The importance and future of prostate MRI report templates: improving oncological care

The radiologist's report is crucial for guiding care post-imaging, with ongoing advancements in report construction. Recent studies across various modalities and organ systems demonstrate enhanced clarity and communication through structured reports. This article will explain the benefits of disease-state specific reporting templates using prostate MRI as the model system. We identify key reporting components for prostate cancer detection and staging as well as imaging in active surveillance and following therapy. We discuss relevant reporting systems including PI-QUAL, PI-RADS, PRECISE, PI-RR and PI-FAB systems. Additionally, we examine optimal reporting structure including disruptive technologies such as graphical reporting and using artificial intelligence to improve report clarity and applicability.

Recommendations to Improve Management of Incidental Pulmonary Nodules in Canada: Expert Panel Consensus

Introduction: Incidental pulmonary nodules (IPN) are common radiologic findings, yet management of IPNs is inconsistent across Canada. This study aims to improve IPN management based on multidisciplinary expert consensus and provides recommendations to overcome patient and system-level barriers. Methods: A modified Delphi consensus technique was conducted. Multidisciplinary experts with extensive experience in lung nodule management in Canada were recruited to participate in the panel. A survey was administered in 3 rounds, using a 5-point Likert scale to determine the level of agreement (1 = extremely agree, 5 = extremely disagree). Results: Eleven experts agreed to participate in the panel; 10 completed all 3 rounds. Consensus was achieved for 183/217 (84.3%) statements. Panellists agreed that radiology reports should include a standardized summary of findings and follow-up recommendations for all nodule sizes (ie, <6, 6-8, and >8 mm). There was strong consensus regarding the importance of an automated system for patient follow-up and that leadership support for organizational change at the administrative level is of utmost importance in improving IPN management. There was no consensus on the need for standardized national referral pathways, development of new guidelines, or establishing a uniform picture archiving and communication system. Conclusion: Canadian IPN experts agree that improved IPN management should include standardized radiology reporting of IPNs, standardized and automated follow-up of patients with IPNs, guideline adherence and implementation, and leadership support for organizational change. Future research should focus on the implementation and long-term effectiveness of these recommendations in clinical practice.

Axial Spondyloarthritis: Does Magnetic Resonance Imaging Classification Improve Report Interpretation

OBJECTIVE

The interpretation of magnetic resonance imaging (MRI) reports is crucial for the diagnosis of axial spondyloarthritis, but the subjective nature of narrative reports can lead to varying interpretations. This study presents a validation of a novel MRI reporting system for the sacroiliac joint in clinical practice.

METHODS

A historical review was conducted on 130 consecutive patients referred by 2 rheumatologists for initial MRI assessment of possible axial spondyloarthritis. The original MRI reports were interpreted by the rheumatologists and the radiologist who originally read the images and then categorized according to the novel system. Two musculoskeletal radiologists then reinterpreted the original MRI scans using the new system, and the resulting reports were interpreted and categorized by the same rheumatologists. The quality of the new framework was assessed by comparing the interpretations of both reports.

RESULTS

Ninety-two patients met the study criteria. The rheumatologists disagreed on the categorization of the original MRI reports in 12% of cases. The rheumatologists and original radiologists disagreed on the categorization of the initial report in 23.4% of cases. In contrast, there was 100% agreement between the rheumatologists and radiologists on the categorization of the new MRI report.

CONCLUSION

The new MRI categorization system significantly improved the agreement between the clinician and radiologist in report interpretation. The system provided a standard vocabulary for reporting, reduced variability in report interpretation, and may therefore improve clinical decision-making.

Bridging Communication Gaps Between Radiologists, Referring Physicians, and Patients Through Standardized Structured Cancer Imaging Reporting: The Experience with Female Pelvic MRI Assessment Using O-RADS and a Simulated Cohort Patient Group

RATIONALE AND OBJECTIVES

This study aimed to evaluate whether implementing structured reporting based on Ovarian-Adnexal Reporting and Data System (O-RADS) magnetic resonance imaging (MRI) in women with sonographically indeterminate adnexal masses improves communication between radiologists, referrers, and patients/caregivers and enhances diagnostic performance for determining adnexal malignancy.

MATERIALS AND METHODS

We retrospectively analyzed prospectively issued MRI reports in 2019-2022 performed for characterizing adnexal masses before and after implementing O-RADS MRI; 56 patients/caregivers and nine gynecologic oncologists ("referrers") were surveyed about report interpretability/clarity/satisfaction; responses for pre- and post-implementation reports were compared using Fisher's exact and Chi-squared tests. Diagnostic performance was assessed using receiver operating characteristic curves.

RESULTS

A total of 123 reports from before and 119 reports from after O-RADS MRI implementation were included. Survey response rates were 35.7% (20/56) for patients/caregivers and 66.7% (6/9) for referrers. For patients/caregivers, O-RADS MRI reports were clearer (p < 0.001) and more satisfactory (p < 0.001) than unstructured reports, but interpretability did not differ significantly (p = 0.14), as 28.0% (28/100) of postimplementation and 38.0% (38/100) of preimplementation reports were considered difficult to interpret. For referrers, O-RADS MRI reports were clearer, more satisfactory, and easier to interpret (p < 0.001); only 1.3% (1/77) were considered difficult to interpret. For differentiating benign from malignant adnexal lesions, O-RADS MRI showed area under the curve of 0.92 (95% confidence interval [CI], 0.85-0.99), sensitivity of 0.81 (95% CI, 0.58-0.95), and specificity of 0.91 (95% CI, 0.83-0.96). Diagnostic performance of reports before implementation could not be calculated due to many different phrases used to describe the likelihood of malignancy.

CONCLUSION

Implementing standardized structured reporting using O-RADS MRI for characterizing adnexal masses improved clarity and satisfaction for patients/caregivers and referrers. Interpretability improved for referrers but remained limited for patients/caregivers.

Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period

OBJECTIVES

To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and distinguishability.

METHODS

A total of 747,393 radiology reports dictated between January 2011 and June 2020 were retrospectively analyzed. The body and cardiothoracic imaging divisions introduced a reporting concept using standardized language and structured reporting templates in January 2016. Reports were segmented by a natural language processing algorithm and converted into a 20-dimension document vector. For analysis, dimensionality was reduced to a 2D visualization with t-distributed stochastic neighbor embedding and matched with metadata. Linguistic standardization was assessed by comparing distinct report types' vector spreads (e.g., run-off MR angiography) between reporting standards. Changes in report type distinguishability (e.g., CT abdomen/pelvis vs. MR abdomen) were measured by comparing the distance between their centroids.

