributed to an increased detection of subcentimeter liver metastasis.

A New (Digital) Era in Medical Journalism: Leveraging Social Media and Other Online Tools to Increase Reach and Engagement.

Social media is a popular communication and marketing tool in modern society, with the power to reach and engage large audiences. Many members of the medical and radiology communities have embraced social media platforms, particularly X (formerly known as Twitter), as an efficient and economic means for performing patient outreach, disseminating research and educational materials, building networks, and promoting diversity. Editors of medical journals with a clear vision and relevant expertise can leverage social media and other digital tools to advance the journal's mission, further their interests, and directly benefit journal authors and readers. For editors, social media offers a means to increase article visibility and downloads, expand awareness of volunteer opportunities, and use metrics and other feedback to inform future initiatives. Authors benefit from broader dissemination of their work, which aids establishment of a national or international reputation. Readers can receive high-quality high-yield content in a digestible format directly on their devices while actively engaging with journal editors and authors in the online community. The authors highlight the multifaceted benefits of social media engagement and digital tool implementation in the context of medical journalism and summarize the activities of the RadioGraphics Social Media and Digital Innovation Team. By enumerating the social media activities of RadioGraphics and describing the underlying rationale for each activity, the authors present a blueprint for other medical journals considering similar initiatives. ©RSNA, 2023.

Performing Quality Control on Magnetic Resonance Imaging Liver Fat/Iron Quantification Studies: A Critical Requirement.

OBJECTIVE: Nonalcoholic fatty liver and iron overload can lead to cirrhosis requiring early detection. Magnetic resonance (MR) imaging utilizing chemical shift-encoded sequences and multi-Time of Echo single-voxel spectroscopy (SVS) are frequently used for assessment. The purpose of this study was to assess various quality factors of technical acceptability and any deficiencies in technologist performance in these fat/iron MR quantification studies. METHODS: Institutional review board waived retrospective quality improvement review of 87 fat/iron MR studies performed over a 6-month period was evaluated. Technical acceptability/unacceptability for chemical shift-encoded sequences (q-Dixon and IDEAL-IQ) included data handling errors (missing maps), liver field coverage, fat/water swap, motion, or other artifacts. Similarly, data handling (missing table/spectroscopy), curve-fit, fat- and water-peak separation, and water-peak sharpness were evaluated for SVS technical acceptability. RESULTS: Data handling errors were found in 11% (10/87) of studies with missing maps or entire sequence (SVS or q-Dixon). Twenty-seven percent (23/86) of the q-Dixon/IDEAL-IQ were technically unacceptable (incomplete liver-field [39%], other artifacts [35%], significant/severe motion [18%], global fat/water swap [4%], and multiple reasons [4%]). Twenty-eight percent (21/75) of SVS sequences were unacceptable (water-peak broadness [67%], poor curve-fit [19%] overlapping fat and water peaks [5%], and multiple reasons [9%]). CONCLUSIONS: A high rate of preventable errors in fat/iron MR quantification studies indicates the need for routine quality control and evaluation of technologist performance and technical deficiencies that may exist within a radiology practice. Potential solutions such as instituting a checklist for technologists during each acquisition procedure and routine auditing may be required.

Are Academic Emergency Radiologists Systematically Disadvantaged Compared With Diagnostic Radiology Subspecialty Counterparts When It Comes to Promotion?

PURPOSE: The aim of this study was to assess academic rank differences between academic emergency and other subspecialty diagnostic radiologists. METHODS: Academic radiology departments likely containing emergency radiology divisions were identified by inclusively merging three lists: Doximity's top 20 radiology programs, the top 20 National Institutes of Health-ranked radiology departments, and all departments offering emergency radiology fellowships. Within departments, emergency radiologists (ERs) were identified via website review. Each was then matched on career length and gender to a same-institutional nonemergency diagnostic radiologist. RESULTS: Eleven of 36 institutions had no ERs or insufficient information for analysis. Among 283 emergency radiology faculty members from 25 institutions, 112 career length- and gender-matched pairs were included. Average career length was 16 years, and 23% were women. The mean h indices for ERs and non-ERs were 3.96 ± 5.60 and 12.81 ± 13.55, respectively (P < .0001). Non-ERs were twice as likely as ERs (0.21 versus 0.1) to be associate professors at h index < 5. Men had nearly 3 times the odds of advanced rank compared with women (odds ratio, 2.91; 95% confidence interval, 1.02-8.26; P = .045). Radiologists with at least one additional degree had nearly 3 times the odds of advancing rank (odds ratio, 2.75; 95% confidence interval, 1.02-7.40; P = .045). Each additional year of practice increased the odds of advancing rank by 14% (odds ratio, 1.14; 95% confidence interval, 1.08-1.21; P < .001). CONCLUSIONS: Academic ERs are less likely to achieve advanced rank compared with career length- and gender-matched non-ERs, and this persists even after adjusting for h index, suggesting that academic ERs are disadvantaged in current promotions systems. Longer term implications for staffing and pipeline development merit further attention as do parallels to other nonstandard subspecialties such as community radiology.

