FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2914485697> ?p ?o ?g. }

• W2914485697 endingPage "B094" @default.
• W2914485697 startingPage "B094" @default.
• W2914485697 abstract "Abstract Introduction and Objective: Prostate cancer (PC) with de novo bone metastases at diagnosis (M1) carries a 5-year survival rate of 28% and requires early, aggressive treatment. Clinical assays and the pathology of prostate needle biopsies (PNBX) cannot distinguish primary M1 tumors from high-grade localized (M0) cases. We hypothesized that digital image analysis can be applied to obtain morphologic biomarkers, not recognizable by pathologists. Here we demonstrate how novel software tools that involve deep learning frameworks can be used to systematically extract handcrafted and autoencoder features and to build models to predict M1 stage at the time of diagnosis. Methods: A study cohort, nested within a biorepository of 2150 PC patients at the Greater LA VA, consisted of 86 high-grade M0 and 85 M1 cases. Slides were digitized at 40X and 2 pathologists annotated all cancer foci. Approximately 30 image tiles were selected from each case. 62 handcrafted (HC) and 64 autoencoder (AE) features were extracted from nuclei. Feature values normalized. The normalized profile of each primary feature gave rise to 11 secondary features, representing the distribution of the feature within a case. We separated cases into training + testing versus validation groups at an 80:20 ratio. Using a bootstrapping method, we selected the best GLMNET models predicting M0 versus M1 status in the training + testing set and applied them to an independent validation set of cases. Results: After successful conversion of M0 and M1 image tiles to digital nuclear masks and color normalization, ~ 400,000 nuclei were isolated using parameters that enriched for nuclei from cancer cells. A denoising autoencoding neural network was used to generate AE biomarkers for each nucleus. HC features quantified nuclear shape, size, color, and texture. A systematic pipeline of preprocessing, normalization, and conversion to case-level secondary features was applied to AE and HC features. For both feature types, the average of 50,000 bootstrapping models resulted in an AUC of 0.8 for the training and an average accuracy of 0.7 for the test cohort. The best 38 AE models or 16 HC models were applied to the independent validation cohort of 24 cases and assigned each case to the M0 versus M1 groups by majority voting. At a threshold of 0.5, this resulted in an accuracy of 70% for M0 versus M1 distinction. Conclusion: We applied digital imaging technology and machine learning software to AE and HC features in order to predict M0 versus M1 stage from the tumor in PNBXs at diagnosis. Unexpectedly, hidden features in nuclei differed between M0 and M1 cases and succeeded in predicting metastatic disease with 70% accuracy. The ultimate goal is to combine the inexpensive risk prediction from quantitative imaging with clinical parameters and RNA sequencing data to develop accurate prediction models of occult metastases and risk of future metastatic progression at the time of diagnosis in all patients with high-grade PC. Funding: DOD PC131996, PCF-Movember GAP1 Unique TMAs Project, Prostate Cancer Foundation (PCF) Creativity Award, Jean Perkins Foundation, NIH/NCI P01 CA098912-09, NIH R01CA131255 and P50CA092131, Stephen Spielberg Team Science Award. Citation Format: Fangjin Huang, Nathan Ing, Miller Eric, Hootan Salemi, Michael Lewis, Isla Garraway, Arkadiusz Gertych, Beatrice Knudsen. Quantitative digital image analysis and machine learning for staging of prostate cancer at diagnosis [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B094." @default.
• W2914485697 created "2019-02-21" @default.
• W2914485697 creator A5007730632 @default.
• W2914485697 creator A5012620880 @default.
• W2914485697 creator A5029397257 @default.
• W2914485697 creator A5037257097 @default.
• W2914485697 creator A5060130342 @default.
• W2914485697 creator A5064974889 @default.
• W2914485697 creator A5078534826 @default.
• W2914485697 creator A5088631005 @default.
• W2914485697 date "2018-08-14" @default.
• W2914485697 modified "2023-09-27" @default.
• W2914485697 title "Abstract B094: Quantitative digital image analysis and machine learning for staging of prostate cancer at diagnosis" @default.
• W2914485697 doi "https://doi.org/10.1158/1538-7445.prca2017-b094" @default.
• W2914485697 hasPublicationYear "2018" @default.
• W2914485697 type Work @default.
• W2914485697 sameAs 2914485697 @default.
• W2914485697 citedByCount "4" @default.
• W2914485697 countsByYear W29144856972019 @default.