RESULTS

Structured reports showed lower document vector spread (thus higher linguistic similarity) compared with free-text reports overall (21.9 [free-text] vs. 15.9 [structured]; - 27.4%; p < 0.001) and for most report types, e.g., run-off MR angiography (15.2 vs. 1.8; - 88.2%; p < 0.001) or double-rule-out CT (26.8 vs. 10.0; - 62.7%; p < 0.001). No changes were observed for reports continued to be written in free text, e.g., CT head reports (33.2 vs. 33.1; - 0.3%; p = 1). Distances between the report types' centroids increased with structured reporting (thus better linguistic distinguishability) overall (27.3 vs. 54.4; + 99.3 ± 98.4%) and for specific report types, e.g., CT abdomen/pelvis vs. MR abdomen (13.7 vs. 37.2; + 171.5%).

CONCLUSION

Structured reporting and the use of factual language yield more homogenous and standardized radiology reports on a linguistic level, tailored to specific reporting scenarios and imaging studies.

CLINICAL RELEVANCE

Information transmission to referring physicians, as well as automated report assessment and content extraction in big data analyses, may benefit from standardized reporting, due to consistent report organization and terminology used for pathologies and normal findings.

KEY POINTS

• Natural language processing and t-distributed stochastic neighbor embedding can transform radiology reports into numeric vectors, allowing the quantification of their linguistic standardization. • Structured reporting substantially increases reports' linguistic standardization (mean: - 27.4% in vector spread) and distinguishability (mean: + 99.3 ± 98.4% increase in vector distance) compared with free-text reports. • Higher standardization and homogeneity outline potential benefits of structured reporting for information transmission and big data analyses.

Natural language processing for automatic evaluation of free-text answers - a feasibility study based on the European Diploma in Radiology examination

BACKGROUND

Written medical examinations consist of multiple-choice questions and/or free-text answers. The latter require manual evaluation and rating, which is time-consuming and potentially error-prone. We tested whether natural language processing (NLP) can be used to automatically analyze free-text answers to support the review process.

METHODS

The European Board of Radiology of the European Society of Radiology provided representative datasets comprising sample questions, answer keys, participant answers, and reviewer markings from European Diploma in Radiology examinations. Three free-text questions with the highest number of corresponding answers were selected: Questions 1 and 2 were "unstructured" and required a typical free-text answer whereas question 3 was "structured" and offered a selection of predefined wordings/phrases for participants to use in their free-text answer. The NLP engine was designed using word lists, rule-based synonyms, and decision tree learning based on the answer keys and its performance tested against the gold standard of reviewer markings.

RESULTS

After implementing the NLP approach in Python, F1 scores were calculated as a measure of NLP performance: 0.26 (unstructured question 1, n = 96), 0.33 (unstructured question 2, n = 327), and 0.5 (more structured question, n = 111). The respective precision/recall values were 0.26/0.27, 0.4/0.32, and 0.62/0.55.

CONCLUSION

This study showed the successful design of an NLP-based approach for automatic evaluation of free-text answers in the EDiR examination. Thus, as a future field of application, NLP could work as a decision-support system for reviewers and support the design of examinations being adjusted to the requirements of an automated, NLP-based review process.

CLINICAL RELEVANCE STATEMENT

Natural language processing can be successfully used to automatically evaluate free-text answers, performing better with more structured question-answer formats. Furthermore, this study provides a baseline for further work applying, e.g., more elaborated NLP approaches/large language models.

KEY POINTS

• Free-text answers require manual evaluation, which is time-consuming and potentially error-prone. • We developed a simple NLP-based approach - requiring only minimal effort/modeling - to automatically analyze and mark free-text answers. • Our NLP engine has the potential to support the manual evaluation process. • NLP performance is better on a more structured question-answer format.

Patient experience of imaging reports: A systematic literature review

Introduction

Written reports are often the sole form of communication from diagnostic imaging. Reports are increasingly being accessed by patients through electronic records. Experiencing medical terminology can be confusing and lead to miscommunication, a decrease in involvement and increased anxiety for patients.

Methods

This systematic review was designed to include predefined study selection criteria and was registered prospectively on PROSPERO (CRD42020221734). MEDLINE, CINAHL, Academic Search Complete (EBSCOhost), EMBASE, Scopus and EThOS were searched to identify articles meeting the inclusion criteria. Studies were assessed against the Mixed-Methods Appraisal Tool version 2018 for quality. A segregated approach was used to synthesise data. A thematic synthesis of the qualitative data and a narrative review of the quantitative data were performed, and findings of both syntheses were then integrated.

Findings

Twelve articles reporting 13 studies were included. This review found that patients' experiences of imaging reports included positive and negative aspects. The study identified two main themes encompassing both qualitative and quantitative findings. Patients reported their experiences regarding their understanding of reports and self-management.

Discussion

Patient understanding of imaging reports is multi factorial including medical terminology, communication aids and errors. Self-management through direct access is important to patients. While receiving bad news is a concern, responsibility for accessing this is accepted.

Conclusion

A patient-centred approach to writing imaging reports may help to improve the quality of service, patient experience and wider health outcomes.

Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered “gold standard”. Matthew’s correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to “gold standard” (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Recommendations for additional magnetic resonance imaging in abdominal computed tomography

BACKGROUND

Reporting the findings from radiologic images is an important method for radiologists to communicate with referring physicians. The purpose of this study was to evaluate the effectiveness of the recommendations for additional imaging (RAIs) after abdominal computed tomography (CT) studies for abdominal magnetic resonance (MR) imaging.

METHODS

The institutional review board approved this retrospective study, which includes data collected from the radiology information system (RIS) database of a tertiary medical referral center. Associations between abdominal CT and subsequent abdominal MR were recorded. The effectiveness of RAIs in an abdominal report was determined. The influence of the wording and the location of the RAIs were also analyzed.

RESULTS

The presence of RAIs in an abdominal CT report for an abdominal MR examination was more likely to result in a subsequent MR examination within 120 days (36.7% vs. 4.0%). RAIs were also associated with a reduction in the time interval between the CT and MR examinations (29.0 days vs. 39.0 days). The most effective recommendations included wording that advocated for further evaluation and were mentioned in both the context and conclusion of the report.

CONCLUSION

RAIs have a significant influence on clinical decisions. Radiologists should be aware of the power of RAIs and be prudent and conscientious when making recommendations in radiology reports.

[Image interpretation and the radiological report]

BACKGROUND

The radiological report is the cornerstone of communication between radiologists and referring physicians and patients, respectively. The report is comprised of image interpretation on the one hand and communication of this interpretation on the other hand.

OBJECTIVES AND METHODS

To outline different types of radiological reports (regarding content as well as structure) and their communication. To this end, current guidelines are summarized and clinical examples are presented.

RESULTS

The radiological report is typically a written piece of free text prose and highly individualized regarding its quality, precision, and structure. In order to improve the understanding of the written report, additional material (e.g., annotations, images, tables) can be supplemented (multimedia-enhanced reporting). In terms of standardization, national and international radiological associations promote structured reporting in radiology. However, this is not without issues.

