Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Impact of a structured reporting template on the quality of HRCT radiology reports for interstitial lung disease.

PURPOSE: This QI study compared the completeness of HRCT radiology reports before and after the implementation of a disease-specific structured reporting template for suspected cases of interstitial lung disease (ILD). MATERIALS AND METHODS: A pre-post study of radiology reports for HRCT of the thorax at a multicenter health system was performed. Data was collected in 6-month period intervals before (June 2019-November 2019) and after (January 2021-June 2021) the implementation of a disease-specific template. The use of the template was voluntary. The primary outcome measure was the completeness of HRCT reports graded based on the documentation of ten descriptors. The secondary outcome measure assessed which descriptor(s) improved after the intervention. RESULTS: 521 HRCT reports before and 557 HRCT reports after the intervention were reviewed. Of the 557 reports, 118 reports (21%) were created using the structured reporting template. The mean completeness score of the pre-intervention group was 9.20 (SD = 1.08) and the post-intervention group was 9.36 (SD = 1.03) with a difference of -0.155, 95% CI [-0.2822, -0.0285, p < 0.0001]. Within the post-intervention group, the mean completeness score of the unstructured reports was 9.25 (SD = 1.07) and the template reports was 9.93 (SD = 0.25) with a difference of -0.677, 95% CI [-0.7871, -0.5671, p < 0.0001]. After the intervention, the use of two descriptors improved significantly: presence of honeycombing from 78.3% to 85.1% (p < 0.0039) and technique from 90% to 96.6% (p < 0.0001). DISCUSSION: Shifting to disease-specific structured reporting for HRCT exams of suspected ILD is beneficial, as it improves the completeness of radiology reports. Further research on how to improve the voluntary uptake of a disease-specific template is needed to help increase the acceptance of structured reporting among radiologists.

Radiologic Technologists' Perceived Reasons for Inappropriate Imaging.

PURPOSE: To examine whether radiologic technologists' perceptions of imaging appropriateness differed based on their primary imaging modality, work shift, shift length, and primary practice type. METHODS: A national, cross-sectional study was conducted in the fourth quarter of 2019 using a simple, randomized sample of American Society of Radiologic Technologists (ASRT) members. Study participants were employed in health care settings in radiography, computed tomography (CT), mammography, or radiology leadership. Seven potential reasons for inappropriate imaging procedures (ie, patient expectations, provide patient with a feeling of being taken seriously, lack of time, expectations from relatives, compensation for insufficient clinical examination, normal findings would reassure the patient, and fear of lawsuits) were evaluated for relationships with their primary imaging modality, work shift, shift length, and primary practice type. RESULTS: Disparities in perceived reasons affecting imaging appropriateness were found. Providing the patient with a feeling of being taken seriously was related to primary practice type (P = .022). Lack of time was related to primary imaging modality (P = .005) and primary practice type (P = .006). Expectations from relatives was related to primary imaging modality (P = .016) and primary practice type (P = .027). Compensation for insufficient clinical examination was related to primary imaging modality (P < .001), shift length (P = .011), work shift (P = .002), and primary practice type (P < .001). Fear of lawsuits was related to primary imaging modality (P = .001)) and work shift (P = .002). DISCUSSION: The study reveals that radiologic technologists' perceptions of patient-centered factors and defensive medicine-related factors differ among imaging modalities, shift types, and practice settings. However, more research is required to determine why radiologic technologists perceive these reasons to be present, investigate whether providers feel similarly, and determine perceptual alignment with evidence-based guidelines. CONCLUSION: The findings suggest that attention should focus on the appropriateness of CT imaging procedures performed in hospitals during night shifts.

Role of Ergonomic Improvements in Decreasing Repetitive Stress Injuries and Promoting Well-Being in a Radiology Department.