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports.

PURPOSE: To evaluate the complexity of diagnostic radiology reports across major imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, California, USA) to simplify these reports to the 8th grade reading level of the average U.S. adult. METHODS: We randomly sampled 100 radiographs (XR), 100 ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT using the prompt "Explain this radiology report to a patient in layman's terms in second person: ". Mean report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report and ChatGPT output. T-tests were used to determine significance. RESULTS: Mean report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5. DISCUSSION: Our study demonstrates the effective use of ChatGPT when tasked with simplifying radiology reports to below the 8th grade reading level. We report significant improvements in FRES, FKRL, and word count, the last of which requires modality-specific context.

Sociodemographic Variables Reporting in Human Radiology Artificial Intelligence Research.

PURPOSE: Artificial intelligence (AI) is rapidly reshaping how radiology is practiced. Its susceptibility to biases, however, is a primary concern as more AI algorithms become available for widespread use. So far, there has been limited evaluation of how sociodemographic variables are reported in radiology AI research. This study aims to evaluate the presence and extent of sociodemographic reporting in human subjects radiology AI original research. METHODS: All human subjects original radiology AI articles published from January to December 2020 in the top six US radiology journals, as determined by impact factor, were reviewed. Reporting of any sociodemographic variables (age, gender, and race or ethnicity) as well as any sociodemographic-based results were extracted. RESULTS: Of the 160 included articles, 54% reported at least one sociodemographic variable, 53% reported age, 47% gender, and 4% race or ethnicity. Six percent reported any sociodemographic-based results. There was significant variation in reporting of at least one sociodemographic variable by journal, ranging from 33% to 100%. CONCLUSIONS: Reporting of sociodemographic variables in human subjects original radiology AI research remains poor, putting the results and subsequent algorithms at increased risk of biases.

Results of the 2020 Survey of the American Alliance of Academic Chief Residents in Radiology.

RATIONALE AND OBJECTIVES: An annual survey of chief residents in accredited North American radiology programs is conducted by the American Alliance of Academic Chief Residents in Radiology (A3CR2). The purpose of this study is to summarize the 2020 A3CR2 chief resident survey. MATERIALS AND METHODS: An online survey was distributed to chief residents from 194 Accreditation Council on Graduate Medical Education-accredited radiology residencies. Questions were designed to gather information about residency program practices, benefits, fellowship or advanced interventional radiology (IR) training choices, and the integration of IR training. Subsets of questions focused on the perception of corporatization, non-physician providers (NPPs), and artificial intelligence (AI) in radiology and their relationship to the radiology job market. RESULTS: 174 individual responses from 94 programs were provided, yielding a 48 % program response rate. Extended emergency department coverage has steadily decreased over the last 5 years (2016-2020), however only 52 % of programs have independent overnight call (without attending coverage). Regarding the impact of new integrated IR residencies on training, 42 % indicated there was no appreciable impact on their DR or IR training, while 20 % indicated DR training for IR residents suffered and 19 % indicated IR training for DR residents suffered. Corporatization in radiology was perceived as the biggest potential threat to the future job market. CONCLUSIONS: Integration of IR residency did not detrimentally affect DR or IR training in most programs. Radiology resident perception of corporatization, NPPs, and AI may help residency programs shape educational content.

Abdominal Imaging Findings on Computed Tomography as a Tool for COVID-19 Mortality Risk Assessment: Comparison With Chest Radiograph Severity Scores.

