FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387207589> ?p ?o ?g. }

• W4387207589 endingPage "S84" @default.
• W4387207589 startingPage "S83" @default.
• W4387207589 abstract "For patients undergoing radical prostatectomy for prostate cancer (PCa), accurate risk stratification is essential to guide post-prostatectomy therapeutic decision making. Recently, there has been success in the use of multi-modal artificial intelligence models for men after prostate biopsy to aid in risk stratification. Herein, we trained and tested a TRansfer learning-based multi-modal Artificial InteLligence model (TRAIL) for biochemical recurrence (BCR) risk stratification following radical prostatectomy.Patients contained within a prospective PCa registry at a single institution were utilized. Digital pathology slides from the diagnostic biopsies prior to radical prostatectomy for patients with clinically localized PCa were scanned at 20x resolution. Features were extracted for the TRAIL model from pathology slides via two transfer learning steps: (1) InceptionResNetv2 that first determines a heatmap of tumor areas, and (2) A ResNet18 that extracts representative features from the high tumor probability areas. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection from the pathology-extracted features. Finally, TRAIL combines the clinical and pathology-extracted features via a classification ensemble model based on weak tree learners to predict 2- and 5-year BCR defined as two consecutive serum PSA levels ≥0.2 ng/mL. TRAIL training was performed on 250 patients and was then locked and applied to the test set of 125 patients. Accuracy and the area under the curve (AUC) were calculated. Comparison to CAPRA-S and to clinical-only features were assessed.A total of 818 digital whole pathology biopsy slides from 375 patients treated with subsequent radical prostatectomy were included. Surgical margins were positive in 29% of the patients, and 41% had extra-prostatic extension. The median follow-up was 48 months (Range: 1-132 months). The rates of 2-and 5-year BCR were 11% and 18% respectively. A total of 19 digital pathology-driven features were included in TRAIL. Clinical factors included age, ISUPG, Gleason score, PSA, pathological T and N stages, surgical margin involvement, and the presence of extra-prostatic extension. On the testing set, TRAIL achieved a 2-year BCR AUC of 0.76 and accuracy of 0.87, and was superior to CAPRA-S (AUC = 0.57) and clinical-only features (AUC 0.50, accuracy 0.14). For 5-year BCR, TRAIL achieved an AUC of 0.69 and accuracy of 0.78, and performed better than CAPRA-S (AUC = 0.58), and clinical only features (AUC = 0.50, accuracy = 0.23).Through a combination of deep and ensemble learning, TRAIL incorporates clinical and histopathology features, enabling an improved BCR risk stratification post-prostatectomy when compared to the currently used clinicopathologic models. Future work with larger datasets with metastatic events is warranted to further optimize the model for clinical use." @default.
• W4387207589 created "2023-09-30" @default.
• W4387207589 creator A5014871240 @default.
• W4387207589 creator A5040372362 @default.
• W4387207589 creator A5042458087 @default.
• W4387207589 creator A5053588971 @default.
• W4387207589 creator A5054274054 @default.
• W4387207589 creator A5056587248 @default.
• W4387207589 creator A5057500759 @default.
• W4387207589 creator A5060873650 @default.
• W4387207589 creator A5080126509 @default.
• W4387207589 creator A5082192767 @default.
• W4387207589 creator A5084645308 @default.
• W4387207589 creator A5087931346 @default.
• W4387207589 creator A5088175694 @default.
• W4387207589 creator A5088388062 @default.
• W4387207589 creator A5090167552 @default.
• W4387207589 date "2023-10-01" @default.
• W4387207589 modified "2023-10-05" @default.
• W4387207589 title "Post-Prostatectomy Risk Stratification of Biochemical Recurrence Using Transfer Learning-Based Multi-Modal Artificial Intelligence" @default.
• W4387207589 doi "https://doi.org/10.1016/j.ijrobp.2023.06.404" @default.
• W4387207589 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37784586" @default.
• W4387207589 hasPublicationYear "2023" @default.
• W4387207589 type Work @default.
• W4387207589 citedByCount "0" @default.
• W4387207589 crossrefType "journal-article" @default.
