Adding Value in Radiology Reporting

Spectrum of Cognitive Biases in Diagnostic Radiology

Cognitive biases are systematic thought processes involving the use of a filter of personal experiences and preferences arising from the tendency of the human brain to simplify information processing, especially when taking in vast amounts of data such as from imaging studies. These biases encompass a wide spectrum of thought processes and frequently overlap in their concepts, with multiple biases usually in operation when interpretive and perceptual errors occur in radiology. The authors review the gamut of cognitive biases that occur in radiology. These biases are organized according to their expected stage of occurrence while the radiologist reads and interprets an imaging study. In addition, the authors propose several additional cognitive biases that have not yet, to their knowledge, been defined in the radiologic literature but are applicable to diagnostic radiology. Case examples are used to illustrate potential biases and their impact, with emergency radiology serving as the clinical paradigm, given the associated high imaging volumes, wide diversity of imaging examinations, and rapid pace, which can further increase a radiologist's reliance on biases and heuristics. Potential strategies to recognize and overcome one's personal biases at each stage of image interpretation are also discussed. Awareness of such biases and their unintended effects on imaging interpretations and patient outcomes may help make radiologists cognizant of their own biases that can result in diagnostic errors. Identification of cognitive bias in departmental and systematic quality improvement practices may represent another tool to prevent diagnostic errors in radiology. ©RSNA, 2024 See the invited commentary by Larson in this issue.

Practical Strategies to Retain Radiologists

Since the great resignation associated with the coronavirus disease 2019 pandemic, radiology practices are now challenged with maintaining adequate radiology staffing requirements to cope with increasing clinical workload requirements. The authors describe practical strategies for radiology practice leaders to retain radiologists in the current challenging job market, while mitigating their burnout.

Perceptions of radiologists on structured reporting for cancer imaging-a survey by the European Society of Oncologic Imaging (ESOI)

OBJECTIVES

To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template.

MATERIALS AND METHODS

An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients.

RESULTS

A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists' experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = - 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = - 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036).

CONCLUSION

Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians.

CLINICAL RELEVANCE STATEMENT

Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement.

KEY POINTS

• The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. • The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). • Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.

DXA Reporting Updates: 2023 Official Positions of the International Society for Clinical Densitometry

INTRODUCTION

Professional guidance and standards assist radiologic interpreters in generating high quality reports. Initially DXA reporting Official Positions were provided by the ISCD in 2003; however, as the field has progressed, some of the current recommendations require revision and updating. This manuscript details the research approach and provides updated DXA reporting guidance.

METHODS

Key Questions were proposed by ISCD established protocols and approved by the Position Development Conference Steering Committee. Literature related to each question was accumulated by searching PubMed, and existing guidelines from other organizations were extracted from websites. Modifications and additions to the ISCD Official Positions were determined by an expert panel after reviewing the Task Force proposals and position papers.

RESULTS

Since most DXA is now performed in radiology departments, an approach was endorsed that better aligns with standard radiologic reports. To achieve this, reporting elements were divided into required minimum or optional. Collectively, required components comprise a standard diagnostic report and are considered the minimum necessary to generate an acceptable report. Additional elements were retained and categorized as optional. These optional components were considered relevant but tailored to a consultative, clinically oriented report. Although this information is beneficial, not all interpreters have access to sufficient clinical information, or may not have the clinical expertise to expand beyond a diagnostic report. Consequently, these are not required for an acceptable report.

CONCLUSION

These updated ISCD positions conform with the DXA field's evolution over the past 20 years. Specifically, a basic diagnostic report better aligns with radiology standards, and additional elements (which are valued by treating clinicians) remain acceptable but are optional and not required. Additionally, reporting guidance for newer elements such as fracture risk assessment are incorporated. It is our expectation that these updated Official Positions will improve compliance with required standards and generate high quality DXA reports that are valuable to the recipient clinician and contribute to best patient care.

Eye tracking validation: Improving radiologist reporting and interpretation

RATIONALE AND OBJECTIVES

This study aims to validate a new radiology reporting style using eye tracking to maximize radiologist interpretation time, increase accuracy, and minimize dictation time, ultimately providing a clinically relevant, concise, and accurate reporting style.

MATERIALS AND METHODS

The positive findings only dictation style using a podcast stand-alone microphone (n = 76) was compared with the standard check-list dictation style using a handheld microphone (n = 81). Experienced board-certified radiologists used each style for various imaging modalities. The number of voice recognition corrections per case was tracked. Eye-tracking glasses captured eye movement to document dictation, interpretation, and total examination times. This device also generated thermal heat maps for each style. The statistical difference between the two methods was assessed via descriptive analysis and inferential statistics.