CONCLUSION

Effective communication should improve patient care and it should be clear and provided in a timely manner. As communication in clinical reality is often hampered by various factors, internal standard operating procedures (SOPs) should be developed to improve communication workflows. to improve communication procedures.

[Interdisciplinary case discussions]

BACKGROUND

Interdisciplinary case discussions, especially tumor conferences, represent a large part of the clinical radiologist's daily work. Radiology plays a key role in tumor conferences, since imaging findings have a direct influence on therapy decisions.

METHODS AND OBJECTIVES

This article discusses the requirements for the radiologist in preparing and conducting tumor conferences. Furthermore, the general conditions and forms of implementation of tumor conferences will be highlighted. Information technology (IT) tools for process automation and systems for assessing the course of tumor diseases will be presented.

RESULTS

Detailed preparation of tumor conferences and clear communication of findings is essential. The radiological expertise in tumor conferences often leads to changes or adjustments of initially planned therapies. In addition to traditional face-to-face meetings, hybrid solutions have become established for tumor conferences in which the core team is on site and other participants (external referring physicians, internal participants outside the core team) are connected via video conference. Various systems have been established for assessing the course of tumor diseases. Due to its broad applicability, RECIST 1.1. is the most widely used. IT tools enable previously marked lesions to be displayed over time in a matrix view (lesion tracking). Artificial intelligence (AI) can also be used to automatically detect lesions and assess their volumes.

CONCLUSION

Preparing and conducting tumor conferences is time-consuming for radiologists. IT tools can automate and thus facilitate the processes. Hybrid solutions combining face-to-face meetings and video conferences make it easier for external referring physicians to present their patients in tumor conferences.

Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame

BACKGROUND

A concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework.

OBJECTIVE

This study proposes and evaluates an NLP-based algorithm capable of extracting the temporal referrals in written radiology reports, applies it to all the radiology reports generated for 10 years, introduces a graphical representation of imaging reports, and investigates its benefits for clinical and research purposes.

METHODS

In this single-center, university hospital, retrospective study, we developed a convolutional neural network capable of extracting the date of referrals from imaging reports. The model's performance was assessed by calculating precision, recall, and F1-score using an independent test set of 149 reports. Next, the algorithm was applied to our department's radiology reports generated from 2011 to 2021. Finally, the reports and their metadata were represented in a modulable graph.

RESULTS

For extracting the date of referrals, the named-entity recognition (NER) model had a high precision of 0.93, a recall of 0.95, and an F1-score of 0.94. A total of 1,684,635 reports were included in the analysis. Temporal reference was mentioned in 53.3% (656,852/1,684,635), explicitly stated as not available in 21.0% (258,386/1,684,635), and omitted in 25.7% (317,059/1,684,635) of the reports. Imaging records can be visualized in a directed and modulable graph, in which the referring links represent the connecting arrows.

CONCLUSIONS

Automatically extracting the date of referrals from unstructured radiology reports using deep learning NLP algorithms is feasible. Graphs refined the selection of distinct pathology pathways, facilitated the revelation of missing comparisons, and enabled the query of specific referring exam sequences. Further work is needed to evaluate its benefits in clinics, research, and resource planning.

Comparison of the predictive value of two international guidelines for safe discharge of patients with mild traumatic brain injuries and associated intracranial pathology

PURPOSE

To determine and compare the sensitivity, specificity, and proportion of patients eligible for discharge by the Brain Injury Guidelines and the Mild TBI Risk Score in patients with mild traumatic brain injury and concomitant intracranial injury.

METHODS

Retrospective review of the medical records of adult patients with traumatic intracranial injuries and an initial Glasgow Coma Scale score of 14-15, who sought care at Helsingborg Hospital between 2014/01/01 and 2019/12/31. Both guidelines were theoretically applied. The sensitivity, specificity, and percentage of the cohort that theoretically could have been discharged by either guideline were calculated. The outcome was defined as death, in-hospital intervention, admission to the intensive care unit, requiring emergency intubation due to intracranial injury, decreased consciousness, or seizure within 30 days of presentation.

RESULTS

Of the 538 patients included, 8 (1.5%) and 10 (1.9%) were eligible for discharge according to the Brain Injury Guidelines and the Mild TBI Risk Score, respectively. Both guidelines had a sensitivity of 100%. The Brain Injury Guidelines had a specificity of 2.3% and the Mild TBI Risk Score had a specificity of 2.9%.

CONCLUSION

There was no difference between the two guidelines in sensitivity, specificity, or proportion of the cohort eligible for discharge. Specificity and proportion of cohort eligible for discharge were lower than each guideline's original study. At present, neither guideline can be recommended for implementation in the current or similar settings.

The experiences of sonographers with regard to report writing and communicating their findings

BACKGROUND

Sonographers in South Africa are legally allowed to write their own reports; however, they often lack adequate training in providing a well-structured and coherent formal written report.

AIM

The aim of this study was to explore and describe how sonographers in the Gauteng province experience the responsibility of report writing and to develop recommendations that could assist sonographers in the execution of their duty.

SETTING

Focus group discussions (FGDs) with sonographers from private and public hospitals located in Gauteng province were conducted at neutral locations that were convenient for the sonographers.

METHODS

A qualitative phenomenological research design was used for this study. A two-stage sampling approach was employed to recruit information-rich sonographers to partake in this study. Purposeful sampling was used to select sonographers based on their first-hand experience of report writing, followed by snowball sampling which allowed the researcher access to new participants on the recommendation of previous sonographers. Thirteen female sonographers voluntarily participated in the study, and the FGDs continued until data saturation was reached. The views and opinions of the sonographers were analysed using content analysis.

RESULTS

Key findings of this study indicated that sonographers felt unprepared to describe ultrasound findings correctly in order to provide a coherent and well-structured formal written report.

CONCLUSION

Sonographers suggested the use of workshops or further training at higher educational institutions (HEIs) to support sonographers in their report-writing role.

CONTRIBUTION

The experiences identified by sonographers can assist HEIs to provide further training or workshops to support sonographers in communicating their findings effectively.

Discordance Between Oncology Clinician-Perceived and Radiologist-Intended Meaning of the Postradiotherapy Positron Emission Tomography/Computed Tomography Freeform Report for Head and Neck Cancer

Importance

Assessment of response after radiotherapy (RT) using 18F-fluorodeoxyglucose positron emission tomography (PET) with computed tomography (CT) is routine in managing head and neck squamous cell carcinoma (HNSCC). Freeform reporting may contribute to a clinician's misunderstanding of the nuclear medicine (NM) physician's image interpretation, with important clinical implications.

Objective

To assess clinician-perceived freeform report meaning and discordance with NM interpretation using the modified Deauville score (MDS).