• W4387207589 hasAuthorship W4387207589A5014871240 @default.
• W4387207589 hasAuthorship W4387207589A5040372362 @default.
• W4387207589 hasAuthorship W4387207589A5042458087 @default.
• W4387207589 hasAuthorship W4387207589A5053588971 @default.
• W4387207589 hasAuthorship W4387207589A5054274054 @default.
• W4387207589 hasAuthorship W4387207589A5056587248 @default.
• W4387207589 hasAuthorship W4387207589A5057500759 @default.
• W4387207589 hasAuthorship W4387207589A5060873650 @default.
• W4387207589 hasAuthorship W4387207589A5080126509 @default.
• W4387207589 hasAuthorship W4387207589A5082192767 @default.
• W4387207589 hasAuthorship W4387207589A5084645308 @default.
• W4387207589 hasAuthorship W4387207589A5087931346 @default.
• W4387207589 hasAuthorship W4387207589A5088175694 @default.
• W4387207589 hasAuthorship W4387207589A5088388062 @default.
• W4387207589 hasAuthorship W4387207589A5090167552 @default.
• W4387207589 hasConcept C121608353 @default.
• W4387207589 hasConcept C126322002 @default.
• W4387207589 hasConcept C126838900 @default.
• W4387207589 hasConcept C126894567 @default.
• W4387207589 hasConcept C142724271 @default.
• W4387207589 hasConcept C154945302 @default.
• W4387207589 hasConcept C2775934546 @default.
• W4387207589 hasConcept C2777008409 @default.
• W4387207589 hasConcept C2777522853 @default.
• W4387207589 hasConcept C2779466945 @default.
• W4387207589 hasConcept C2780192828 @default.
• W4387207589 hasConcept C2781217009 @default.
• W4387207589 hasConcept C34626388 @default.
• W4387207589 hasConcept C41008148 @default.
• W4387207589 hasConcept C71924100 @default.
• W4387207589 hasConceptScore W4387207589C121608353 @default.
• W4387207589 hasConceptScore W4387207589C126322002 @default.
• W4387207589 hasConceptScore W4387207589C126838900 @default.
• W4387207589 hasConceptScore W4387207589C126894567 @default.
• W4387207589 hasConceptScore W4387207589C142724271 @default.
• W4387207589 hasConceptScore W4387207589C154945302 @default.
• W4387207589 hasConceptScore W4387207589C2775934546 @default.
• W4387207589 hasConceptScore W4387207589C2777008409 @default.
• W4387207589 hasConceptScore W4387207589C2777522853 @default.
• W4387207589 hasConceptScore W4387207589C2779466945 @default.
• W4387207589 hasConceptScore W4387207589C2780192828 @default.
• W4387207589 hasConceptScore W4387207589C2781217009 @default.
• W4387207589 hasConceptScore W4387207589C34626388 @default.
• W4387207589 hasConceptScore W4387207589C41008148 @default.
• W4387207589 hasConceptScore W4387207589C71924100 @default.
• W4387207589 hasIssue "2" @default.
• W4387207589 hasLocation W43872075891 @default.
• W4387207589 hasLocation W43872075892 @default.
• W4387207589 hasOpenAccess W4387207589 @default.
• W4387207589 hasPrimaryLocation W43872075891 @default.
• W4387207589 hasRelatedWork W1967410728 @default.
• W4387207589 hasRelatedWork W2025979791 @default.
• W4387207589 hasRelatedWork W2026113581 @default.
• W4387207589 hasRelatedWork W2102167265 @default.
• W4387207589 hasRelatedWork W2123702677 @default.
• W4387207589 hasRelatedWork W2133978084 @default.
• W4387207589 hasRelatedWork W2172257731 @default.
• W4387207589 hasRelatedWork W2942389067 @default.
• W4387207589 hasRelatedWork W3150909901 @default.
• W4387207589 hasRelatedWork W3162788459 @default.
• W4387207589 hasVolume "117" @default.
• W4387207589 isParatext "false" @default.
• W4387207589 isRetracted "false" @default.
• W4387207589 workType "article" @default.