RATIONALE AND OBJECTIVES: To determine if ergonomic improvements in a radiology department can decrease repetitive stress injuries (RSIs), advance ergonomics knowledge, and improve well-being. MATERIALS AND METHODS: Radiologists in an academic institution were surveyed regarding physician wellness, workstations, RSIs, and ergonomics knowledge before and after interventions over 1 year. Interventions included committee formation, education, wrist pads and wireless mice, broken table and chair replacement, and cord organization. Mann-Whitney U test was used for analysis. RESULTS: Survey response was 40% preinterventions (59/147), and 42% (66/157) postinterventions. Preinterventions, of radiologists with RSI history, 17/40 (42%) reported the RSI caused symptoms which can lead to burnout, and 15/40 (37%) responded their RSI made them think about leaving their job. Twenty-three of 59 (39%) radiologists had an active RSI preinterventions. Postinterventions, 9/25 (36%) RSI resolved, 13/25 (52%) RSI improved, and 3/25 (12%) RSI did not improve. RSI improvements were attributed to ergonomic interventions in 19/25 (76%) and therapy in 2/25 (8%). Radiologists who thought their workstation was designed with well-being in mind increased from 9/59 (15%) to 52/64 (81%). The percentage of radiologists knowing little or nothing about ergonomics decreased from 15/59 (25%) to 5/64 (8%). After ergonomics interventions, more radiologists thought the administration cared about safety and ergonomics, equipment was distributed fairly, and radiologists had the ability to ask for equipment (p < .01). Fifty-three of 64 (83%) of radiologists after interventions said improving workstation ergonomic design contributed to well-being. CONCLUSION: Ergonomic improvements in radiology can decrease RSIs, advance ergonomics knowledge, and improve well-being.

Artificial Intelligence for Quality Improvement in Radiology.

Artificial intelligence (AI) and informatics promise to improve the quality and efficiency of diagnostic radiology but will require substantially more standardization and operational coordination to realize and measure those improvements. As radiology steps into the AI-driven future we should work hard to identify the needs and desires of our customers and develop process controls to ensure we are meeting them. Rather than focusing on easy-to-measure turnaround times as surrogates for quality, AI and informatics can support more comprehensive quality metrics, such as ensuring that reports are accurate, readable, and useful to patients and health care providers.

Separating Hope from Hype: Artificial Intelligence Pitfalls and Challenges in Radiology.

Although recent scientific studies suggest that artificial intelligence (AI) could provide value in many radiology applications, much of the hard engineering work required to consistently realize this value in practice remains to be done. In this article, we summarize the various ways in which AI can benefit radiology practice, identify key challenges that must be overcome for those benefits to be delivered, and discuss promising avenues by which these challenges can be addressed.

The reporting quality of natural language processing studies: systematic review of studies of radiology reports.

BACKGROUND: Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. METHODS: We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. RESULTS: Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. CONCLUSIONS: There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies.

NCI Imaging Data Commons.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

Appropriate use criteria for cardiovascular MRI: SIC - SIRM position paper Part 2 (myocarditis, pericardial disease, cardiomyopathies and valvular heart disease).

Cardiovascular magnetic resonance (CMR) has emerged as an accurate diagnostic technique for the evaluation of patients with cardiac disease in the majority of clinical settings, thanks to an established additional diagnostic and prognostic value. This document has been developed by a joined group of experts of the Italian Society of Cardiology (SIC) and Italian Society of Radiology (SIRM) to provide a summary about the current state of technology and clinical applications of CMR, to improve the clinical diagnostic pathways and to promote its inclusion in clinical practice. The writing committee consisted of members and experts of both societies in order to develop a more integrated approach in the field of cardiac imaging. This section 2 will cover myocarditis, pericardial disease, cardiomyopathies and valvular heart disease.

Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules. RESEARCH QUESTION: Is it possible to identify pulmonary nodules and associated characteristics using an automated method? STUDY DESIGN AND METHODS: We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics. RESULTS: In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%). INTERPRETATION: The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.

Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses.