Design, Setting, and Participants

In this retrospective cohort study that was conducted at an academic referral center and National Cancer Institute-designated Comprehensive Cancer Center and included patients with HNSCC treated with RT between January 2014 and December 2019 with a posttreatment PET/CT and 1 year or longer of follow-up, 4 masked clinicians independently reviewed freeform PET/CT reports and assigned perceived MDS responses. Interrater reliability was determined. Clinician consensus-perceived MDS was then compared with the criterion standard NM MDS response derived from image review. Data analysis was conducted between December 2021 and February 2022.

Exposures

Patients were treated with RT in either the definitive or adjuvant setting, with or without concurrent chemotherapy. They then underwent posttreatment PET/CT response assessment.

Main Outcomes and Measures

Clinician-perceived (based on the freeform PET/CT report) and NM-defined response categories were assigned according to MDS. Clinical outcomes included locoregional control, progression-free survival, and overall survival.

Results

A total of 171 patients were included (45 women [26.3%]; median [IQR] age, 61 [54-65] years), with 149 (87%) with stage III to IV disease. Of these patients, 52 (30%) received postoperative RT and 153 (89%) received concurrent chemotherapy. Interrater reliability was moderate (κ = 0.68) among oncology clinicians and minimal (κ = 0.36) between clinician consensus and NM. Exact agreement between clinician consensus and the NM was 64%. The NM-rated MDS was significantly associated with locoregional control, progression-free survival, and overall survival.

Conclusions and Relevance

The results of this cohort study suggest that considerable variation in perceived meaning exists among oncology clinicians reading freeform HNSCC post-RT PET/CT reports, with only minimal agreement between MDS derived from clinician perception and NM image interpretation. The NM use of a standardized reporting system, such as MDS, may improve clinician-NM communication and increase the value of HNSCC post-RT PET/CT reports.

Ovarian cancer reporting lexicon for computed tomography (CT) and magnetic resonance (MR) imaging developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group

OBJECTIVES

Imaging evaluation is an essential part of treatment planning for patients with ovarian cancer. Variation in the terminology used for describing ovarian cancer on computed tomography (CT) and magnetic resonance (MR) imaging can lead to ambiguity and inconsistency in clinical radiology reports. The aim of this collaborative project between Society of Abdominal Radiology (SAR) Uterine and Ovarian Cancer (UOC) Disease-focused Panel (DFP) and the European Society of Uroradiology (ESUR) Female Pelvic Imaging (FPI) Working Group was to develop an ovarian cancer reporting lexicon for CT and MR imaging.

METHODS

Twenty-one members of the SAR UOC DFP and ESUR FPI working group, one radiology clinical fellow, and two gynecologic oncology surgeons formed the Ovarian Cancer Reporting Lexicon Committee. Two attending radiologist members of the committee prepared a preliminary list of imaging terms that was sent as an online survey to 173 radiologists and gynecologic oncologic physicians, of whom 67 responded to the survey. The committee reviewed these responses to create a final consensus list of lexicon terms.

RESULTS

An ovarian cancer reporting lexicon was created for CT and MR Imaging. This consensus-based lexicon has 6 major categories of terms: general, adnexal lesion-specific, peritoneal carcinomatosis-specific, lymph node-specific, metastatic disease -specific, and fluid-specific.

CONCLUSIONS

This lexicon for CT and MR imaging evaluation of ovarian cancer patients has the capacity to improve the clarity and consistency of reporting disease sites seen on imaging.

KEY POINTS

• This reporting lexicon for CT and MR imaging provides a list of consensus-based, standardized terms and definitions for reporting sites of ovarian cancer on imaging at initial diagnosis or follow-up. • Use of standardized terms and morphologic imaging descriptors can help improve interdisciplinary communication of disease extent and facilitate optimal patient management. • The radiologists should identify and communicate areas of disease, including difficult to resect or potentially unresectable disease that may limit the ability to achieve optimal resection.

Radiological Lexicon: Use of Disease Severity Modifiers

The task of a radiologist can be described as the translation of imaging appearances into the written word. However, the optimally functioning radiologist does not simply list descriptive features in an arbitrary fashion. Instead, they integrate their clinical acumen with the patient's medical history and the available imaging, to generate a tailored, clinically relevant report. One of the particular skills of an experienced radiologist is their ability to grade the relative severity of an imaging finding, which is an important factor that influences a clinician's treatment. To make such a determination, the radiologist often employs a subjective assessment, incorporating various imaging and nonimaging features. This skill takes time and experience to develop, and the acquisition of this ability can be daunting to a radiology resident. This article discusses the underlying thought processes and the lexicon involved in grading severity of disease, and aims to shed some light on this seemingly abstract skill.

Neck Imaging Reporting and Data System Category 3 on Surveillance Computed Tomography: Incidence, Biopsy Rate, and Predictive Performance in Head and Neck Squamous Cell Carcinoma

OBJECTIVES

Neck Imaging Reporting and Data System (NI-RADS) is a radiology reporting system for head and neck cancer surveillance. Imaging findings of high suspicion for recurrence are assigned Category 3 and recommended for "Biopsy, if clinically indicated." After implementing NI-RADS for surveillance neck computed tomography (CT), our objectives are to determine the incidence of squamous cell carcinoma (SCC) Category 3 lesions in the year post-implementation, the associated biopsy rate, and the positive predictive value of NI-RADS 3 for SCC recurrence.

STUDY DESIGN

Retrospective cohort study.

METHODS

Neck CTs reported with NI-RADS between February 2020 and February 2021 were reviewed to identify patients undergoing surveillance for SCC assigned NI-RADS 3. Cancer recurrence, defined as positive biopsy result or treatment of clinically determined recurrence, was determined by electronic medical record review.

RESULTS

During the study period, 580 neck CTs were reported with NI-RADS, of which 39 (7%) CTs obtained in 37 unique patients (28 male, 9 female, mean age 66.6 years) formed the study cohort. Biopsies were obtained in 23 lesions (45%), of which 17 (74%) were positive for recurrent SCC. One nondiagnostic biopsy was clinically determined to represent recurrence. Of 28 (55%) lesions not biopsied, 18 (64%) were ultimately treated as clinically determined recurrence. Thus, among 51 individual NI-RADS 3 lesions (32 primary, 19 neck), 36 (71%) represented recurrence.

CONCLUSION

The incidence of NI-RADS 3 lesions in our cohort was 7%. The biopsy rate was 45%, and the overall positive predictive value of NI-RADS 3 for recurrent SCC was 71%. Category 3 lesions are associated with substantial SCC recurrence risk and should be managed accordingly.

LEVEL OF EVIDENCE

4 Laryngoscope, 2022.

Radiology reporting in rectal cancer using MRI: adherence to national template for structured reporting

BACKGROUND

In 2014, a national workshop program was initiated and a reporting template and manual for rectal cancer primary staging using magnetic resonance imaging (MRI) was introduced and made available by the national Swedish Colorectal Cancer Registry.

