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4.2.4.2-1: Build Template Process Flow in MRRT Profile 33.4.2.5 Use Case #5: Manage Template 33.4.2.5.1 Manage Template Description 365 A radiologist has decided that a particular template should be retired, due to replacement to a new template. The radiologist may use a Report Template Creator to edit a template that changes the metadata. That template is stored in a Report Template Manager where it available for later retrieval by a Report Creator.
Source publication
In 2013, the Integrating the Healthcare Enterprise (IHE) Radiology workgroup developed the Management of Radiology Report Templates (MRRT) profile, which defines both the format of radiology reporting templates using an extension of Hypertext Markup Language version 5 (HTML5), and the transportation mechanism to query, retrieve, and store these tem...
Citations
... Structured reporting facilitates data sharing and mining for clinical care or research and improves the quality of medical care (3). Although initiatives to promote structured reporting exist (4)(5)(6), unstructured reports written in free text remain. As radiology data are described mostly in terms of anatomic regions, text classification by anatomic domain could be used to organize the contents of free-text reports. ...
Purpose:
To assess whether transfer learning with a bidirectional encoder representations from transformers (BERT) model, pretrained on a clinical corpus, can perform sentence-level anatomic classification of free-text radiology reports, even for anatomic classes with few positive examples.
Materials and methods:
This retrospective study included radiology reports of patients who underwent whole-body PET/CT imaging from December 2005 to December 2020. Each sentence in these reports (6272 sentences) was labeled by two annotators according to body part ("brain," "head & neck," "chest," "abdomen," "limbs," "spine," or "others"). The BERT-based transfer learning approach was compared with two baseline machine learning approaches: bidirectional long short-term memory (BiLSTM) and the count-based method. Area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUC) were computed for each approach, and AUCs were compared using the DeLong test.
Results:
The BERT-based approach achieved a macro-averaged AUPRC of 0.88 for classification, outperforming the baselines. AUC results for BERT were significantly higher than those of BiLSTM for all classes and those of the count-based method for the "brain," "chest," "abdomen," and "others" classes (P values < .025). AUPRC results for BERT were superior to those of baselines even for classes with few labeled training data (brain: BERT, 0.95, BiLSTM, 0.11, count based, 0.41; limbs: BERT, 0.74, BiLSTM, 0.28, count based, 0.46; spine: BERT, 0.82, BiLSTM, 0.53, count based, 0.69).
Conclusion:
The BERT-based transfer learning approach outperformed the BiLSTM and count-based approaches in sentence-level anatomic classification of free-text radiology reports, even for anatomic classes with few labeled training data.Keywords: Anatomy, Comparative Studies, Technology Assessment, Transfer Learning Supplemental material is available for this article. © RSNA, 2023.
... These determine both the format of radiology report templates using version 5 of Hypertext Markup Language (HTML5) and the transporting mechanism to request, retrieve and stock these schedules. The radiology report was structured by using a series of "codified queries" integrated in the preselected sections of the T-Rex editor [49]. ...
Objectives
To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology.
Methods
A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach’s alpha (C α ) correlation coefficient.
Results
The final SR form included 118 items (6 in the “Patient Clinical Data” section, 4 in the “Clinical Evaluation” section, 9 in the “Imaging Protocol” section, and 99 in the “Report” section). The experts’ overall mean score and sum of scores were 4.77 (range 1–5) and 257.56 (range 206–270) in the first Delphi round, and 4.96 (range 4–5) and 208.44 (range 200–210) in the second round, respectively.
In the second Delphi round, the experts’ overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, C α was higher in the second round than in the first round (0.97 vs 0.87).
Conclusions
Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team.
... Moreover, RSNA started the so-called Reporting Initiative with the aim of developing and providing vendor-neutral reporting templates [45]. This led to the publication of the Management of Radiology Report Templates profile by IHE, which extensively describes the concepts and technical details for interoperable, standardized and structured report templates [46]. ...
The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.
... Such profiles determine both the format of radiology report templates [using version 5 of Hypertext Markup Language (HTML5)] and the transporting mechanism to request, retrieve, and stock these schedules. The radiology report was structured by using a series of "codified queries" integrated in the T-Rex editor's preselected sections (21). ...
Background
Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams.
Materials and Methods
A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.
Results
The final SR version was built by including n=16 items in the “Patient Clinical Data” section, n=13 items in the “Clinical Evaluation” section, n=8 items in the “Imaging Protocol” section, and n=17 items in the “Report” section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively.
