FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288085286> ?p ?o ?g. }

• W4288085286 endingPage "1147" @default.
• W4288085286 startingPage "1141" @default.
• W4288085286 abstract "Rationale and Objectives Adoption of the Prostate Imaging Reporting & Data System (PI-RADS) has been shown to increase detection of clinically significant prostate cancer on prostate mpMRI. We propose that a rule-based algorithm based on Regular Expression (RegEx) matching can be used to automatically categorize prostate mpMRI reports into categories as a means by which to assess for opportunities for quality improvement. Materials and Methods All prostate mpMRIs performed in the Duke University Health System from January 2, 2015, to January 29, 2021, were analyzed. Exclusion criteria were applied, for a total of 5343 male patients and 6264 prostate mpMRI reports. These reports were then analyzed by our RegEx algorithm to be categorized as PI-RADS 1 through PI-RADS 5, Recurrent Disease, or “No Information Available.” A stratified, random sample of 502 mpMRI reports was reviewed by a blinded clinical team to assess performance of the RegEx algorithm. Results Compared to manual review, the RegEx algorithm achieved overall accuracy of 92.6%, average precision of 88.8%, average recall of 85.6%, and F1 score of 0.871. The clinical team also reviewed 344 cases that were classified as “No Information Available,” and found that in 150 instances, no numerical PI-RADS score for any lesion was included in the impression section of the mpMRI report. Conclusion Rule-based processing is an accurate method for the large-scale, automated extraction of PI-RADS scores from the text of radiology reports. These natural language processing approaches can be used for future initiatives in quality improvement in prostate mpMRI reporting with PI-RADS. Adoption of the Prostate Imaging Reporting & Data System (PI-RADS) has been shown to increase detection of clinically significant prostate cancer on prostate mpMRI. We propose that a rule-based algorithm based on Regular Expression (RegEx) matching can be used to automatically categorize prostate mpMRI reports into categories as a means by which to assess for opportunities for quality improvement. All prostate mpMRIs performed in the Duke University Health System from January 2, 2015, to January 29, 2021, were analyzed. Exclusion criteria were applied, for a total of 5343 male patients and 6264 prostate mpMRI reports. These reports were then analyzed by our RegEx algorithm to be categorized as PI-RADS 1 through PI-RADS 5, Recurrent Disease, or “No Information Available.” A stratified, random sample of 502 mpMRI reports was reviewed by a blinded clinical team to assess performance of the RegEx algorithm. Compared to manual review, the RegEx algorithm achieved overall accuracy of 92.6%, average precision of 88.8%, average recall of 85.6%, and F1 score of 0.871. The clinical team also reviewed 344 cases that were classified as “No Information Available,” and found that in 150 instances, no numerical PI-RADS score for any lesion was included in the impression section of the mpMRI report. Rule-based processing is an accurate method for the large-scale, automated extraction of PI-RADS scores from the text of radiology reports. These natural language processing approaches can be used for future initiatives in quality improvement in prostate mpMRI reporting with PI-RADS." @default.
• W4288085286 created "2022-07-28" @default.
• W4288085286 creator A5030603521 @default.
• W4288085286 creator A5040192736 @default.
• W4288085286 creator A5050275221 @default.
• W4288085286 creator A5061721402 @default.
• W4288085286 creator A5068794484 @default.
• W4288085286 creator A5081806047 @default.
• W4288085286 date "2023-06-01" @default.
• W4288085286 modified "2023-10-03" @default.
• W4288085286 title "Utility of a Rule-Based Algorithm in the Assessment of Standardized Reporting in PI-RADS" @default.
• W4288085286 cites W1775135849 @default.
• W4288085286 cites W1827911007 @default.
• W4288085286 cites W2102519580 @default.
• W4288085286 cites W2117172301 @default.
• W4288085286 cites W2137961864 @default.
• W4288085286 cites W2341466379 @default.
• W4288085286 cites W2477375111 @default.
• W4288085286 cites W2552766130 @default.
• W4288085286 cites W2586386444 @default.