PURPOSE

To evaluate the effect of the national template program by identify if there was a gap between the content in Swedish MRI reports from 2016 and the national reporting template from 2014. The aim was to explore and compare differences in content in reporting practice in different hospitals in relation to the national reporting template, with focus on: (i) identifying any implementational differences in reporting styles; and (ii) evaluating if reporting completeness vary based on such implementational differences.

MATERIAL AND METHODS

A total of 250 MRI reports from 10 hospitals in four healthcare regions in Sweden were collected. Reports were analyzed using qualitative content analysis with a deductive thematic coding scheme based on the national reporting template.

RESULTS

Three different implemented reporting styles were identified with variations of content coverage in relation to the template: (i) standardized and structured protocol (reporting style A); (ii) standardized semi-structured free-text (reporting style B); and (iii) regular free-text (reporting style C). The relative completeness of reporting practice of rectal cancer staging in relation to the national reporting template were 92.9% for reporting style A, 77.5% for reporting style B, and 63.9% for reporting style C.

CONCLUSION

The implementation of template-based reporting according to reporting style A is a key factor to conform to evidence-based practice for rectal cancer reporting using MRI.

Qualifying Certainty in Radiology Reports through Deep Learning-Based Natural Language Processing

BACKGROUND AND PURPOSE

Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning-based bidirectional contextual language models for automatically assessing diagnostic certainty expressed in the radiology reports to facilitate the precision of communication.

MATERIALS AND METHODS

We randomly sampled 594 head MR imaging reports from an academic medical center. We asked 3 board-certified radiologists to read sentences from the Impression section and assign each sentence 1 of the 4 certainty categories: "Non-Definitive," "Definitive-Mild," "Definitive-Strong," "Other." Using the annotated 2352 sentences, we developed and validated a natural language-processing system based on the start-of-the-art bidirectional encoder representations from transformers (BERT), which can capture contextual uncertainty semantics beyond the lexicon level. Finally, we evaluated 3 BERT variant models and reported standard metrics including sensitivity, specificity, and area under the curve.

RESULTS

A κ score of 0.74 was achieved for interannotator agreement on uncertainty interpretations among 3 radiologists. For the 3 BERT variant models, the biomedical variant (BioBERT) achieved the best macro-average area under the curve of 0.931 (compared with 0.928 for the BERT-base and 0.925 for the clinical variant [ClinicalBERT]) on the validation data. All 3 models yielded high macro-average specificity (93.13%-93.65%), while the BERT-base obtained the highest macro-average sensitivity of 79.46% (compared with 79.08% for BioBERT and 78.52% for ClinicalBERT). The BioBERT model showed great generalizability on the heldout test data with a macro-average sensitivity of 77.29%, specificity of 92.89%, and area under the curve of 0.93.

CONCLUSIONS

A deep transfer learning model can be developed to reliably assess the level of uncertainty communicated in a radiology report.

Meaningful words in rectal MRI synoptic reports: How "polypoid" may be prognostic

PURPOSE

This study explored the clinicopathologic outcomes of rectal tumor morphological descriptors used in a synoptic rectal MRI reporting template and determined that prognostic differences were observed.

METHODS

This retrospective study was conducted at a comprehensive cancer center. Fifty patients with rectal tumors for whom the synoptic descriptor "polypoid" was chosen by three experienced radiologists were compared with ninety comparator patients with "partially circumferential" and "circumferential" rectal tumors. Two radiologists re-evaluated all cases. The outcome measures were agreement among two re-interpreting radiologists, clinical T staging with MRI (mrT) and descriptive nodal features, and degrees of wall attachment of tumors (on MRI) compared with pathological (p) T and N stage when available.

RESULTS

Re-evaluation by two radiologists showed moderate to excellent agreement in tumor morphology, presence of a pedicle, and degree of wall attachment (k = 0.41-0.76) and excellent agreement on lymph node presence and size (ICC = 0.83-0.91). Statistically significant lower mrT stage was noted for polypoid morphology, wherein 98% were mrT1/2, while only 7% and 2% of partially circumferential and circumferential tumors respectively were mrT1/2. Pathologic T and N stages among the three morphologies also differed significantly, with only 14% of polypoid cases higher than stage pT2 compared to 48% of partially circumferential cases and 60% of circumferential cases.

CONCLUSION

Using a "polypoid" morphology in rectal cancer MRI synoptic reports revealed a seemingly distinct phenotype with lower clinical and pathologic T and N stages when compared with alternative available descriptors.

PRECIS

"Polypoid" morphology in rectal cancer confers a lower clinical and pathologic T and N stage and may be useful in determining whether to proceed with surgery versus neoadjuvant treatment.

Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downstream tasks such as quality improvement, epidemiological research, and monitoring guideline adherence. Class imbalance, variation in dataset size, variation in report complexity, and algorithm type all influence NLP performance but have not yet been systematically and interrelatedly evaluated. In this study, we investigate these factors on the performance of four types [a fully connected neural network (Dense), a long short-term memory recurrent neural network (LSTM), a convolutional neural network (CNN), and a Bidirectional Encoder Representations from Transformers (BERT)] of deep learning-based NLP. Two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (CT) studies (n = 2255) were split into training sets (80%) and testing sets (20%). The training data was used as a source to train all four model types in 84 experiments (Fracture-data) and 45 experiments (Chest-data) with variation in size and prevalence. The performance was evaluated on sensitivity, specificity, positive predictive value, negative predictive value, area under the curve, and F score. After the NLP of radiology reports, all four model-architectures demonstrated high performance with metrics up to > 0.90. CNN, LSTM, and Dense were outperformed by the BERT algorithm because of its stable results despite variation in training size and prevalence. Awareness of variation in prevalence is warranted because it impacts sensitivity and specificity in opposite directions.

A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing

Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches.

Initial Referring Physician and Radiologist Experience with Neck Imaging Reporting and Data System

OBJECTIVES/HYPOTHESIS

Neck Imaging Reporting and Data System (NI-RADS) is a radiology reporting system developed for head and neck cancer surveillance imaging, using standardized terminology, numeric levels of suspicion, and linked management recommendations. Through a multidisciplinary, interdepartmental quality improvement initiative, we implemented NI-RADS for the reporting of head and neck cancer surveillance CT. Our objective is to summarize our initial experience from the standpoints of head and neck cancer providers and radiologists.

STUDY DESIGN

Quality improvement study.

METHODS

Before and 3 months post-implementation, surveys were offered to referring physicians (n = 21 pre-adoption; 22 post-adoption) and radiologists (n = 17 pre- and post-adoption). NI-RADS utilization was assessed over time.