RESULTS

Eye tracking revealed that the new positive findings dictation style led to a noteworthy shift in radiologists' visual attention, with reduced heat map overlaying the reporting software compared to the standard check-list style, indicating greater focus on medical images. Cases with at least one voice recognition correction significantly decreased using the positive findings dictation style versus the standard check-list style (5.26 % vs. 14.81 %; p = 0.0240). The positive findings dictation style significantly decreased average dictation time (16.54 s [s] vs. 29.39 s; p = 0.0003) without impacting interpretation time (70.90 s vs. 64.30 s; p = 0.7799) or total examination time (87.45 s vs. 93.69 s; p = 0.3756) compared to the standard style.

CONCLUSION

Positive findings only dictation style significantly decreased dictation time and enhanced accuracy without compromising total interpretation time.

Impact of Structured Reporting of Skeletal Survey in Skeletal Dysplasia: A Single Institution Experience

Background Structured reporting has the advantages of reducing ambiguity in written radiology reports with greater uniformity and comparability of reports amongst different institutes. It has multiple facets: structured format, structured content, and standardized language. While structured reporting initiative has been used in various radiology subspecialties such as oncology, cardiothoracic, abdominal and interventional radiology; skeletal dysplasia is a domain that remains largely untouched by this concept.

Purpose To evaluate the impact of structured reporting in skeletal dysplasia.

Methods and Materials This was an ethically approved pragmatic clinical trial. A defined number (75) of clinically diagnosed and/or genetically confirmed skeletal dysplasia radiographs were evaluated by two radiologists (reader A and reader B) with 5-and 7-years' experience in general radiology, respectively. A pre-defined structured reporting format for skeletal dysplasia was used as an interventional tool. Both the readers interpreted the radiographs before and after the training session. In addition to diagnosis, diagnostic confidence was noted using a semiquantitative scale. Improvement in diagnostic accuracy and diagnostic confidence after training were assessed. McNemar's test was used to assess the statistical significance of difference in proportion of correct diagnoses in pre- and post-education phases. An interrater reliability analysis using the Kappa statistic was performed to determine interobserver agreement between readers both in pre- and post-education phases.

Results In the post-education phase, the proportion of accurate diagnosis improved from 48% (36/75) to 64% (48/75) for reader A, and from 44% (33/75) to 60% (45/75) for reader B as compared with the pre-education phase. Amongst the cases with a correct radiologic diagnosis, an increase in diagnostic confidence was noted in 18 cases for reader A, and 15 cases for reader B. In none of the cases, there was a reduction in diagnostic confidence after training. A McNemar's test determined that there was a statistically significant difference in the proportion of correct diagnoses in pre- and post-education phases, p < 0.001. The interobserver agreement between the readers was found to increase from Kappa = 0.33 (p = 0.004) using non-structured reporting in pre-education phase to Kappa = 0.46 (p < 0.001) using structured reporting in the post-education phase.

Conclusion A structured reporting of skeletal survey can improve accuracy and confidence in diagnosing skeletal dysplasia.

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI)

This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.

A Systematic Review of Chest Imaging Findings in Long COVID Patients

Long COVID is the persistence of one or more COVID-19 symptoms after the initial viral infection, and there is evidence supporting its association with lung damage. In this systematic review, we provide an overview of lung imaging and its findings in long COVID patients. A PubMed search was performed on 29 September 2021, for English language studies in which lung imaging was performed in adults suffering from long COVID. Two independent researchers extracted the data. Our search identified 3130 articles, of which 31, representing the imaging findings of 342 long COVID patients, were retained. The most common imaging modality used was computed tomography (CT) (N = 249). A total of 29 different imaging findings were reported, which were broadly categorized into interstitial (fibrotic), pleural, airway, and other parenchymal abnormalities. A direct comparison between cases, in terms of residual lesions, was available for 148 patients, of whom 66 (44.6%) had normal CT findings. Although respiratory symptoms belong to the most common symptoms in long COVID patients, this is not necessarily linked to radiologically detectable lung damage. Therefore, more research is needed on the role of the various types of lung (and other organ) damage which may or may not occur in long COVID.

Value assessment of artificial intelligence in medical imaging: a scoping review

BACKGROUND

Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging.

METHODS

We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes.

RESULTS

Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations.