OBJECTIVE: To assess the diagnostic performance and inter-observer agreement of the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US). METHODS: From January 2016 to December 2018 a total of 1054 adnexal lesions in 1035 patients with pathologic results from two hospitals were retrospectively included. Each lesion was assigned to an O-RADS US category according to the criteria. Kappa (κ) statistics were applied to assess inter-observer agreement between a less experienced and an expert radiologist. RESULTS: Of the 1054 adnexal lesions, 750 were benign and 304 were malignant. The malignancy rates of O-RADS 5, O-RADS 4, O-RADS 3, and O-RADS 2 lesions were 89.57%, 34.46%, 1.10%, and 0.45% respectively. Area under the receiver operating characteristic curve was 0.960 (95% CI, 0.947-0.971). The optimal cutoff value for predicting malignancy was >O-RADS 3 with a sensitivity and specificity of 98.7% (95% CI, 0.964-0.996) and 83.2% (95% CI, 0.802-0.858) respectively. When sub-classifying multilocular cysts and smooth solid lesions in O-RADS 4 lesions as O-RADS 4a lesions and the rest cystic lesions with solid components as O-RADS 4b lesions, the malignancy rate were 17.02% and 42.57% respectively, which showed better risk stratification (P < 0.001). The inter-observer agreement between a less-experienced and an expert radiologist of O-RADS categorization was good (κ = 0.714). CONCLUSIONS: The ACR O-RADS US provides effective malignancy risk stratification for adnexal lesions with high reliability for radiologists with different experience. Sub-grouping of O-RADS 4 lesions into two groups facilitated better stratification of the intermediate risk.

Musculoskeletal ultrasound imaging standards in the UK: British Society of Skeletal Radiologists (BSSR) position statement.

There has been some concern expressed by UK regulator, the Professional Standards Authority regarding the risks arising from Independent sonographer practices. The Professional Standards Authority presented evidence demonstrating that there are instances of harm occurring because of errors made by non-radiologists performing musculoskeletal ultrasound (MSKUS), particularly MSKUS-guided interventions. This document summarises British Society of Skeletal Radiologists position for Musculoskeletal use of ultrasound in UK, representing the agreed consensus of experts from the British Society of Skeletal Radiologists Ultrasound committee. The purpose of this position statement is to review the current practices affecting the delivery of MSKUS. Recommendations are given for education and training, audit and clinical governance, reporting, and medicolegal issues.

Data Integrity of Radiology Images Over an Insecure Network Using AES Technique.

BACKGROUND: While transmitting the medical images in radiology information systems the adversary effect can break the CIA (Confidentiality, Integrity, and Availability) triads of information security. The objective of the study was to transmit the complete set of image objects in a dataset without data integrity violation. METHODS: In this paper a hybrid cryptographic technique which combines the prime details from the patient dataset (stack of axial 2D images) and the Advanced Encryption Standard (AES) method has been proposed. The steps include a) Creating an artificial X-ray image (DRR) from the 3D volume, b) dividing the DRR image in x and y directions equally into four regions, c) applying the zig-zag pattern to each quadrant, and d) encryption of each quadrant with block cipher mode using the AES algorithm. After dataset transmission the DRR image was regenerated at the receiver and compared each of the deciphered blocks (transmitted ones) using the histogram technique. RESULTS: The technique was tested on CT and MRI scans of sixty datasets. The image injection techniques, such as adding and deleting an image from the dataset and modifying the image pixels, were tested. The results were validated statistically using mean square error and histogram matching techniques. CONCLUSION: The combination of the DRR and the AES technique has ensured the secured transmission of the entire dataset and not an individual image.

An International Survey of Quality and Safety Programs in Radiology.

PURPOSE: The aim of this study was to determine the status of radiology quality improvement programs in a variety of selected nations worldwide. METHODS: A survey was developed by select members of the International Economics Committee of the American College of Radiology on quality programs and was distributed to committee members. Members responded on behalf of their country. The 51-question survey asked about 12 different quality initiatives which were grouped into 4 themes: departments, users, equipment, and outcomes. Respondents reported whether a designated type of quality initiative was used in their country and answered subsequent questions further characterizing it. RESULTS: The response rate was 100% and represented Australia, Canada, China, England, France, Germany, India, Israel, Japan, the Netherlands, Russia, and the United States. The most frequently reported quality initiatives were imaging appropriateness (91.7%) and disease registries (91.7%), followed by key performance indicators (83.3%) and morbidity and mortality rounds (83.3%). Peer review, equipment accreditation, radiation dose monitoring, and structured reporting were reported by 75.0% of respondents, followed by 58.3% of respondents for quality audits and critical incident reporting. The least frequently reported initiatives included Lean/Kaizen exercises and physician performance assessments, implemented by 25.0% of respondents. CONCLUSION: There is considerable diversity in the quality programs used throughout the world, despite some influence by national and international organizations, from whom further guidance could increase uniformity and optimize patient care in radiology.