RESULTS

Survey response rates were 62% (13/21) and 73% (16/22) for referring physicians pre- and post-adoption, respectively, and 94% (16/17) for radiologists pre- and post-adoption. Among post-adoption provider respondents, 100% (16/16) strongly agreed or agreed with "I want our radiologists to continue using NI-RADS," "The NI-RADS numerical rating of radiologic suspicion is helpful," and "The language and style of NI-RADS neck CT reports are clear and understandable." Among radiologist respondents, 88% (14/16) strongly agreed or agreed with "NI-RADS improves consistency among our radiologists in the reporting of surveillance neck CTs." Radiologist NI-RADS utilization increased over time (46% month 1; 72% month 3).

CONCLUSIONS

Most referring physicians and radiologists preferred NI-RADS. Head and neck cancer providers indicated that NI-RADS reports are clear, understandable, direct, and helpful in guiding clinical management. Radiologists indicated that NI-RADS improves radiologist consistency in the reporting of surveillance neck CT, and radiologists increasingly used NI-RADS over time.

LEVEL OF EVIDENCE

Level 4 Laryngoscope, 2021.

Ovarian-Adnexal Reporting Lexicon for MRI: A White Paper of the ACR Ovarian-Adnexal Reporting and Data Systems MRI Committee

MRI is used in the evaluation of ovarian and adnexal lesions. MRI can further characterize lesions seen on ultrasound to help decrease the number of false-positive lesions and avoid unnecessary surgery in benign lesions. Currently, the reporting of ovarian and adnexal findings on MRI is inconsistent because of the lack of standardized descriptor terminology. The development of uniform reporting descriptors can lead to improved interpretation agreement and communication between radiologists and referring physicians. The Ovarian-Adnexal Reporting and Data Systems MRI Committee was formed under the direction of the ACR to create a standardized lexicon for adnexal lesions with the goal of improving the quality and consistency of imaging reports. This white paper describes the consensus process in the creation of a standardized lexicon for ovarian and adnexal lesions for MRI and the resultant lexicon.

Defining competence profiles of different medical specialties with the requirement-tracking questionnaire - a pilot study to provide a framework for medial students' choice of postgraduate training

BACKGROUND

The medical specialties are characterised by a great diversity in their daily work which requires different sets of competences. A requirement analysis would help to establish competence profiles of the different medical specialities. The aim of this pilot study was to define competence profiles for individual medical specialties. This could provide a framework as support for medical graduates who wish to choose a medical specialty for their postgraduate training.

METHODS

In February 2020, physicians were invited via the State Chamber of Physicians' monthly journal to electronically fill out the requirement tracking (R-Track) questionnaire. It contains 63 aspects assigned to six areas of competence: "Mental abilities", "Sensory abilities", "Psychomotor and multitasking abilities", "Social interactive competences", "Motivation", and "Personality traits". The expression of the different aspects was assessed on a 5-point Likert scale (1: "very low" to 5: "very high"). Sociodemographic data and information about the current workplace (hospital or practice) were also collected.

RESULTS

In total, 195 practicing physicians from 19 different specialities followed the invitation by the State Chamber of Physicians to participate in this survey. For almost all medical specialties, the competence area "Motivation" reached rank 1. "Psychomotor and multitasking abilities" received high ranks among specialties performing surgical activities, while "Social interactive competences" and "Personality traits" were highly rated by specialties with an intense level of patient-physician-interaction. "Mental abilities" were only rated highly by radiologists (rank 2) and physiologists (rank 3) while "Sensory abilities" were generally rated very low with the expression (rank 4) for anaesthesiology and ENT.

CONCLUSIONS

In this pilot study, a first outline of competences profiles for 17 medical specialties were defined. The specific "Motivation" for a medical specialty seemed to play the greatest role for most specialties. This first specialty specific competence framework could provide a first insight into specific competences required by medical specialties and could serve medical graduate as a decision aid when looking for a medical specialty for their postgraduate training.

Using qualitative descriptors of chronic liver disease on MRI: A practice prone to error

PURPOSE

Assess accuracy of qualitative descriptors for chronic liver disease (CLD) in radiology reports compared to histopathological staging.

METHODS

Database search for patients with hepatitis B/C (HBV/HCV) CLD, abdominal MRI during 2009-2016, and liver biopsy within 6 months of MRI or prior biopsy showing cirrhosis. Reports reviewed for mention of CLD and associated descriptors. Findings stratified into categories: normal/no mention of CLD; changes of CLD without qualitative descriptor; mild/early; moderate; severe/advanced and cirrhosis. Descriptive ranges categorized to the lesser degree. Percent concordance/discordance of descriptors and Scheuer stage (F0-F4), false positive (FP), false negative (FN) and sensitivity/specificity calculated.

RESULTS

309 patients, median age 54 (24-74). 91% had HCV (282/309), 7% HBV and 2% both HBV/HCV. Biopsy showed 19% without CLD/F0; 8% F1, 15% F2, 15% F3 and 43% F4. 188 MRI reports (61%) stated CLD was present; however, 16 had no fibrosis on histopathology (9% FP). 39% (121/309) did not mention or stated no CLD; however, 78 had CLD on histopathology (64% FN). 59% of FN were early fibrosis (F1 or F2), 27% F3 and 11% F4. Overall sensitivity and specificity was 69% and 73%, respectively. 77% (145/188) of MRI reports used a descriptive qualifier when describing CLD. 10% were concordant with exact histopathology staging. Of discordant reports, 90% identified CLD but under-called severity.

CONCLUSION

Abdominal radiologists can detect CLD on MRI but degree of CLD is often under-called compared to histopathology suggesting radiologists should refrain from qualitative descriptors in assessing CLD on MRI and reaffirms the need for quantitative imaging.

Comparison of Urologist Satisfaction for Different Types of Prostate MRI Reports: A Large Sample Investigation

Objective

To evaluate urologist satisfaction on structured prostate MRI reports, including report with tumor-node-metastasis (TNM) staging (report B) and with Prostate Imaging Reporting and Data System (PI-RADS) score with/without TNM staging (report C, report with PI-RADS score only [report C-a] and report with PI-RADS score and TNM staging [C-b]) compared with conventional free-text report (report A).

Materials and Methods

This was a prospective comparative study. Altogether, 3015 prostate MRI reports including reports A, B, C-a, and C-b were rated by 13 urologists using a 5-point Likert Scale. A questionnaire was used to assess urologist satisfaction based on the following parameters: correctness, practicality, and urologist subjectivity. Kruskal-Wallis H-test followed by Nemenyi test was used to compare urologists' satisfaction parameters for each report type. The rate of urologist-radiologist recalls for each report type was calculated.

