Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients With Low-Grade Cancer.

PURPOSE: The aim of this study was to evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa). METHODS: This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest Prostate Imaging Reporting & Data System (PI-RADS®) score and pathologic diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa; Gleason score ≥ 7). The metrics AIR, CDR, and CDR adjusted for pathologic confirmation rate were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa. RESULTS: A total of 3,207 examinations were interpreted by 33 radiologists. Overall AIR, CDR, and CDR adjusted for pathologic confirmation rate at PI-RADS 3 to 5 (PI-RADS 4 and 5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3 to 5 (PI-RADS 4 and 5) were in the 36.8% to 75.6% (21.9%-57.5%) range and the 16.3%-28.7% (10.9%-26.5%) range, respectively. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line. CONCLUSIONS: The authors propose CDR and AIR as performance metrics in prostate MRI and report reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI Performed for Clinical Suspicion of Prostate Cancer.

PURPOSE: To report cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI performed for clinical suspicion of prostate cancer (PCa). MATERIALS AND METHODS: This retrospective single-institution, three-center study included patients who underwent MRI for clinical suspicion of PCa between 2017 and 2021. Patients with known PCa were excluded. Patient-level Prostate Imaging-Reporting and Data System (PI-RADS) score was extracted from the radiology report. AIR was defined as number of abnormal MRI (PI-RADS score 3-5) / total number of MRIs. CDR was defined as number of clinically significant PCa (csPCa: Gleason score ≥7) detected at abnormal MRI / total number of MRI. AIR, CDR, and CDR adjusted for pathology confirmation rate were calculated for each of three centers and pre-MRI biopsy status (biopsy-naive and previous negative biopsy). RESULTS: A total of 9,686 examinations (8,643 unique patients) were included. AIR, CDR, and CDR adjusted for pathology confirmation rate were 45.4%, 23.8%, and 27.6% for center I; 47.2%, 20.0%, and 22.8% for center II; and 42.3%, 27.2%, and 30.1% for center III, respectively. Pathology confirmation rate ranged from 81.6% to 88.0% across three centers. AIR and CDR for biopsy-naive patients were 45.5% to 52.6% and 24.2% to 33.5% across three centers, respectively, and those for previous negative biopsy were 27.2% to 39.8% and 11.7% to 14.2% across three centers, respectively. CONCLUSION: We reported CDR and AIR in prostate MRI for clinical suspicion of PCa. CDR needs to be adjusted for pathology confirmation rate and pre-MRI biopsy status for interfacility comparison.

A Bayesian Network-Based Browsing Model for Patients Seeking Radiology-Related Information on Hospital Websites: Development and Usability Study.

BACKGROUND: An increasing number of people are visiting hospital websites to seek better services and treatments compared to the past. It is therefore important for hospitals to develop websites to meet the needs of their patients. However, few studies have investigated whether and how the current hospital websites meet the patient's needs. Above all, in radiation departments, it may be difficult for patients to obtain the desired information regarding modality and diagnosis because such information is subdivided when described on a website. OBJECTIVE: The purpose of this study is to suggest a hospital website search behavior model by analyzing the browsing behavior model using a Bayesian network from the perspective of one-to-one marketing. METHODS: First, we followed the website access log of Hokkaido University Hospital, which was collected from September 1, 2016, to August 31, 2017, and analyzed the access log using Google Analytics. Second, we specified the access records related to radiology from visitor browsing pages and keywords. Third, using these resources, we structured 3 Bayesian network models based on specific patient needs: radiotherapy, nuclear medicine examination, and radiological diagnosis. Analyzing each model, this study considered why some visitors could not reach their desired page and improvements to meet the needs of visitors seeking radiology-related information. RESULTS: The radiotherapy model showed that 74% (67/90) of the target visitors could reach their requested page, but only 2% (2/90) could reach the Center page where inspection information, one of their requested pages, is posted. By analyzing the behavior of the visitors, we clarified that connecting with the radiotherapy and radiological diagnosis pages is useful for increasing the proportion of patients reaching their requested page. CONCLUSIONS: We proposed solutions for patient web-browsing accessibility based on a Bayesian network. Further analysis is necessary to verify the accuracy of the proposed model in comparison to other models. It is expected that information provided on hospital websites will be improved using this method.

Integrating Electronic Health Record Data into the ADEpedia-on-OHDSI Platform for Improved Signal Detection: A Case Study of Immune-related Adverse Events.

With widespread adoption of electronic health records (EHRs), Real World Data and Real World Evidence (RWE) have been increasingly used by FDA for evaluating drug safety and effectiveness. However, integration of heterogeneous drug safety data sources and systems remains an impediment for effective pharmacovigilance studies. In an ongoing project, we have developed a next generation pharmacovigilance signal detection framework known as ADEpedia-on-OHDSI using the OMOP common data model (CDM). The objective of the study is to demonstrate the feasibility of the framework for integrating both spontaneous reporting data and EHR data for improved signal detection with a case study of immune-related adverse events. We first loaded the OMOP CDM with both recent and legacy FAERS (FDA Adverse Event Reporting System) data (from the time period between Jan. 2004 and Dec. 2018). We also integrated the clinical data from the Mayo Clinic EHR system for six oncological immunotherapy drugs. We implemented a signal detection algorithm and compared the timelines of positive signals detected from both FAERS and EHR data. We found that the signals detected from EHRs are 4 months earlier than signals detected from FAERS database (depending on the signal detection methods used) for the ipilimumab-induced hypopituitarism. Our CDM-based approach would be useful to provide a scalable solution to integrate both drug safety data and EHR data to generate RWE for improved signal detection.

