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Background To compare the quality of free-text reports (FTR) and structured reports (SR) of brain magnetic resonance imaging (MRI) examinations in patients following mechanical thrombectomy for acute stroke treatment. Methods A template for SR of brain MRI examinations based on decision trees was designed and developed in house and applied to twenty patients with acute ischemic stroke in addition to FTR. Two experienced stroke neurologists independently evaluated the quality of FTR and SR regarding clarity, content, presence of key features, information extraction, and overall report quality. The statistical analysis for the differences between FTR and SR was performed using the Mann–Whitney U-test or the Chi-squared test. Results Clarity (p < 0.001), comprehensibility (p < 0.001), inclusion of relevant findings (p = 0.016), structure (p = 0.005), and satisfaction with the content of the report for immediate patient management (p < 0.001) were evaluated significantly superior for the SR by both neurologist raters. One rater additionally found the explanation of the patient’s clinical symptoms (p = 0.003), completeness (p < 0.009) and length (p < 0.001) of SR to be significantly superior compared to FTR and stated that there remained no open questions, requiring further consultation of the radiologist (p < 0.001). Both neurologists preferred SR over FTR. Conclusions The use of SR for brain magnetic resonance imaging may increase the report quality and satisfaction of the referring physicians in acute ischemic stroke patients following mechanical thrombectomy. Trial registration Retrospectively registered.

ObjectivesTo update the 2012 ESGAR consensus guidelines on the acquisition, interpretation and reporting of magnetic resonance imaging (MRI) for clinical staging and restaging of rectal cancer.MethodsFourteen abdominal imaging experts from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) participated in a consensus meeting, organised according to an adaptation of the RAND-UCLA Appropriateness Method. Two independent (non-voting) Chairs facilitated the meeting. 246 items were scored (comprising 229 items from the previous 2012 consensus and 17 additional items) and classified as ‘appropriate’ or ‘inappropriate’ (defined by ≥ 80 % consensus) or uncertain (defined by < 80 % consensus).ResultsConsensus was reached for 226 (92 %) of items. From these recommendations regarding hardware, patient preparation, imaging sequences and acquisition, criteria for MR imaging evaluation and reporting structure were constructed. The main additions to the 2012 consensus include recommendations regarding use of diffusion-weighted imaging, criteria for nodal staging and a recommended structured report template.ConclusionsThese updated expert consensus recommendations should be used as clinical guidelines for primary staging and restaging of rectal cancer using MRI.Key Points• These guidelines present recommendations for staging and reporting of rectal cancer.• The guidelines were constructed through consensus amongst 14 pelvic imaging experts.• Consensus was reached by the experts for 92 % of the 246 items discussed.• Practical guidelines for nodal staging are proposed.• A structured reporting template is presented.

Objectives Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists’ reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports. Materials and methods We developed an approach to controlling the LLM output, ensuring the validity and completeness of structured reports produced by a locally hosted Llama-2-70B-chat model. A dataset with de-identified narrative chest radiograph (CXR) reports was compiled retrospectively. It included 202 English reports from a publicly available MIMIC-CXR dataset and 197 German reports from our university hospital. Senior radiologist prepared a detailed, fully structured reporting template with 48 question-answer pairs. All reports were independently structured by the LLM and two human readers. Bayesian inference (Markov chain Monte Carlo sampling) was used to estimate the distributions of Matthews correlation coefficient (MCC), with [−0.05, 0.05] as the region of practical equivalence (ROPE). Results The LLM generated valid structured reports in all cases, achieving an average MCC of 0.75 (94% HDI: 0.70–0.80) and F1 score of 0.70 (0.70–0.80) for English, and 0.66 (0.62–0.70) and 0.68 (0.64–0.72) for German reports, respectively. The MCC differences between LLM and humans were within ROPE for both languages: 0.01 (−0.05 to 0.07), 0.01 (−0.05 to 0.07) for English, and −0.01 (−0.07 to 0.05), 0.00 (−0.06 to 0.06) for German, indicating approximately comparable performance. Conclusion Locally hosted, open-source LLMs can automatically structure free-text radiology reports with approximately human accuracy. However, the understanding of semantics varied across languages and imaging findings. Key Points Question Why has structured reporting not been widely adopted in radiology despite clear benefits and how can we improve this? Findings A locally hosted large language model successfully structured narrative reports, showing variation between languages and findings. Critical relevance Structured reporting provides many benefits, but its integration into the clinical routine is limited. Automating the extraction of structured information from radiology reports enables the capture of structured data while allowing the radiologist to maintain their reporting workflow.

Structured reporting is advocated as a means of improving reporting in radiology to the ultimate benefit of both radiological and clinical practice. Several large initiatives are currently evaluating its potential. However, with numerous characterizations of the term in circulation, “structured reporting” has become ambiguous and is often confused with “standardization,” which may hamper proper evaluation and implementation in clinical practice. This paper provides an overview of interpretations of structured reporting and proposes a clear definition that differentiates structured reporting from standardization. Only a clear uniform definition facilitates evidence-based implementation, enables evaluation of its separate components, and supports (meta-)analyses of literature reports.