Results

Reports B and C including its subtypes had higher ratings of satisfaction than report A for overall satisfaction degree, and parameters of correctness, practicality, and subjectivity (p < 0.05). There was a significant difference between report B and C (p < 0.05) in practicality score, but no statistical difference was found in overall satisfaction degree, and correctness and subjectivity scores (p > 0.05). Compared with report C-b (p > 0.05), report B and C-a (p < 0.05) showed a significant difference in overall satisfaction degree and parameters of practicality and subjectivity. In terms of correctness score, neither report C-a nor C-b had a significant difference with report B (p > 0.05). No statistical difference was found between report C-a and C-b in overall satisfaction degree and all three parameters (p > 0.05). The rate of urologist-radiologist recalls for reports A, B, C-a and C-b were 29.1%, 10.8%, 18.1% and 11.2%, respectively.

Conclusion

Structured reports, either using TNM or PI-RADS are highly preferred over conventional free-text reports and lead to fewer report-related post-hoc inquiries from urologists.

Interobserver Agreement for the Computed Tomography Severity Grading Scales for Acute Traumatic Brain Injury

The purpose of this study was to determine the interobserver variability among providers of different specialties and levels of experience across five established computed tomography (CT) scoring systems for acute traumatic brain injury (TBI). One hundred cases were selected at random from a retrospective population of adult patients transported to our emergency department and subjected to a non-contrast head CT due to suspicion of TBI. Eight neuroradiologists and neurosurgeons in trainee (residents and fellows) and attending roles independently scored each non-contrast head CT scan on the Marshall, Rotterdam, Helsinki, Stockholm, and NeuroImaging Radiological Interpretation System (NIRIS) head CT scales. Interobserver variability of scale scores-overall and by specialty and level of training-was quantified using the intraclass correlation coefficient (ICC), and agreement with respect to National Institutes of Health Common Data Elements (NIH CDEs) was assessed using Cohen's kappa. All CT severity scoring systems showed high interobserver agreement as evidenced by high ICCs, ranging from 0.75-0.89. For all scoring systems, neuroradiologists (ICC range from 0.81-0.94) tended to have higher interobserver agreement than neurosurgeons (ICC range from 0.63-0.76). For all scoring systems, attendings (ICC range from 0.76-0.89) had similar interobserver agreement to trainees (ICC range from 0.73-0.89). Agreement with respect to NIH CDEs was high for ascertaining presence/absence of hemorrhage, skull fracture, and mass effect, with estimated kappa statistics of least 0.89. Acute TBI CT scoring systems demonstrate high interobserver agreement. These results provide scientific rigor for future use of these systems for the classification of acute TBI.

Use of an Online Crowdsourcing Platform to Assess Patient Comprehension of Radiology Reports and Colloquialisms

OBJECTIVE. The purpose of this study was to use an online crowdsourcing platform to assess patient comprehension of five radiology reporting templates and radiology colloquialisms. MATERIALS AND METHODS. In this cross-sectional study, participants were surveyed as patient surrogates using a crowdsourcing platform. Two tasks were completed within two 48-hour time periods. For the first crowdsourcing task, each participant was randomly assigned a set of radiology reports in a constructed reporting template and subsequently tested for comprehension. For the second crowdsourcing task, each participant was randomly assigned a radiology colloquialism and asked to indicate whether the phrase indicated a normal, abnormal, or ambivalent finding. RESULTS. A total of 203 participants enrolled for the first task and 1166 for the second within 48 hours of task publication. The payment totaled $31.96. Of 812 radiology reports read, 384 (47%) were correctly interpreted by the patient surrogates. Patient surrogates had higher rates of comprehension of reports written in the patient summary (57%, p < 0.001) and traditional unstructured in combination with patient summary (51%, p = 0.004) formats than in the traditional unstructured format (40%). Most of the patient surrogates (114/203 [56%]) expressed a preference for receiving a full radiology report via an electronic patient portal. Several radiology colloquialisms with modifiers such as "low," "underdistended," and "decompressed" had low rates of comprehension. CONCLUSION. Use of the crowdsourcing platform is an expeditious, cost-effective, and customizable tool for surveying laypeople in sentiment- or task-based research. Patient summaries can help increase patient comprehension of radiology reports. Radiology colloquialisms are likely to be misunderstood by patients.

Optimizing Radiology Reports for Patients and Referring Physicians: Mitigating the Curse of Knowledge

As the movement for increased transparency in healthcare continues, more and more patients are accessing their imaging reports via patient portals. The shift to structured radiology reports has increased report clarity for referring providers and is supported by most radiologists. When radiologists address the clinical question that was posed, avoid the use of abbreviations, and create a report impression that is as simple as possible, we provide real added value via effective communication through our reports. In creating our reports with the patient in mind, and specifically knowing that many patients now directly review their imaging reports, we must be cognizant of the "curse of knowledge." The curse of knowledge is a cognitive bias that exists when we assume others have the background to understand our often complex radiology reports. Striving to mitigate the curse of knowledge is important for both patients and referring providers reading our reports, and a report impression that is presented as simply as possible in "lay language" is one tangible step toward this goal. Educating our residents and fellows about these important considerations as they create their reports is imperative to their success as radiologists.

Towards data-driven medical imaging using natural language processing in patients with suspected urolithiasis

OBJECTIVE

The majority of radiological reports are still written as free text and lack structure. Further evaluation of free-text reports is difficult to achieve without a great deal of manual effort, and is not possible in everyday clinical practice. This study aims to automatically capture clinical information and positive hit rates from narrative radiological reports of suspected urolithiasis using natural language processing (NLP).

METHODS

Narrative reports of low dose computed tomography (CT) of the retroperitoneum from April 2016 to July 2018 (n = 1714) were analyzed using NLP. These free-text reports were automatically structured based on RadLex concepts. Manual feedback was used to test and train the NLP engine to further enhance the performance. The chi-squared test, phi coefficient, and logistic regression analysis were performed to determine the effect of clinical information on the positive hit rate of urolithiasis.

RESULTS

Urolithiasis was affirmed in 72 % of the reports; in 38 % at least one stone was described in the kidneys, and in 45 % at least one stone was described in the ureter. Clinical information, such as previous stone history and obstructive uropathy, showed a strong correlation with confirmed urolithiasis (p = 0.001). Previous stone history and the combination of obstructive uropathy and loin pain had the highest association with positive urolithiasis (p < 0.001).

CONCLUSION

Applying this NLP approach to already existing free-text reports allows the conversion of such reports into a structured form. This may be valuable for epidemiological studies, to evaluate the appropriateness of CT examinations, or to answer a variety of research questions.

Use of a Macro as Nudge Factor in Communication Between Radiologists and Referring Physicians

INTRODUCTION/METHODS

Radiologists provide value through communication of imaging findings. We outline a quality improvement effort using a dedicated dictation macro as a behavioral nudge to increase direct communication between radiologists and referring physicians. Use of the macro was encouraged by departmental leadership and publicised widely prior to implementation. Monthly data regarding the use of the macro and corresponding departmental volumes were acquired over a 24 month period.