CONCLUSIONS

This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.

The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study

Background

Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification.

Methods

In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports.

Results

The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data.

Conclusion

Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.

Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

Analysis of Radiology Report Recommendation Characteristics and Rate of Recommended Action Performance

IMPORTANCE

Following up on recommendations from radiologic findings is important for patient care, but frequently there are failures to carry out these recommendations. The lack of reliable systems to characterize and track completion of actionable radiology report recommendations poses an important patient safety challenge.

OBJECTIVES

To characterize actionable radiology recommendations and, using this taxonomy, track and understand rates of loop closure for radiology recommendations in a primary care setting.

DESIGN, SETTING, AND PARTICIPANTS

Radiology reports in a primary care clinic at a large academic center were redesigned to include actionable recommendations in a separate dedicated field. Manual review of all reports generated from imaging tests ordered between January 1 and December 31, 2018, by primary care physicians that contained actionable recommendations was performed. For this quality improvement study, a taxonomy system that conceptualized recommendations was developed based on 3 domains: (1) what is recommended (eg, repeat a test or perform a different test, specialty referral), (2) specified time frame in which to perform the recommended action, and (3) contingency language qualifying the recommendation. Using this framework, a 2-stage process was used to review patients' records to classify recommendations and determine loop closure rates and factors associated with failure to complete recommended actions. Data analysis was conducted from April to July 2021.

MAIN OUTCOMES AND MEASURES

Radiology recommendations, time frames, and contingencies. Rates of carrying out vs not closing the loop on these recommendations in the recommended time frame were assessed.

RESULTS

A total of 598 radiology reports were identified with structured recommendations: 462 for additional or future radiologic studies and 196 for nonradiologic actions (119 specialty referrals, 47 invasive procedures, and 43 other actions). The overall rate of completed actions (loop closure) within the recommended time frame was 87.4%, with 31 open loop cases rated by quality expert reviewers to pose substantial clinical risks. Factors associated with successful loop closure included (1) absence of accompanying contingency language, (2) shorter recommended time frames, and (3) evidence of direct radiologist communication with the ordering primary care physicians. A clinically significant lack of loop closure was found in approximately 5% of cases.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that creating structured radiology reports featuring a dedicated recommendations field permits the development of taxonomy to classify such recommendations and determine whether they were carried out. The lack of loop closure suggests the need for more reliable systems.

Talking Points: Enhancing Communication Between Radiologists and Patients

Radiologists communicate along multiple pathways, using written, verbal, and non-verbal means. Radiology trainees must gain skills in all forms of communication, with attention to developing effective professional communication in all forms. This manuscript reviews evidence-based strategies for enhancing effective communication between radiologists and patients through direct communication, written means and enhanced reporting. We highlight patient-centered communication efforts, available evidence, and opportunities to engage learners and enhance training and simulation efforts that improve communication with patients at all levels of clinical care.

Structured Reporting in Radiology: what do radiologists think and does RANZCR have a role in implementation

INTRODUCTION

The Royal Australian and New Zealand College of Radiologists (RANZCR) established a working group to explore how the college should engage with the future development of structured radiology reporting in our region, particularly in the context of a broader move to digital healthcare. Phase 1 of the project surveyed college members and affiliated interest groups about how they are using structured reporting currently and might like it to evolve.

METHODS

Member and interest group questionnaires were based on previously published studies and posted to the Survey Monkey platform. Responses were analysed descriptively.

RESULTS

There were 114 members and 58 affiliated group responses. There is clearest support for RANZCR developing guidelines around structured report quality, for improvements in report content, particularly tailoring to clinical context and study parameters, and for improved integration of structured reporting and RIS/PACS systems.

CONCLUSIONS

Phase 2 of the structured reporting working group project will aim to develop guidelines for structured report quality and processes through which RANZCR can implement them.

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.

Structured Reporting in Multiple Sclerosis Reduces Interpretation Time

RATIONALE AND OBJECTIVES

Previous studies have reported mixed results regarding whether the use of structured reporting (SR) leads to a change in interpretation times. The objective of this study was to quantify any change in interpretation times after the implementation of SR for multiple sclerosis (MS) follow-up magnetic resonance imaging (MRI) of the brain.

MATERIALS AND METHODS

Interpretation times before and after the transition to MS MRI SR were compared over a 5-year period. To control for changing practice patterns, a control group of non-MS (intracranial masses) reports not using SR was also assessed. In a secondary analysis, interpretation times for 2D and 3D MRI MS protocols after the initiation of SR were compared to determine whether increased image number with the 3D protocol affected interpretation times.