Conclusions
The present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.
... org) by using a T-Rex template format, in line with IHE (Integrating Healthcare Enterprise) and the MRRT (management of radiology report templates) profiles, accessible as open-source software, with the technical support of Exprivia (Exprivia SpA, Bari, Italy). These determine both the format of radiology report templates [using version 5 of Hypertext Markup Language (HTML5)] and the transporting mechanism to request, retrieve and stock these schedules [22]. The radiology report was structured by using a series of "codified queries" integrated in the T-Rex editor's preselected sections [22]. ...
... These determine both the format of radiology report templates [using version 5 of Hypertext Markup Language (HTML5)] and the transporting mechanism to request, retrieve and stock these schedules [22]. The radiology report was structured by using a series of "codified queries" integrated in the T-Rex editor's preselected sections [22]. ...
... For example, in our report a significant section is dedicated to the description of peritoneal carcinosis. A correct evaluation of this (thereby referring to Sugarbaker's peritoneal carcinosis index [22]) allows for a stratification of patients and avoids unnecessary surgery. Using a checklist and a systematic search pattern may help to prevent such diagnostic errors. ...
Background
Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in colon cancer during the staging phase in order to improve communication between the radiologist, members of multidisciplinary teams and patients.
Materials and methods
A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.
Results
The final SR version was built by including n = 18 items in the “Patient Clinical Data” section, n = 7 items in the “Clinical Evaluation” section, n = 9 items in the “Imaging Protocol” section and n = 29 items in the “Report” section. Overall, 63 items were included in the final version of the SR. Both in the first and second round, all sections received a higher than good rating: a mean value of 4.6 and range 3.6–4.9 in the first round; a mean value of 5.0 and range 4.9–5 in the second round. In the first round, Cronbach’s alpha (Cα) correlation coefficient was a questionable 0.61. In the first round, the overall mean score of the experts and the sum of scores for the structured report were 4.6 (range 1–5) and 1111 (mean value 74.07, STD 4.85), respectively. In the second round, Cronbach’s alpha (Cα) correlation coefficient was an acceptable 0.70. In the second round, the overall mean score of the experts and the sum of score for structured report were 4.9 (range 4–5) and 1108 (mean value 79.14, STD 1.83), respectively. The overall mean score obtained by the experts in the second round was higher than the overall mean score of the first round, with a lower standard deviation value to underline greater agreement among the experts for the structured report reached in this round.
Conclusions
A wide implementation of SR is of critical importance in order to offer referring physicians and patients optimum quality of service and to provide researchers with the best quality data in the context of big data exploitation of available clinical data. Implementation is a complex procedure, requiring mature technology to successfully address the multiple challenges of user-friendliness, organization and interoperability.
... using a T-Rex template format, in line with IHE (Integrating the Healthcare Enterprise) and the MRRT (Management of Radiology Report Templates) profiles, accessible as open-source software, with the technical support of Exprivia (Exprivia SpA, Bari, Italy). These determine both the format of radiology report templates (using version 5 of HyperText Markup Language (HTML5)) and the transporting mechanism to request, retrieve, and stock these schedules [30]. The radiology report was structured using a series of "codified queries" integrated in the T-Rex editor's preselected sections [30]. ...
... These determine both the format of radiology report templates (using version 5 of HyperText Markup Language (HTML5)) and the transporting mechanism to request, retrieve, and stock these schedules [30]. The radiology report was structured using a series of "codified queries" integrated in the T-Rex editor's preselected sections [30]. ...
Background:
Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams.
Materials and methods:
A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.
Results:
The final CT-SR version was built by including n = 16 items in the "Patient Clinical Data" section, n = 11 items in the "Clinical Evaluation" section, n = 7 items in the "Imaging Protocol" section, and n = 18 items in the "Report" section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the "Patient Clinical Data" section, n = 11 items in the "Clinical Evaluation" section, n = 8 items in the "Imaging Protocol" section, and n = 14 items in the "Report" section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93.
Conclusions:
The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.
... This determines both the format of radiology report templates [using version 5 of Hypertext Markup Language (HTML5)] and the transporting mechanism to request, retrieve, and stock these schedules. The radiology report was structured by using a series of "codified queries" integrated in the T-Rex editor's preselected sections [17]. ...