RESULTS

Over the 24-month study period, there were 1,334,555 total exams performed and 52,276 total communications (3.90%; monthly range 2.21-4.67%). The greatest increase in adoption rate occurred during the initial 4-month period, with sustained rates of communication achieved after month 4. Results were more frequently communicated to a clinician when a resident trainee was involved in the dictation process. The greatest number of documented communications was for x-ray, followed by Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), and nuclear medicine. Inpatient studies (7.23%) were communicated at a statistically significantly higher rate than Emergency Department (ED) (3.86%) or Outpatient (OP) studies (1.31%), P < 0.0001 for all comparisons. The rate of documented communication steadily increased across all patient classes.

CONCLUSION

Our findings demonstrate that simple interventions to increase the rate of documented communication can have durable results, and highlight the critical role radiologists play in timely and effective patient care delivery. Introduction of a communication macro coupled with departmental nudges resulted in increased direct communication of imaging results. This effort has promoted mutual engagement between radiologists and their colleagues, and demonstrates the active role of radiologists in direct imaging consultation.

Ambiguous Findings on Radiographs

Findings with uncertain clinical significance are frequently encountered on radiographs. A structure or opacity visible on radiographs could be due to several causes ranging from artifact or external structure to malignancy or a life-threatening process. The approach that a radiologist chooses to address ambiguous findings can have a significant impact on a patient's health. In this article we discuss the causes and impact of ambiguous findings on radiographs. We also discuss the various strategies radiologists can adopt to maximize clinical value and, when needed, reach a definite diagnosis.

Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs

BACKGROUND

Data used for training of deep learning networks usually needs large amounts of accurate labels. These labels are usually extracted from reports using natural language processing or by time-consuming manual review. The aim of this study was therefore to develop and evaluate a workflow for using data from structured reports as labels to be used in a deep learning application.

MATERIALS AND METHODS

We included all plain anteriorposterior radiographs of the ankle for which structured reports were available. A workflow was designed and implemented where a script was used to automatically retrieve, convert, and anonymize the respective radiographs of cases where fractures were either present or absent from the institution's picture archiving and communication system (PACS). These images were then used to retrain a pretrained deep convolutional neural network. Finally, performance was evaluated on a set of previously unseen radiographs.

RESULTS

Once implemented and configured, completion of the whole workflow took under 1 h. A total of 157 structured reports were retrieved from the reporting platform. For all structured reports, corresponding radiographs were successfully retrieved from the PACS and fed into the training process. On an unseen validation subset, the model showed a satisfactory performance with an area under the curve of 0.850 (95% CI 0.634-1.000) for detection of fractures.

CONCLUSION

We demonstrate that data obtained from structured reports written in clinical routine can be used to successfully train deep learning algorithms. This highlights the potential role of structured reporting for the future of radiology, especially in the context of deep learning.

Artificial intelligence in medical education

Artificial intelligence (AI) is a growing phenomenon, and will soon facilitate wide-scale changes in many professions, including medical education. In order for medical educators to be properly prepared for AI, they will need to have at least a fundamental knowledge of AI in relation to learning and teaching, and the extent to which it will impact on medical education. This Guide begins by introducing the broad concepts of AI by using fairly well-known examples to illustrate AI's implications within the context of education. It then considers the impact of AI on medicine and the implications of this impact for educators trying to educate future doctors. Drawing on these strands, it then identifies AI's direct impact on the methodology and content of medical education, in an attempt to prepare medical educators for the changing demands and opportunities that are about to face them because of AI.

Communicating with the hepatobiliary surgeon through structured report

Communicating radiological findings to hepatobiliary surgeons is not an easy task due to the complexity of liver imaging, coexistence of multiple hepatic lesions and different surgical treatment options. Recently, the adoption and implementation of structured report in everyday clinical practice has been supported to achieve higher quality, more reproducibility in communication and closer adherence to current guidelines. In this review article, we will illustrate the main benefits, strengths and limitations of structured reporting, with particular attention on the advantages and challenges of structured template in the preoperative evaluation of cirrhotic and non-cirrhotic patients with focal liver lesions. Structured reporting may improve the preoperative evaluation, focusing on answering specific clinical questions that are requested by hepatobiliary surgeons in candidates to liver resection.

The value of structured radiology reports to categorize intracranial metastases following radiation therapy

PURPOSE

Radiology descriptions of intracranial metastases following radiotherapy are often imprecise. This study sought to improve such reports by creating and disseminating a structured template that encourages discrete categorization of intracranial lesions.

METHODS

Following initiation of the structured template, a retrospective review assessed patients with intracranial metastases that underwent radiotherapy, comparing 'pre-template' with 'post-template' reports. A total of 139 patients were included; 94 patients (67.6%) were imaged pre-template, 45 (32.4%) post-template. Reports were assessed for discrete versus non-specific descriptions of lesions: '(presumed) new metastases', 'treated metastases', and 'indeterminate lesions'. Non-specific language was subdivided based on the type of lesion(s) described: e.g. 'stable enhancing foci' was deemed a non-specific description of 'treated metastases'.

RESULTS

Non-specific descriptions of lesions were used in 25/94 reports (26.6%) pre-template, and eight reports (17.8%) post-template. No significant difference was found in the frequency of inappropriate/ambiguous descriptions of intracranial lesions following template initiation (P = 0.52). However, only 27/45 (60.0%) of the reports in the post-template time period used the structured report; the other reports were written as free prose. Of the reports that did use the structured template, the authors used significantly less ambiguous language structured template (P = 0.02).

CONCLUSION

When utilized, a structured report template resulted in decreased non-specific descriptions and improved discrete characterization of intracranial metastases in patients treated with radiation. However, the frequency of non-specific language usage before and after template initiation was unchanged, probably due to poor compliance with template utilization.

The state of structured reporting: the nuance of standardized language

Radiology reports are the principal form of communication with the referring provider. Unfortunately, they can be a form of communication riddled with errors and inscrutable statements burying the intended meaning, failing to achieve the main task for which it was made: communicating key imaging findings as they pertain to the clinical question being posed. Structured reporting is a multifaceted and modular solution to problematic reports, with variable iterations and benefits. Structured reports have been adapted across departments and even national societies, with standardized format, content and language. Newer developments include contextual reporting and common data elements. Herein, we discuss the various forms and levels of structured reporting and the latest advancements, as well as the general acceptance within radiology. We also discuss some areas for improvement as the practice of structured reporting matures.

Big data, artificial intelligence, and structured reporting

The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult. This is mostly because a significant portion of information is handled as a collection of narrative texts and interoperability is still lacking. This review aims at giving a brief overview on how structured reporting can help to facilitate research in artificial intelligence and the context of big data.