RESULTS

Mean and median interpretation times before the initiation of SR for MS MRI were 11.0 and 8.0 minutes versus 8.5 and 6.0 minutes after the implementation of SR (p < 0.001). Although non-MS MRI interpretation times also decreased, an interaction analysis demonstrated that the decrease in MS interpretation times was significantly higher (p < 0.001). Mean and median interpretation times using 3D protocols were slighter increased compared to interpretation times with 2D protocols (p = 0.036).

CONCLUSION

After the implementation of SR for MS follow-up MRI at our institution, interpretation times significantly decreased despite the increased number of images with some of the examinations due to the adoption of 3D protocols. The adoption of SR for MS MRI follow-up scans may improve radiologist efficiency.

Common blind spots and interpretive errors of neck imaging

Complex anatomy and a wide spectrum of diseases in the head and neck predispose interpretation of neck imaging to cognitive pitfalls and perceptual errors. Extra attention to common blind spots in the neck and familiarity with common interpretive challenges could aid radiologists in preventing these diagnostic errors.

Identification of chronic thromboembolic pulmonary hypertension on CTPAs performed for diagnosing acute pulmonary embolism depending on level of expertise

BACKGROUND

Expert reading often reveals radiological signs of chronic thromboembolic pulmonary hypertension (CTEPH) or chronic PE on computed tomography pulmonary angiography (CTPA) performed at the time of acute pulmonary embolism (PE) presentation preceding CTEPH. Little is known about the accuracy and reproducibility of CTPA reading by radiologists in training in this setting.

OBJECTIVES

To evaluate 1) whether signs of CTEPH or chronic PE are routinely reported on CTPA for suspected PE; and 2) whether CTEPH-non-expert readers achieve comparable predictive accuracy to CTEPH-expert radiologists after dedicated instruction.

METHODS

Original reports of CTPAs demonstrating acute PE in 50 patients whom ultimately developed CTEPH, and those of 50 PE who did not, were screened for documented signs of CTEPH. All scans were re-assessed by three CTEPH-expert readers and two CTEPH-non-expert readers (blinded and independently) for predefined signs and overall presence of CTEPH.

RESULTS

Signs of chronic PE were mentioned in the original reports of 14/50 cases (28%), while CTEPH-expert radiologists had recognized 44/50 (88%). Using a standardized definition (≥3 predefined radiological signs), moderate-to-good agreement was reached between CTEPH-non-expert readers and the experts' consensus (k-statistics 0.46; 0.61) at slightly lower sensitivities. The CTEPH-non-expert readers had moderate agreement on the presence of CTEPH (κ-statistic 0.38), but both correctly identified most cases (80% and 88%, respectively).

CONCLUSIONS

Concomitant signs of CTEPH were poorly documented in daily practice, while most CTEPH patients were identified by CTEPH-non-expert readers after dedicated instruction. These findings underline the feasibility of achieving earlier CTEPH diagnosis by assessing CTPAs more attentively.

Basic Artificial Intelligence Techniques: Natural Language Processing of Radiology Reports

Natural language processing (NLP) is a subfield of computer science and linguistics that can be applied to extract meaningful information from radiology reports. Symbolic NLP is rule based and well suited to problems that can be explicitly defined by a set of rules. Statistical NLP is better situated to problems that cannot be well defined and requires annotated or labeled examples from which machine learning algorithms can infer the rules. Both symbolic and statistical NLP have found success in a variety of radiology use cases. More recently, deep learning approaches, including transformers, have gained traction and demonstrated good performance.

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

Background: Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation. Results: The final SR version was built by including 16 items in the “Patient Clinical Data” section, 4 items in the “Clinical Evaluation” section, 8 items in the “Exam Technique” section, 22 items in the “Report” section, and 5 items in the “Conclusion” section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1–5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4–5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report. Conclusions: The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.

