Bridging the Gap between Structured and Free-form Radiology Reporting: A Case-study on Coronary CT Angiography

Free-form radiology reports associated with coronary computed tomography angiography (CCTA) include nuanced and complicated linguistics to report cardiovascular disease. Standardization and interpretation of such reports is crucial for clinical use of CCTA. Coronary Artery Disease Reporting and Data System (CAD-RADS) has been proposed to achieve such standardization by implementing a strict template-based report writing and assignment of a score between 0 and 5 indicating the severity of coronary artery lesions. Even after its introduction, free-form unstructured report writing remains popular among radiologists. In this work, we present our attempts at bridging the gap between structured and unstructured reporting by natural language processing. We present machine learning models that while being trained only on structured reports, can predict CAD-RADS scores by analysis of free-text of unstructured radiology reports. The best model achieves 98% accuracy on structured reports and 92% 1-margin accuracy (difference of \le 1 in the predicted and the actual scores) for free-form unstructured reports. Our model also performs well under very difficult circumstances including nuanced and widely varying terminology used for reporting cardiovascular functions and diseases, scarcity of labeled data for training our model, and uneven class label distribution.

Radiology reporting in oncology—oncologists’ perspective

Background Structured reporting and standardized criteria are increasingly recognized as means of improving both radiological and clinical practice by allowing for better content and clarity. Our aim was to examine oncologists’ opinions and expectations concerning the radiologist’s report to identify general needs in daily practice and ways to improve interdisciplinary communication. Methods A 19-question survey was sent to 230 oncologists from three different countries (France, Romania, Switzerland) identified on the online web pages of different hospitals and private clinics. The survey was sent by electronic mail with an online survey program (Google Forms®). All recipients were informed of the purpose of the study. The data were collected by the online survey program and analysed through filtering the results and cross-tabulation. Results A total of 52 responses were received (response rate of 22.6%). The majority of the respondents (46/52, 88%) preferred the structured report, which follows a predefined template. Most of the respondents (40/52, 77%) used RECIST 1.1 or iRECIST in tumour assessment. Nearly half of the oncologists (21/52, 40%) measured 1–3 cases per week. On a 10-point Likert scale, 34/52 (65%) oncologists rated their overall level of satisfaction with radiologists’ service between 7 and 10. In contrast, 12/52 (19%) oncologists rated the radiologists’ service between 1 and 4. Moreover, 42/52 (80%) oncologists acknowledged that reports created by a radiologist with a subspecialty in oncologic imaging were superior to those created by a general radiologist. Conclusion Structured reports in oncologic patients and the use of RECIST criteria are preferred by oncologists in their daily clinical practice, which signals the need for radiologists also to implement such reports to facilitate communication. Furthermore, most of the oncologists we interviewed recognized the added value provided by radiologists specializing in oncologic imaging. Because this subspecialty is present in only a few countries, generally in large clinics, further training might become a challenge; nevertheless, intensive efforts should be made to enhance expertise in cancer imaging.

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

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.

Computer-Assisted Reporting and Decision Support in Standardized Radiology Reporting for Cancer Imaging

PURPOSE Recent advances in structured reporting are providing an opportunity to enhance cancer imaging assessment to drive value-based care and improve patient safety. METHODS The computer-assisted reporting and decision support (CAR/DS) framework has been developed to enable systematic ingestion of guidelines as clinical decision structured reporting tools embedded within the radiologist's workflow. RESULTS CAR/DS tools can reduce the radiology reporting variability and increase compliance with clinical guidelines. The lung cancer use-case is used to describe various scenarios of a cancer imaging structured reporting pathway, including incidental findings, screening, staging, and restaging or continued care. Various aspects of these tools are also described using cancer-related examples for different imaging modalities and applications such as calculators. Such systems can leverage artificial intelligence (AI) algorithms to assist with the generation of structured reports and there are opportunities for new AI applications to be created using the structured data associated with CAR/DS tools. CONCLUSION These AI-enabled systems are starting to allow information from multiple sources to be integrated and inserted into structured reports to drive improvements in clinical decision support and patient care.