Coverage Evaluation of CTCAE for Capturing the Immune-related Adverse Events Leveraging Text Mining Technologies.

Background: Immunotherapy is seen as a promising new treatment for cancer but it may also cause immune-related adverse events (irAEs). Post-market surveillance of immunotherapy drugs highly depends on the ability to capture and standardize irAE data. The Common Terminology Criteria for Adverse Events (CTCAE) is a potential terminology that can be leveraged for irAEs standardization. However, the capability of the CTCAE in irAEs standardization needs to be evaluated. Methods: We investigated the irAEs of six FDA approved cancer immunotherapy monoclonal antibodies (mAbs) and evaluated the coverage of the CTCAE for capturing irAEs. We manually identified irAEs from drug labels of the 6 mAbs as the gold standard. We assessed the performance of two text mining pipelines using the dictionary lookup of the CTCAE terms and identified irAEs. In the coverage evaluation, the CTCAE was compared with MedDRA, a standard terminology for regulatory science, for irAE standardization. Results: We manually identified 510 unique irAEs from the drug labels. When using the CTCAE as a dictionary to run the text mining pipeline, the precision, recall and F-measure value was 100%, 10.78% and 19.47%. After adding manually identified irAE terms into the dictionary, the recall and F-measure value significantly improved, increased to 95.69% and 97.31%, respectively. In the coverage evaluation, compared with MedDRA, the coverage rate of the CTCAE is only 13.50% when taking all the mining results together into consideration. Conclusion: With some limitations in our study, we clearly demonstrated that the CTCAE needs an extension to meet the irAE standardization task.

Development of a Knowledge-based Application Utilizing Ontologies for the Continuing Site-specific JJ1017 Master Maintenance.

The purpose of this study was to develop the JJ1017 Knowledge-based Application (JKA) to support the continuing maintenance of a site-specific JJ1017 master defined by the JJ1017 guideline as a standard radiologic procedure master for medical information systems that are being adopted by some medical facilities in Japan. The method consisted of the following three steps: (1) construction of the JJ1017 Ontology (JJOnt) as a knowledge base using the Hozo (an environment for building/using ontologies); (2) development of modules (operation, I/O, graph modules) that are required to continue the maintenance of a site-specific JJ1017 master; and (3) unit testing of the JKA that consists of the JJOnt and the modules. As a result, the number of classes included in the JJOnt was 21,697. Within the radiologic procedure classes included in the above, the ratio of a JJ1017 master code for an external beam radiotherapy was the highest (51%). In unit testing of the JKA, we checked the main operations (e.g., keyword search of a JJ1017 master code/code meaning, editing the description of classes, etc.). The JJOnt is a knowledge base for implementing features that medical technologists find necessary in medical information systems. To enable medical technologists to exchange/retrieve semantically accurate information while using medical information systems in the future, we expect the JKA to support the maintenance and improvement of the site-specific JJ1017 master.

An ontology design for visualizing implicit knowledge of the radiotherapy risk.

Knowledge sharing of radiotherapy risk contributes to keep the safety practice between the oncologists, medical physicists, and therapists. But the mechanisms of risks tend to complex because of its diversities. Recently, an ontological analysis is tried to share knowledge in the various domains. Therefore, the aim of this study is to construct Radiotherapy Risk Ontology (RRO) and clarify the implicit structures of risks towards knowledge sharing. The method of this study is below. First, the classes and relations were extracted from the risk categorization of the publications. Second, these classes and relationships were connecting and mapping by the ontology editor. Third, the total relationships of classes were verified using paired comparison chart. This paper distinguishes between publication-oriented relationships and the other relationships. And these relationships was defined the explicit and implicit relationships. RRO was constructed 789 classes and 14 types of the explicit relationships. The 22 types of the implicit relationships were clarified using paired comparison. RRO provided the conceptual mapping and the implicit knowledge. The result of this study assisted the knowledge sharing.

Development of terminology for mammographic techniques for radiological technologists.

We are developing a mammographic ontology to share knowledge of the mammographic domain for radiologic technologists, with the aim of improving mammographic techniques. As a first step in constructing the ontology, we used mammography reference books to establish mammographic terminology for identifying currently available knowledge. This study proceeded in three steps: (1) determination of the domain and scope of the terminology, (2) lexical extraction, and (3) construction of hierarchical structures. We extracted terms mainly from three reference books and constructed the hierarchical structures manually. We compared features of the terms extracted from the three reference books. We constructed a terminology consisting of 440 subclasses grouped into 19 top-level classes: anatomic entity, image quality factor, findings, material, risk, breast, histological classification of breast tumors, role, foreign body, mammographic technique, physics, purpose of mammography examination, explanation of mammography examination, image development, abbreviation, quality control, equipment, interpretation, and evaluation of clinical imaging. The number of terms that occurred in the subclasses varied depending on which reference book was used. We developed a terminology of mammographic techniques for radiologic technologists consisting of 440 terms.