How to Use LI-RADS to Report Liver CT and MRI Observations

Primary liver cancer is the fourth leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) comprising the vast majority of primary liver malignancies. Imaging plays a central role in HCC diagnosis and management. As a result, the content and structure of radiology reports are of utmost importance in guiding clinical management. The Liver Imaging Reporting and Data System (LI-RADS) provides guidance for standardized reporting of liver observations in patients who are at risk for HCC. LI-RADS standardized reporting intends to inform patient treatment and facilitate multidisciplinary communication and decisions, taking into consideration individual clinical factors. Depending on the context, observations may be reported individually, in aggregate, or as a combination of both. LI-RADS provides two templates for reporting liver observations: in a single continuous paragraph or in a structured format with keywords and imaging findings. The authors clarify terminology that is pertinent to reporting, highlight the benefits of structured reports, discuss the applicability of LI-RADS for liver CT and MRI, review the elements of a standardized LI-RADS report, provide guidance on the description of LI-RADS observations exemplified with two case-based reporting templates, illustrate relevant imaging findings and components to be included when reporting specific clinical scenarios, and discuss future directions. An invited commentary by Yano is available online. Online supplemental material is available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.

Prevalence, Association Relation, and Dynamic Evolution Analysis of Critical Values in Health Checkup in China: A Retrospective Study

Objective: The critical values in health checkup play a key role in preventing chronic diseases and different types of cancer. This study aimed to analyze the prevalence, association relation, and dynamic evolution of critical values in health checkups at a large physical examination center in China.

Methods: Herein, we chose 33,639 samples of physical examiners from January 2017 to December 2019. After strict exclusion processes, combined with the critical values in health checkup reporting data, 4,721 participants with at least one critical value were included. We first defined a critical value list for laboratory test, imaging, cervical cancer screening, electrocardiogram, and health checkup informed on site, and then performed a cross-sectional study to analyze the distribution and significance of critical values of 4,721 participants from different views and the association relation of 628 participants with more than one critical value and a retrospective cohort study to analyze the incidence and dynamic evolution of critical values based on 2,813 participants attending the physical examination from 2017 to 2019.

Results: A total of 4,721 participants were included in the retrospective study. The prevalence of 10 critical values from 33,639 participants was over 0.6%. The critical values of obesity, hypertension, Glucose_T, Liver_T, Kidney_T, Lipid_T, Urine_T, and Head_CT were significantly increased in men (P < 0.05), whereas the results were the opposite for the Blood_T and Thyroid_US (P < 0.01). The prevalence trend of critical values increased along with age, where the prevalence of men was higher than that of women under 60 years old (P < 0.01), while the prevalence of women increased by four times and exceeded the prevalence of men above 70 years old. Association relation analysis identified 16 and 6 effective rules for men and women, respectively, where the critical values of Urine_T and Glucose_T played the central roles. Furthermore, a retrospective dynamic evolution analysis found that the incidence of new critical values was about 10%, the incidence of persistent critical values was about 50%, and that most of the effective evolution paths tended to no critical values for men and women.

Conclusion: In conclusion, this study provides a new perspective to explore the population health status using the critical value reporting data in a physical examination center, which can assist in decision-making by health management at the population level and in the prevention and treatment of various types of cancer and chronic diseases at the individual level.

Handoffs in Radiology: Minimizing Communication Errors and Improving Care Transitions

Handoffs are essential to achieving safe care transitions. In radiology practice, frequent transitions of care responsibility among clinicians, radiologists, and patients occur between moments of care such as determining protocol, imaging, interpreting, and consulting. Continuity of care is maintained across these transitions with handoffs, which are the process of communicating patient information and transferring decision-making responsibility. As a leading cause of medical error, handoffs are a major communication challenge that is exceedingly common in both diagnostic and interventional radiology practice. The frequency of handoffs in radiology underscores the importance of using evidence-based strategies to improve patient safety in the radiology department. In this article, reliability science principles and handoff improvement tools are adapted to provide radiology-focused strategies at individual, team, and organizational levels with the goal of minimizing handoff errors and improving care transitions.

Structured Reporting of Rectal Cancer Staging and Restaging: A Consensus Proposal

Simple Summary

Structured reporting in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Abstract

Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information. Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess the internal consistency of each section and to measure the quality analysis according to the average inter-item correlation. The intraclass correlation coefficient (ICC) was also evaluated. Results: After the second Delphi round of the SR RC staging, the panelists’ single scores and sum of scores were 3.8 (range 2–4) and 169, and the SR RC restaging panelists’ single scores and sum of scores were 3.7 (range 2–4) and 148, respectively. The Cα correlation coefficient was 0.79 for SR staging and 0.81 for SR restaging. The ICCs for the SR RC staging and restaging were 0.78 (p < 0.01) and 0.82 (p < 0.01), respectively. The final SR version was built and included 53 items for RC staging and 50 items for RC restaging. Conclusions: The final version of the structured reports of MRI-based RC staging and restaging should be a helpful and promising tool for clinicians in managing cancer patients properly. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal

BACKGROUND

Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams.