High filtration in interventional practices reduces patient radiation doses but not always scatter radiation doses

Objectives: In fluoroscopy-guided interventional practices, new dose reduction systems have proved to be efficient in the reduction of patient doses. However, it is not clear whether this reduction in patient dose is proportionally transferred to operators’ doses. This work investigates the secondary radiation fields produced by two kinds of interventional cardiology units from the same manufacturer with and without dose reduction systems. Methods: Data collected from a large sample of clinical procedures over a 2-year period (more than 5000 procedures and 340,000 radiation events) and the DICOM radiation dose structured reports were analysed. Results: The average cumulative Hp(10) per procedure measured at the C-arm was similar for the standard and the dose reduction systems (452 vs 476 μSv respectively). The events analysis showed that the ratio Hp(10)/KAP at the C-arm was (mean ± SD) 5 ± 2, 10 ± 4, 14 ± 4 and 14 ± 6 μSv·Gy−1·cm−2 for the beams with no added filtration, 0.1, 0.4 and 0.9 mm Cu respectively and suggested that the main cause for the increment of the ratio Hp(10)/KAP vs the “standard system” is the use of higher beam filtration in the “dose reduction” system. Conclusion: Dose reduction systems are beneficial to reduce KAP in patients and their use should be encouraged, but they may not be equally effective to reduce occupational doses. Interventionalists should not overlook their own personal protection when using new technologies with dose reduction systems. Advances in knowledge: Dose reduction technology in interventional systems may increase scatter dose for operators. Personal protection should not be overlooked with dose reduction systems.

The role of structured reporting and structured operation planning in functional endoscopic sinus surgery

Computed tomography (CT) scans represent the gold standard in the planning of functional endoscopic sinus surgeries (FESS). Yet, radiologists and otolaryngologists have different perspectives on these scans. In general, residents often struggle with aspects involved in both reporting and operation planning. The aim of this study was to compare the completeness of structured reports (SR) of preoperative CT images and structured operation planning (SOP) to conventional reports (CR) and conventional operation planning (COP) to potentially improve future treatment decisions on an individual level. In total, 30 preoperative CT scans obtained for surgical planning of patients scheduled for FESS were evaluated using SR and CR by radiology residents. Subsequently, otolaryngology residents performed a COP using free texts and a SOP using a specific template. All radiology reports and operation plannings were evaluated by two experienced FESS surgeons regarding their completeness for surgical planning. User satisfaction of otolaryngology residents was assessed by using visual analogue scales. Overall radiology report completeness was significantly higher using SRs regarding surgically important structures compared to CRs (84.4 vs. 22.0%, p<0.001). SOPs produced significantly higher completeness ratings (97% vs. 39.4%, p<0.001) regarding pathologies and anatomical variances. Moreover, time efficiency was not significantly impaired by implementation of SR (148 s vs. 160 s, p = 0.61) and user satisfaction was significantly higher for SOP (VAS 8.1 vs. 4.1, p<0.001). Implementation of SR and SOP results in a significantly increased completeness of radiology reports and operation planning for FESS. Consequently, the combination of both facilitates surgical planning and may decrease potential risks during FESS.

Evaluation of optimal education level to implement structured reporting into ultrasound training

Aims: Reporting of head and neck ultrasound (HNU) has been outlined to be a major obstacle during ultrasound training due to a lack of standardized structure, content and terminology. Consequently, overall report quality differs significantly between various examiners posing a severe risk factor for information loss and miscommunication. Therefore, the present study’s purpose is to compare the overall quality of free text reports (FTR) and structured reports (SR) of HNU at various stages of training in order to determine the optimal educational level to implement SR.Material and methods: Typical pathologies in HNU were reported upon using SR and FTR by medical students, junior residents and senior residents. The reports were assessed for overall quality, time efficiency and readability. Additionally, user satisfaction was determined using a questionnaire.Results: SRs exhibited a significantly superior report quality (93.1% vs. 45.6%, p<0.001) at all training levels. Overall time efficiency was significantly better for SRs, especially at the stages of medical school and early residency (89.4 s vs. 160.2 s., p<0.001). Using structured reporting also increased user satisfaction significantly (VAS 8.6 vs. 3.9, p<0.001).Conclusions: Implementing structured reporting of HNU results in a superior report quality at all training stages. Greatest benefits for time efficiency are achieved by implementation during medical school. Therefore, structured reporting of HNU should be implemented early on in the training of HNU.