Structured MRI reporting increases completeness of radiological reports and requesting physicians' satisfaction in the diagnostic workup for pelvic endometriosis

Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports

Background

Structured reporting has been demonstrated to increase report completeness and to reduce error rate, also enabling data mining of radiological reports. Still, structured reporting is perceived by radiologists as a fragmented reporting style, limiting their freedom of expression.

Purpose

A deep learning-based natural language processing method was developed to automatically convert unstructured COVID-19 chest CT reports into structured reports.

Methods

Two hundred-two COVID-19 chest CT were retrospectively reviewed by two experienced radiologists, who wrote for each exam a free-form text radiological report and coherently filled the template provided by the Italian Society of Medical and Interventional Radiology, used as ground-truth. A semi-supervised convolutional neural network was implemented to extract 62 categorical variables from the report. Two iterations were carried-out, the first without fine-tuning, the second one performing a fine-tuning. The performance was measured using the mean accuracy and the F1 mean score. An error analysis was performed to identify errors entirely attributable to incorrect processing of the model.

Results

The algorithm achieved a mean accuracy of 93.7% and an F1 score 93.8% in the first iteration. Most of the errors were exclusively attributable to wrong inference (46%). In the second iteration the model achieved for both parameters 95,8% and percentage of errors attributable to wrong inference decreased to 26%.

Conclusions

The convolutional neural network achieved an optimal performance in the automated conversion of free-form text into structured radiological reports, overcoming all the limitation attributed to structured reporting and finally paving the way for data mining of radiological report.

MRI of endometriosis in correlation with the #Enzian classification: applicability and structured report

Endometriosis represents one of the most common causes of life-impacting chronic pelvic pain and female infertility. Magnetic resonance imaging (MRI) plays an increasing role in the diagnosis and mapping of endometriosis, while diagnostic laparoscopy currently tends to be reserved for the patients with negative imaging results. The #Enzian, published in 2021, proposes a new comprehensive classification system of endometriosis, combining a complete staging of deep infiltrative endometriosis with the evaluation of peritoneal/ovarian/tubal localizations and the presence of adenomyosis. This article addresses in detail the applicability of the #Enzian classification, primarily based on surgical findings, to the MRI evaluation of the endometriosis. Overall, there is a significant matching between MRI features and the #Enzian classification criteria, two different perspectives of endometriosis mapping, with different goals and levels of detail. The main discrepancy lies in the evaluation of tubo-ovarian condition, which is not fully assessable by MRI. Furthermore, as endometriosis is a complex disease, usually multifocal, that can present with a myriad of imaging findings, MRI reporting should be clear and well organized. The authors group, both radiologists and gynecologists, propose a structured MRI report of endometriosis in correlation with the #Enzian classification, merging the detailed anatomical and pre-operative information provided by the MRI with the benefits of a comprehensive classification system of endometriosis in the clinical practice and research field.Critical relevance statement This article addresses in detail the applicability of the #Enzian classification, primarily based on surgical findings, to the MRI evaluation of the endometriosis and proposes a #Enzian-based structured MRI report.

Structured CT reporting of pancreatic ductal adenocarcinoma: impact on completeness of information and interdisciplinary communication for surgical planning

PURPOSE

With the rise in popularity of structured reports in radiology, we sought to evaluate whether free-text CT reports on pancreatic ductal adenocarcinoma (PDAC) staging at our institute met published guidelines and assess feedback of pancreatic surgeons comparing free-text and structured report styles with the same information content.

METHODS

We retrospectively evaluated 298 free-text preoperative CT reports from 2015 to 2017 for the inclusion of key tumor descriptors. Two surgeons independently evaluated 50 free-text reports followed by evaluation of the same reports in a structured format using a 7-question survey to assess the usefulness and ease of information extraction. Fisher's exact test and Chi-square test for independence were utilized for categorical responses and an independent samples t test for comparing mean ratings of report quality as rated on a 5-point Likert scale.

RESULTS

The most commonly included descriptors in free-text reports were tumor location (99%), liver lesions (97%), and suspicious lymph nodes (97%). The most commonly excluded descriptors were variant arterial anatomy and peritoneal/omental nodularity, which were present in only 23% and 42% of the reports, respectively. For vascular involvement, a mention of the presence or absence of perivascular disease with the main portal vein was most commonly included (87%). Both surgeons' rating of overall report quality was significantly higher for structured reports (p < 0.001).

CONCLUSION

Our results indicate that free-text reports may not include key descriptors for staging PDAC. Surgeons rated structured reports that presented the same information as free-text reports but in a template format superior for guiding clinical management, convenience of use, and overall report quality.

Performance of imaging interpretation, intra- and inter-reader agreement for diagnosis of pelvic endometriosis: comparison between an abbreviated and full MRI protocol

PURPOSE

To compare the performance of imaging interpretation, intra- and inter-reader agreement between an abbreviated (aMRI) and full (fMRI) MRI protocol for diagnosis of pelvic endometriosis.

METHODS

Seventy consecutive fMRI exams performed under suspicion of pelvic endometriosis were selected. Four radiologists (Rd) (1-10 years experience) independently evaluated presence/absence of endometriosis at 9 anatomic sites (AS). The readers evaluated aMRI (coronal T2 TSE volumetric images and axial T1 GRE fat-sat without contrast, extracted from fMRI) and fMRI protocols randomly, with at least 4 weeks interval between readings. The degree of confidence for diagnosis at each AS was evaluated with a 1-3 Likert Scale (1: low; 3: high). Intra- and inter-reader agreement between protocols were evaluated by kappa statistics and took reading experience into account. The gold standard for assessing the performance of imaging interpretation (sensitivity, specificity and accuracy) used a consensus reading of two other Rd (> 15 years experience).

RESULTS

There was no significant difference in the accuracy of imaging interpretation between the abbreviated (0.83-0.86) and full (0.83-0.87) protocols (p = 0.15). Intra-reader agreement between protocols ranged from substantial to almost perfect (0.74-0.96). A substantial inter-reader agreement was found for both protocols for readers with similar levels of experience (0.67-0.69) and in the global analysis (0.66 for both protocols). No difference was found in terms of degree of confidence between protocols, for all readers.

CONCLUSION

An abbreviated MRI protocol for pelvic endometriosis provided an accuracy of interpretation comparable to that of a complete protocol, with similar degrees of confidence and reproducibility, regardless the level of experience.