OBJECTIVE: To quantify the association between computed tomography abdomen and pelvis with contrast (CTAP) findings and chest radiograph (CXR) severity score, and the incremental effect of incorporating CTAP findings into predictive models of COVID-19 mortality. METHODS: This retrospective study was performed at a large quaternary care medical center. All adult patients who presented to our institution between March and June 2020 with the diagnosis of COVID-19 and had a CXR up to 48 hours before a CTAP were included. Primary outcomes were the severity of lung disease before CTAP and mortality within 14 and 30 days. Logistic regression models were constructed to quantify the association between CXR score and CTAP findings. Penalized logistic regression models and random forests were constructed to identify key predictors (demographics, CTAP findings, and CXR score) of mortality. The discriminatory performance of these models, with and without CTAP findings, was summarized using area under the characteristic (AUC) curves. RESULTS: One hundred ninety-five patients (median age, 63 years; 119 men) were included. The odds of having CTAP findings was 3.89 times greater when a CXR score was classified as severe compared with mild (P = 0.002). When CTAP findings were included in the feature set, the AUCs for 14-day mortality were 0.67 (penalized logistic regression) and 0.71 (random forests). Similar values for 30-day mortality were 0.76 and 0.75. When CTAP findings were omitted, all AUC values were attenuated. CONCLUSIONS: The CTAP findings were associated with more severe CXR score and may serve as predictors of COVID-19 mortality.

Imaging Informatics Fellowship Curriculum: Building Consensus on the Most Critical Topics and the Future of the Informatics Fellowship.

The existing fellowship imaging informatics curriculum, established in 2004, has not undergone formal revision since its inception and inaccurately reflects present-day radiology infrastructure. It insufficiently equips trainees for today's informatics challenges as current practices require an understanding of advanced informatics processes and more complex system integration. We sought to address this issue by surveying imaging informatics fellowship program directors across the country to determine the components and cutline for essential topics in a standardized imaging informatics curriculum, the consensus on essential versus supplementary knowledge, and the factors individual programs may use to determine if a newly developed topic is an essential topic. We further identified typical program structural elements and sought fellowship director consensus on offering official graduate trainee certification to imaging informatics fellows. Here, we aim to provide an imaging informatics fellowship director consensus on topics considered essential while still providing a framework for informatics fellowship programs to customize their individual curricula.

Utilization of Chest and Abdominopelvic CT for Traumatic Injury From 2011 to 2018: Evaluation Using a National Commercial Database.

BACKGROUND. Increases in the use of CT to evaluate patients presenting with trauma have raised concern about inappropriate imaging. The evolving utilization of CT for trauma evaluation may be impacted by injury severity. OBJECTIVE. The purpose of this study was to explore patterns in utilization of chest and abdominopelvic CT among trauma-related emergency department (ED) visits across the United States. METHODS. This retrospective study was conducted with national commercial claims information extracted from the MarketScan Commercial Database. Trauma-related ED encounters were identified from the 2011-2018 MarketScan database files and classified by injury severity score (minor, intermediate, and major injuries) on the basis of International Classification of Diseases codes. ED encounters were also assessed for chest CT, abdominopelvic CT, and single-encounter chest and abdominopelvic CT examinations. Utilization per 1000 trauma-related ED encounters was determined. Multivariable Poisson regression models were used to determine incidence rate ratios (IRRs) as a measure of temporal changes in utilization. RESULTS. From 2011 to 2018, 8,369,092 trauma-related ED encounters were identified (5,685,295 for minor, 2,624,944 for intermediate, and 58,853 for major injuries). Utilization of chest CT per 1000 trauma-related ED encounters increased from 4.9 to 13.5 examinations (adjusted IRR, 1.15 per year; minor injuries, from 2.2 to 7.7 [adjusted IRR, 1.17]; intermediate injuries, from 8.5 to 21.5 [adjusted IRR, 1.16]; major injuries, from 117.8 to 200.1 [adjusted IRR, 1.08]). Utilization of abdominopelvic CT per 1000 trauma-related ED encounters increased from 7.5 to 16.4 (adjusted IRR, 1.12; minor injuries, 4.8 to 12.2 [adjusted IRR, 1.13]; intermediate injuries, 10.6 to 21.7 [adjusted IRR, 1.13]; major injuries, 134.8 to 192.6 [adjusted IRR, 1.07]). Utilization of single-encounter chest and abdominopelvic CT per 1000 trauma-related ED encounters increased from 3.4 to 8.9 [adjusted IRR, 1.16; minor injuries, 1.1 to 4.6 [adjusted IRR, 1.18]; intermediate injuries, 6.4 to 16.4 [adjusted IRR, 1.16]; major injuries, 99.6 to 179.9 [adjusted IRR, 1.08]). CONCLUSION. National utilization of chest and abdominopelvic CT for trauma-related ED encounters increased among commercially insured patients from 2011 to 2018, particularly for single-encounter chest and abdominopelvic CT examinations and for minor injuries. CLINICAL IMPACT. Given concerns about increased cost and detection of incidental findings, further investigation is warranted to explore the potential benefit of single-encounter chest and abdominopelvic CT examinations of patients with minor injuries and to develop strategies for optimizing appropriateness of imaging orders.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results: 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