Citation: Granata, V.; Pradella, S.; Cozzi, D.; Fusco, R.; Faggioni, L.; Coppola, F.; Grassi, R.; Maggialetti, N.; Buccicardi, D.; Lacasella, G.V.; et al. Computed Tomography Structured Reporting in the Staging of Lymphoma: A Delphi Consensus Proposal.
... using a T-Rex template format in line with the IHE (Integrating the Healthcare Enterprise) and MRRT (Management of Radiology Report Templates) profiles, accessible as open-source software, with the technical support of Exprivia™. These determined both the format of the radiology report templates (using version 5 of the Hypertext Markup Language (HTML5)) and the transporting mechanism used to request, get back and stock these schedules [25]. The radiology report was structured using a series of "codified queries" integrated into the T-Rex editor's preselected sections [25]. ...
... These determined both the format of the radiology report templates (using version 5 of the Hypertext Markup Language (HTML5)) and the transporting mechanism used to request, get back and stock these schedules [25]. The radiology report was structured using a series of "codified queries" integrated into the T-Rex editor's preselected sections [25]. ...
Background:
Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients.
Materials and methods:
A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation.
Results:
The final SR version was built by including 16 items in the "Patient Clinical Data" section, 4 items in the "Clinical Evaluation" section, 8 items in the "Exam Technique" section, 22 items in the "Report" section, and 5 items in the "Conclusion" section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1-5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4-5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach's alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report.
Conclusions:
The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.
... using a T-Rex template in Hypertext Markup Language (HTML) format in line with the IHE (Integrating Healthcare Enterprise) and the MRRT (management of radiology report templates) profile, accessible as open-source software, with the technical support of Exprivia. These determine both the format of the radiology report templates using both HTML5, and the transporting mechanism to request, get back and stock these schedules [18]. The radiology report was structured using a series of "codified queries" integrated into the T-Rex editor's preselected sections [18]. ...
... These determine both the format of the radiology report templates using both HTML5, and the transporting mechanism to request, get back and stock these schedules [18]. The radiology report was structured using a series of "codified queries" integrated into the T-Rex editor's preselected sections [18]. ...
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.
... The final structured report, resulting from the third round, was assembled on the radreport.org website of RSNA, through the T-Rex template editor, freely available as open source software, in HTML format according to the IHE (Integrating Healthcare Enterprise) MRRT (management of radiology report templates) profile, which defines both the format of radiology reporting templates using an extension of Hypertext Markup Language version 5 (HTML5) and the transportation mechanism to query, retrieve, and store these templates [18]. The report was built through a sequence of "coded questions," included in the predefined sections of the T-Rex editor [18]. ...
... website of RSNA, through the T-Rex template editor, freely available as open source software, in HTML format according to the IHE (Integrating Healthcare Enterprise) MRRT (management of radiology report templates) profile, which defines both the format of radiology reporting templates using an extension of Hypertext Markup Language version 5 (HTML5) and the transportation mechanism to query, retrieve, and store these templates [18]. The report was built through a sequence of "coded questions," included in the predefined sections of the T-Rex editor [18]. ...
Objectives:
The need of a standardized reporting scheme and language, in imaging of COVID-19 pneumonia, has been welcomed by major scientific societies. The aim of the study was to build the reporting scheme of chest CT in COVID-19 pneumonia.
Methods:
A team of experts, of the Italian Society of Medical and Interventional Radiology (SIRM), has been recruited to compose a consensus panel. They used a modified Delphi process to build a reporting scheme and expressed a level of agreement for each section of the report. To measure the internal consistency of the panelist ratings for each section of the report, a quality analysis based on the average inter-item correlation was performed with Cronbach's alpha (Cα) correlation coefficient.
Results:
The overall mean score of the experts and the sum of score were 3.1 (std.dev. ± 0.11) and 122 in the second round, and improved to 3.75 (std.dev. ± 0.40) and 154 in the third round. The Cronbach's alpha (Cα) correlation coefficient was 0.741 (acceptable) in the second round and improved to 0.789 in the third round. The final report was built in the management of radiology report template (MRRT) and includes n = 4 items in the procedure information, n = 5 items in the clinical information, n = 16 in the findings, and n = 3 in the impression, with overall 28 items.
Conclusions:
The proposed structured report could be of help both for expert radiologists and for the less experienced who are faced with the management of these patients. The structured report is conceived as a guideline, to recommend the key items/findings of chest CT in COVID-19 pneumonia.