Synoptic reporting increases completeness and standardization of surgical pathology reports and thereby contributes to an increased quality of clinical cancer care. Nevertheless, its widespread practical implementation remains a challenge, which is in part related to the effort required for setup and maintenance of database structures. This prompted us to assess the effect of a simple template-based, database-free system for synoptic reporting on completeness of surgical pathology reports. For this purpose, we analyzed 200 synoptic reports (100 colon and 100 lung cancer resections each) for completeness as required by the pertinent College of American Pathologists (CAP) protocols and compared these to a control dataset of 200 narrative reports. Introduction of template-based synoptic reporting resulted in improved completeness (98% of mandatory data elements) as compared to narrative reports (77%). Narrative reports showed a high degree of completeness for data elements covered by previously existing dictation templates. In conclusion, template-based synoptic reporting without underlying database structure can be a useful transitory phase in the implementation of synoptic reporting. It can result in a similar degree of completeness as reported in the literature for database solutions and provides other benefits of synoptic reporting while facilitating its implementation.

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.

Objectives To assess the opinion on structured reporting (SR) and its usage by radiologist members of the Italian Society of Medical Radiology (SIRM) via an online survey. Methods All members received an email invitation to join the survey as an initiative by the SIRM Imaging Informatics Chapter. The survey included 10 questions about demographic information, definition of radiological SR, its usage in everyday practice, perceived advantages and disadvantages over conventional reporting and overall opinion about SR. Results 1159 SIRM members participated in the survey. 40.3 % of respondents gave a correct definition of radiological SR, but as many as 56 % of them never used it at work. Compared with conventional reporting, the most appreciated advantages of SR were higher reproducibility (70.5 %), better interaction with referring clinicians (58.3 %) and the option to link metadata (36.7 %). Risk of excessive simplification (59.8 %), template rigidity (56.1 %) and poor user compliance (42.1 %) were the most significant disadvantages. Overall, most respondents (87.0 %) were in favour of the adoption of radiological SR. Conclusions Most radiologists were interested in radiological SR and in favour of its adoption. However, concerns about semantic, technical and professional issues limited its diffusion in real working life, encouraging efforts towards improved SR standardisation and engineering. Key Points • Despite radiologists’ awareness, radiological SR is little used in working practice. • Perceived SR advantages are reproducibility, better clinico-radiological interaction and link to metadata. • Perceived SR disadvantages are excessive simplification, template rigidity and poor user compliance. • Improved standardisation and engineering may be helpful to boost SR diffusion.

Rectal MR is the key diagnostic exam at initial presentation for rectal cancer patients. It is the primary determinant in establishing clinical stage for the patient and greatly impacts the clinical decision-making process. Consequently, structured reporting for MR is critically important to ensure that all required information is provided to the clinical care team. The SAR initial staging reporting template has been constructed to address these important items, including locoregional extent and factors impacting the surgical approach and management of the patient. Potential outputs to each item are defined, requiring the radiologist to commit to a result. This provides essential information to the surgeon or oncologist to make specific treatment deisions for the patient. The SAR Initial Staging MR reporting template has now been officially adopted by the NAPRC (National Accreditation Program for Rectal Cancer) under the American College of Surgery. With the recent revisions to the reporting template, this user guide has been revamped to improve its practicality and support to the radiologist to complete the structured report. Each line item of the report is supplemented with clinical perspectives, images, and illustrations to help the radiologist understand the potential implications for a given finding. Common errors and pitfalls to avoid are highlighted. Ideally, rectal MR interpretation should not occur in a vacuum but in the context of a multi-disciplinary tumor board to ensure that healthcare providers use common terminology and share a solid understanding of the strengths and weaknesses of MR.

The use of structured reports (SR) has been shown to improve report completeness, time efficiency and interrater reliability in head and neck sonography (HNS). However, no data exists to date on the influence on report accuracy in terms of correct interpretation of findings. The aim of this study was to evaluate report accuracy as well as completeness using SR and free text reports (FTR). 128 participants of certified HNS courses were randomly assigned to create either SRs or FTR of predefined clinical cases. Demographic data, clinical training level and ultrasound experience of participants were documented prior to randomization. Each case included ultrasound images and clinical histories representing typical pathologies in HNS. Reports were independently evaluated by DEGUM-certified otorhinolaryngologists using standardized templates with respect to accuracy and completeness. SR demonstrated significantly higher accuracy ( p  < 0.001) as well as completeness ( p  < 0.001) compared to FTR. A strong positive correlation was found between completeness and accuracy using SR ( r  = 0.30, p  < 0.01) only. Additionally, use of SR was the only significant predictor of improved report accuracy ( p  < 0.01) and completeness ( p  < 0.01). Structured reporting significantly improves both completeness and accuracy in head and neck ultrasound reports. The use of SR may contribute to more consistent reporting quality in clinical and educational settings.

ObjectivesArtificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools.MethodsFindings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports.ResultsChest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001).ConclusionThe AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future.Critical relevance statementWith the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest.Key points• A pipeline was developed for automated transfer of AI results into structured reports.• Pipeline chest X-ray reporting is faster than free-text or conventional structured reports.• Report quality was also rated higher for reports created with the pipeline.• The pipeline offers efficient, standardized AI integration into the clinical workflow.