• W4288085286 cites W2616913649 @default.
• W4288085286 cites W2765735211 @default.
• W4288085286 cites W2767349510 @default.
• W4288085286 cites W2775769660 @default.
• W4288085286 cites W2791258488 @default.
• W4288085286 cites W2893698442 @default.
• W4288085286 cites W2906484429 @default.
• W4288085286 cites W2922071185 @default.
• W4288085286 cites W3009832770 @default.
• W4288085286 cites W3011342404 @default.
• W4288085286 cites W3093251512 @default.
• W4288085286 cites W3119005666 @default.
• W4288085286 cites W3154299409 @default.
• W4288085286 cites W3172431965 @default.
• W4288085286 cites W3198293575 @default.
• W4288085286 cites W3203730897 @default.
• W4288085286 cites W3210661083 @default.
• W4288085286 cites W3217294131 @default.
• W4288085286 cites W4220866585 @default.
• W4288085286 cites W4223895485 @default.
• W4288085286 cites W4280520911 @default.
• W4288085286 doi "https://doi.org/10.1016/j.acra.2022.06.024" @default.
• W4288085286 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35909050" @default.
• W4288085286 hasPublicationYear "2023" @default.
• W4288085286 type Work @default.
• W4288085286 citedByCount "2" @default.
• W4288085286 countsByYear W42880852862023 @default.
• W4288085286 crossrefType "journal-article" @default.
• W4288085286 hasAuthorship W4288085286A5030603521 @default.
• W4288085286 hasAuthorship W4288085286A5040192736 @default.
• W4288085286 hasAuthorship W4288085286A5050275221 @default.
• W4288085286 hasAuthorship W4288085286A5061721402 @default.
• W4288085286 hasAuthorship W4288085286A5068794484 @default.
• W4288085286 hasAuthorship W4288085286A5081806047 @default.
• W4288085286 hasConcept C11413529 @default.
• W4288085286 hasConcept C121608353 @default.
• W4288085286 hasConcept C126322002 @default.
• W4288085286 hasConcept C126838900 @default.
• W4288085286 hasConcept C154945302 @default.
• W4288085286 hasConcept C2776235491 @default.
• W4288085286 hasConcept C2780192828 @default.
• W4288085286 hasConcept C41008148 @default.
• W4288085286 hasConcept C71924100 @default.
• W4288085286 hasConceptScore W4288085286C11413529 @default.
• W4288085286 hasConceptScore W4288085286C121608353 @default.
• W4288085286 hasConceptScore W4288085286C126322002 @default.
• W4288085286 hasConceptScore W4288085286C126838900 @default.
• W4288085286 hasConceptScore W4288085286C154945302 @default.
• W4288085286 hasConceptScore W4288085286C2776235491 @default.
• W4288085286 hasConceptScore W4288085286C2780192828 @default.
• W4288085286 hasConceptScore W4288085286C41008148 @default.
• W4288085286 hasConceptScore W4288085286C71924100 @default.
• W4288085286 hasIssue "6" @default.
• W4288085286 hasLocation W42880852861 @default.
• W4288085286 hasLocation W42880852862 @default.
• W4288085286 hasOpenAccess W4288085286 @default.
• W4288085286 hasPrimaryLocation W42880852861 @default.
• W4288085286 hasRelatedWork W131936546 @default.
• W4288085286 hasRelatedWork W1497253641 @default.
• W4288085286 hasRelatedWork W2075763133 @default.
• W4288085286 hasRelatedWork W2088520467 @default.
• W4288085286 hasRelatedWork W2376423713 @default.
• W4288085286 hasRelatedWork W2386364393 @default.
• W4288085286 hasRelatedWork W2748952813 @default.
• W4288085286 hasRelatedWork W2899084033 @default.
• W4288085286 hasRelatedWork W3015759778 @default.
• W4288085286 hasRelatedWork W3032731309 @default.
• W4288085286 hasVolume "30" @default.
• W4288085286 isParatext "false" @default.
• W4288085286 isRetracted "false" @default.
• W4288085286 workType "article" @default.