Intellectual property: A primer for radiologists.

Typically the creative product of the mind, intellectual property often forms the basis of a new product, service line, or company. Intellectual property law is complicated and nuanced, and poorly understood by many physicians, innovators, and entrepreneurs. Successfully navigating the process of intellectual property protection is critical in facilitating the translation of innovation into clinical practice. We define intellectual property and common terms used in intellectual property law and offer justification for the importance of intellectual property protections. We additionally highlight resources to assist radiologists with intellectual property protection and outline basic guidelines to successfully initiate discussions around intellectual property with third party vendors and consultants. SUMMARY: Proactive intellectual property protection is critically important for radiologist innovators seeking to bring new ideas to the marketplace.

Effect of Sociodemographic Factors on Utilization of an Online Patient Portal to Self-Schedule Screening Mammography: A Cross-Sectional Study.

PURPOSE: The aim of this study was to assess for sociodemographic factors associated with the use of an online patient portal to self-schedule screening mammography (SM) compared with the traditional scheduling pathway (phone call and referral system). METHODS: A retrospective study was conducted at an urban quaternary care academic medical center with patient portal access to the electronic health record. All female patients undergoing SM at the institution from January 1, 2019, to December 31, 2019, were included. The institutional data warehouse was queried to extract the following variables: patient scheduling pathway (online self-scheduled vs traditional), age, language, race/ethnicity, health insurance provider, and ZIP code. ZIP code was linked to census data to extract the following: computer with Internet access, median household income, and education level. Multivariable logistic regression was used to identify independent factors associated with using the online self-scheduling pathway for SM. RESULTS: A total of 46,083 patients met the inclusion criteria. Three hundred two (0.7%) used the online self-scheduling pathway. Patients using the online self-scheduling pathway had higher odds of being younger (odds ratio [OR] for age in years, 0.94; 95% confidence interval [CI], 0.93-0.96), being English speakers (OR, 21.3; 95% CI, 2.9-153.9), being White non-Hispanic (OR, 1.7; 95% CI, 1.2-2.5), and having commercial insurance (OR, 1.5; 95% CI, 1.1-2.1). Patients using the online self-scheduling pathway had higher odds of living in ZIP-code areas with higher access to computers with Internet connection (OR, 1.04; 95% CI, 1.01-1.07) and lower rates of education at or above the college level (OR, 0.98; 95% CI, 0.97-1.00). Patient median household income by ZIP code was not significantly associated with use of the online self-scheduling pathway. CONCLUSIONS: Patients with limited English proficiency, those of racial/ethnic minorities, those who were older, and those without commercial insurance were less likely to use an online self-scheduling pathway for SM.

Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.

BACKGROUND: Transfer learning is a common practice in image classification with deep learning where the available data is often limited for training a complex model with millions of parameters. However, transferring language models requires special attention since cross-domain vocabularies (e.g. between two different modalities MR and US) do not always overlap as the pixel intensity range overlaps mostly for images. METHOD: We present a concept of similar domain adaptation where we transfer inter-institutional language models (context-dependent and context-independent) between two different modalities (ultrasound and MRI) to capture liver abnormalities. RESULTS: We use MR and US screening exam reports for hepatocellular carcinoma as the use-case and apply the transfer language space strategy to automatically label imaging exams with and without structured template with > 0.9 average f1-score. CONCLUSION: We conclude that transfer learning along with fine-tuning the discriminative model is often more effective for performing shared targeted tasks than the training for a language space from scratch.