Matches in SemOpenAlex for { <https://semopenalex.org/work/W967946355> ?p ?o ?g. }
Showing items 1 to 64 of
64
with 100 items per page.
- W967946355 endingPage "3736" @default.
- W967946355 startingPage "3736" @default.
- W967946355 abstract "Purpose: To develop a non-invasive and quantitative information platform to accurately identify the prostate cancer foci and tumor aggressiveness grade. Methods: 11 patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging (MRSI) prior to the surgery were studied. Multi-parametric imaging included extracting T2-map, Apparent Diffusion Coefficient (ADC) using diffusion weighted MRI, Ktrans using Dynamic Contrast Enhanced MRI, and 3D-MR Spectroscopy. Each image was composed of approximately 10 slices, which were divided into octants and individually assessed for the presence/absence of tumor by a radiologist. Following the radical prostatectomy, digital images of both the slice specimens and the histopathology slides were obtained. A pathologist reviewed all 223 octants and marked cancerous regions on each and graded them with Gleason score, which served as the ground truth to validate our prediction. Both binary prediction (indolent/aggressive cancer, separated by Gleason = 6) and multi-class prediction (no-cancer, non-aggressive cancer and aggressive cancer) were performed. Adaptive boosting with random under-sampling method was used. Area under the receiver operating characteristic (ROC) curve (AUC) and 95% confidence intervals were collected after repetitions of 10-fold cross-validation. Results: In binary prediction, average AUC value of was 0.77 [0.73, 0.79] with average sensitivity and specificity 79% [74%, 85%] and 74% [70%, 77%], respectively. For the multi-class classification, the average prediction accuracy for the no-cancer class was 97% [91%, 100%], for non-aggressive cancer class was 80% [76%, 83%] and for aggressive cancer class was 65% [59%, 72%]. The overall accuracy was 86% [83%, 88%]. Conclusion: We provided a sophisticated while user-friendly platform using multi-parametric MRI combined with supervised learning to be able to accurately detect cancer foci and its aggressiveness. In addition, our method was non-invasive and allowed for non-subjective disease characterization, which provided physician information to make personalized treatment decision." @default.
- W967946355 created "2016-06-24" @default.
- W967946355 creator A5014385668 @default.
- W967946355 creator A5014572211 @default.
- W967946355 creator A5051111179 @default.
- W967946355 creator A5056570748 @default.
- W967946355 creator A5060958778 @default.
- W967946355 creator A5083023771 @default.
- W967946355 date "2015-06-01" @default.
- W967946355 modified "2023-10-18" @default.
- W967946355 title "TH-CD-207-07: Prostate Cancer Foci Detection and Aggressiveness Identification Using Multi-Parametric MRI/MRS and Supervised Learning" @default.
- W967946355 doi "https://doi.org/10.1118/1.4926265" @default.
- W967946355 hasPublicationYear "2015" @default.
- W967946355 type Work @default.
- W967946355 sameAs 967946355 @default.
- W967946355 citedByCount "0" @default.
- W967946355 crossrefType "journal-article" @default.
- W967946355 hasAuthorship W967946355A5014385668 @default.
- W967946355 hasAuthorship W967946355A5014572211 @default.
- W967946355 hasAuthorship W967946355A5051111179 @default.
- W967946355 hasAuthorship W967946355A5056570748 @default.
- W967946355 hasAuthorship W967946355A5060958778 @default.
- W967946355 hasAuthorship W967946355A5083023771 @default.
- W967946355 hasConcept C121608353 @default.
- W967946355 hasConcept C126322002 @default.
- W967946355 hasConcept C126838900 @default.
- W967946355 hasConcept C143409427 @default.
- W967946355 hasConcept C2779466945 @default.
- W967946355 hasConcept C2780192828 @default.
- W967946355 hasConcept C2989005 @default.
- W967946355 hasConcept C58471807 @default.
- W967946355 hasConcept C70816921 @default.
- W967946355 hasConcept C71924100 @default.
- W967946355 hasConceptScore W967946355C121608353 @default.
- W967946355 hasConceptScore W967946355C126322002 @default.
- W967946355 hasConceptScore W967946355C126838900 @default.
- W967946355 hasConceptScore W967946355C143409427 @default.
- W967946355 hasConceptScore W967946355C2779466945 @default.
- W967946355 hasConceptScore W967946355C2780192828 @default.
- W967946355 hasConceptScore W967946355C2989005 @default.
- W967946355 hasConceptScore W967946355C58471807 @default.
- W967946355 hasConceptScore W967946355C70816921 @default.
- W967946355 hasConceptScore W967946355C71924100 @default.
- W967946355 hasIssue "6Part43" @default.
- W967946355 hasLocation W9679463551 @default.
- W967946355 hasOpenAccess W967946355 @default.
- W967946355 hasPrimaryLocation W9679463551 @default.
- W967946355 hasRelatedWork W2015694955 @default.
- W967946355 hasRelatedWork W2084573604 @default.
- W967946355 hasRelatedWork W2153516924 @default.
- W967946355 hasRelatedWork W2476462198 @default.
- W967946355 hasRelatedWork W3030395053 @default.
- W967946355 hasRelatedWork W3030685393 @default.
- W967946355 hasRelatedWork W3032026377 @default.
- W967946355 hasRelatedWork W3032062016 @default.
- W967946355 hasRelatedWork W3114143907 @default.
- W967946355 hasRelatedWork W4281618808 @default.
- W967946355 hasVolume "42" @default.
- W967946355 isParatext "false" @default.
- W967946355 isRetracted "false" @default.
- W967946355 magId "967946355" @default.
- W967946355 workType "article" @default.