• W2914485697 countsByYear W29144856972020 @default.
• W2914485697 countsByYear W29144856972021 @default.
• W2914485697 countsByYear W29144856972022 @default.
• W2914485697 crossrefType "journal-article" @default.
• W2914485697 hasAuthorship W2914485697A5007730632 @default.
• W2914485697 hasAuthorship W2914485697A5012620880 @default.
• W2914485697 hasAuthorship W2914485697A5029397257 @default.
• W2914485697 hasAuthorship W2914485697A5037257097 @default.
• W2914485697 hasAuthorship W2914485697A5060130342 @default.
• W2914485697 hasAuthorship W2914485697A5064974889 @default.
• W2914485697 hasAuthorship W2914485697A5078534826 @default.
• W2914485697 hasAuthorship W2914485697A5088631005 @default.
• W2914485697 hasConcept C101738243 @default.
• W2914485697 hasConcept C108583219 @default.
• W2914485697 hasConcept C121608353 @default.
• W2914485697 hasConcept C126322002 @default.
• W2914485697 hasConcept C126838900 @default.
• W2914485697 hasConcept C136886441 @default.
• W2914485697 hasConcept C138885662 @default.
• W2914485697 hasConcept C142724271 @default.
• W2914485697 hasConcept C144024400 @default.
• W2914485697 hasConcept C153180895 @default.
• W2914485697 hasConcept C154945302 @default.
• W2914485697 hasConcept C19165224 @default.
• W2914485697 hasConcept C2776235491 @default.
• W2914485697 hasConcept C2776401178 @default.
• W2914485697 hasConcept C2777522853 @default.
• W2914485697 hasConcept C2780192828 @default.
• W2914485697 hasConcept C2989005 @default.
• W2914485697 hasConcept C41008148 @default.
• W2914485697 hasConcept C41895202 @default.
• W2914485697 hasConcept C71924100 @default.
• W2914485697 hasConceptScore W2914485697C101738243 @default.
• W2914485697 hasConceptScore W2914485697C108583219 @default.
• W2914485697 hasConceptScore W2914485697C121608353 @default.
• W2914485697 hasConceptScore W2914485697C126322002 @default.
• W2914485697 hasConceptScore W2914485697C126838900 @default.
• W2914485697 hasConceptScore W2914485697C136886441 @default.
• W2914485697 hasConceptScore W2914485697C138885662 @default.
• W2914485697 hasConceptScore W2914485697C142724271 @default.
• W2914485697 hasConceptScore W2914485697C144024400 @default.
• W2914485697 hasConceptScore W2914485697C153180895 @default.
• W2914485697 hasConceptScore W2914485697C154945302 @default.
• W2914485697 hasConceptScore W2914485697C19165224 @default.
• W2914485697 hasConceptScore W2914485697C2776235491 @default.
• W2914485697 hasConceptScore W2914485697C2776401178 @default.
• W2914485697 hasConceptScore W2914485697C2777522853 @default.
• W2914485697 hasConceptScore W2914485697C2780192828 @default.
• W2914485697 hasConceptScore W2914485697C2989005 @default.
• W2914485697 hasConceptScore W2914485697C41008148 @default.
• W2914485697 hasConceptScore W2914485697C41895202 @default.
• W2914485697 hasConceptScore W2914485697C71924100 @default.
• W2914485697 hasIssue "16_Supplement" @default.
• W2914485697 hasLocation W29144856971 @default.
• W2914485697 hasOpenAccess W2914485697 @default.
• W2914485697 hasPrimaryLocation W29144856971 @default.
• W2914485697 hasRelatedWork W2669956259 @default.
• W2914485697 hasRelatedWork W2776466379 @default.
• W2914485697 hasRelatedWork W2785535669 @default.
• W2914485697 hasRelatedWork W2897995864 @default.
• W2914485697 hasRelatedWork W2939353110 @default.
• W2914485697 hasRelatedWork W2998168123 @default.
• W2914485697 hasRelatedWork W3165463024 @default.
• W2914485697 hasRelatedWork W4220775285 @default.
• W2914485697 hasRelatedWork W4287995534 @default.
• W2914485697 hasRelatedWork W4327774331 @default.
• W2914485697 hasVolume "78" @default.
• W2914485697 isParatext "false" @default.
• W2914485697 isRetracted "false" @default.
• W2914485697 magId "2914485697" @default.
• W2914485697 workType "article" @default.