MATERIALS AND METHODS

A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.

RESULTS

The final SR version was built by including n=16 items in the "Patient Clinical Data" section, n=13 items in the "Clinical Evaluation" section, n=8 items in the "Imaging Protocol" section, and n=17 items in the "Report" section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively.

CONCLUSIONS

The present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.

Reporting on Renal Masses, Recommendations for Terminology, and Sample Templates

Given the incidence of small renal masses, from benign cysts to malignancy, most radiologists encounter these lesions multiple times during their career. Radiologists have an opportunity to provide critical data that will further refine the understanding of the impact of these masses on patient outcomes. This article summarizes and describes recent updates and understanding of the critical observations and descriptors of renal masses. The templates and glossary of terms presented in this review article facilitate the radiology reporting of such data elements, giving radiologists the opportunity to improve diagnostic accuracy and influence management of small renal masses.

Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal

OBJECTIVES

The need of a standardized reporting scheme and language, in imaging of COVID-19 pneumonia, has been welcomed by major scientific societies. The aim of the study was to build the reporting scheme of chest CT in COVID-19 pneumonia.

METHODS

A team of experts, of the Italian Society of Medical and Interventional Radiology (SIRM), has been recruited to compose a consensus panel. They used a modified Delphi process to build a reporting scheme and expressed a level of agreement for each section of the report. To measure the internal consistency of the panelist ratings for each section of the report, a quality analysis based on the average inter-item correlation was performed with Cronbach's alpha (Cα) correlation coefficient.

RESULTS

The overall mean score of the experts and the sum of score were 3.1 (std.dev. ± 0.11) and 122 in the second round, and improved to 3.75 (std.dev. ± 0.40) and 154 in the third round. The Cronbach's alpha (Cα) correlation coefficient was 0.741 (acceptable) in the second round and improved to 0.789 in the third round. The final report was built in the management of radiology report template (MRRT) and includes n = 4 items in the procedure information, n = 5 items in the clinical information, n = 16 in the findings, and n = 3 in the impression, with overall 28 items.

CONCLUSIONS

The proposed structured report could be of help both for expert radiologists and for the less experienced who are faced with the management of these patients. The structured report is conceived as a guideline, to recommend the key items/findings of chest CT in COVID-19 pneumonia.

The Role of Cognitive Bias in Breast Radiology Diagnostic and Judgment Errors

Cognitive bias is an unavoidable aspect of human decision-making. In breast radiology, these biases contribute to missed or erroneous diagnoses and mistaken judgments. This article introduces breast radiologists to eight cognitive biases commonly encountered in breast radiology: anchoring, availability, commission, confirmation, gambler's fallacy, omission, satisfaction of search, and outcome. In addition to illustrative cases, this article offers suggestions for radiologists to better recognize and counteract these biases at the individual level and at the organizational level.

Statistical modeling can determine what factors are predictive of appropriate follow-up in patients presenting with incidental pulmonary nodules on CT

PURPOSE

To assess the performance of statistical modeling in predicting follow-up adherence of incidentally detected pulmonary nodules (IPN) on CT, based on patient variables (PV), radiology report related variables (RRRV) and physician-patient communication variables (PPCV).

METHODS

200 patients with IPN on CT were retrospectively identified and randomly selected. PV (age, gender, smoking status, ethnicity), RRRV (nodule size, patient context, whether follow-up recommendations were provided) and PPCV (whether referring physician documented IPN and ordered follow-up on the electronic medical record) were recorded. Primary outcome was whether patients received appropriate follow-up within +/- 1 month of the recommended time frame. Statistical methods included logistic regression and machine learning (K-nearest neighbors and support vector machine).

RESULTS

Adherence was low, with or without recommendations provided in the radiology report (23.4 %-27.4 %). Whether the referring physician ordered follow-up was the dominant predictor of adherence in all models. The following variables were statistically significant predictors of whether referring physician ordered follow-up: recommendations provided in the radiology report, smoking status, patient context and nodule size (FDR logworth of respectively 21.18, 11.66, 2.35, 1.63, p < 0.05). Prediction accuracy varied from 72 % (PV) to 93 % (PPCV, all variables).

CONCLUSION

PPCV are the most important predictors of adherence. Amongst all variables, patient context, smoking status, nodule size, and whether the radiologist provided follow-up recommendations in the report were all statistically significant predictors of patient follow-up adherence, supporting the utility of statistical modeling for analytics, quality assurance and optimization of outcomes related to IPN.