Time Requirement and Feasibility of a Systematic Quality Peer Review of Reporting in Radiology.

OBJECTIVE: To estimate the human resources required for a retrospective quality review of different percentages of all routine diagnostic procedures in the Department of Radiology at Bern University Hospital, Switzerland. MATERIALS AND METHODS: Three board-certified radiologists retrospectively evaluated the quality of the radiological reports of a total of 150 examinations (5 different examination types: abdominal CT, chest CT, mammography, conventional X-ray images and abdominal MRI). Each report was assigned a RADPEER score of 1 to 3 (score 1: concur with previous interpretation; score 2: discrepancy in interpretation/not ordinarily expected to be made; score 3: discrepancy in interpretation/should be made most of the time). The time (in seconds, s) required for each review was documented and compared. A sensitivity analysis was conducted to calculate the total workload for reviewing different percentages of the total annual reporting volume of the clinic. RESULTS: Among the total of 450 reviews analyzed, 91.1 % (410/450) were assigned a score of 1 and 8.9 % (40/450) were assigned scores of 2 or 3. The average time (in seconds) required for a peer review was 60.4 s (min. 5 s, max. 245 s). The reviewer with the greatest clinical experience needed significantly less time for reviewing the reports than the two reviewers with less clinical expertise (p < 0.05). Average review times were longer for discrepant ratings with a score of 2 or 3 (p < 0.05). The total time requirement calculated for reviewing all 5 types of examination for one year would be more than 1200 working hours. CONCLUSION: A retrospective peer review of reports of radiological examinations using the RADPEER system requires considerable human resources. However, to improve quality, it seems feasible to peer review at least a portion of the total yearly reporting volume. KEY POINTS: · A systematic retrospective assessment of the content of radiological reports using the RADPEER system involves high personnel costs.. · The retrospective assessment of all reports of a clinic or practice seems unrealistic due to the lack of highly specialized personnel.. · At least part of all reports should be reviewed with the aim of improving the quality of reports.. CITATION FORMAT: · Maurer MH, Brönnimann M, Schroeder C et al. Time Requirement and Feasibility of a Systematic Quality Peer Review of Reporting in Radiology. Fortschr Röntgenstr 2021; 193: 160 - 167.

Discrepancies in the Recommended Management of Adrenal Incidentalomas by Various Guidelines.

PURPOSE: Adrenal incidentalomas are being discovered with increasing frequency, and their discovery poses a challenge to clinicians. Despite the 2002 National Institutes of Health consensus statement, there are still discrepancies in the most recent guidelines from organizations representing endocrinology, endocrine surgery, urology and radiology. We review recent guidelines across the specialties involved in diagnosing and treating adrenal incidentalomas, and discuss points of agreement as well as controversy among guidelines. MATERIALS AND METHODS: PubMed®, Scopus®, Embase™ and Web of Science™ databases were searched systematically in November 2019 in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement to identify the most recently updated committee produced clinical guidelines in each of the 4 specialties. Five articles met the inclusion criteria. RESULTS: There is little debate among the reviewed guidelines as to the initial evaluation of an adrenal incidentaloma. All patients with a newly discovered adrenal incidentaloma should receive an unenhanced computerized tomogram and hormone screen. The most significant points of divergence among the guidelines regard reimaging an initially benign appearing mass, repeat hormone testing and management of an adrenal incidentaloma that is not easily characterized as benign or malignant on computerized tomography. The guidelines range from actively recommending against any repeat imaging and hormone screening to recommending a repeat scan as early as in 3 to 6 months and annual hormonal screening for several years. CONCLUSIONS: After reviewing the guidelines and the evidence used to support them we posit that best practices lie at their convergence and have presented our management recommendations on how to navigate the guidelines when they are discrepant.