[Histomolecular classification of urothelial carcinoma of the urinary bladder : From histological phenotype to genotype and back]. / Histomolekulare Klassifikation des Urothelkarzinoms der Harnblase : Vom histologischen Phänotyp zum Genotyp und zurück.
Pathologie (Heidelb)
; 45(2): 106-114, 2024 Mar.
Article
en De
| MEDLINE
| ID: mdl-38285173
ABSTRACT
BACKGROUND:
Of all urothelial carcinomas (UCs), 25% are muscle invasive and associated with a 5-year overall survival rate of 50%. Findings regarding the molecular classification of muscle-invasive urothelial carcinomas (MIUCs) have not yet found their way into clinical practice.OBJECTIVES:
Prediction of molecular consensus subtypes in MIUCs with artificial intelligence (AI) based on histologic hematoxylin-eosin (HE) sections.METHODS:
Pathologic review and annotation of The Cancer Genome Atlas (TCGA) Bladder Cancer (BLCA) Cohort (Nâ¯= 412) and the Dr. Senckenberg Institute of Pathology (SIP) BLCA Cohort (Nâ¯= 181). An AI model for the prediction of molecular subtypes based on annotated histomorphology was trained.RESULTS:
For a five-fold cross-validation with TCGA cases (Nâ¯= 274), an internal TCGA test set (Nâ¯= 18) and an external SIP test set (Nâ¯= 27), we reached mean area under the receiver operating characteristic curve (AUROC) scores of 0.73, 0.8 and 0.75 for the classification of the used molecular subtypes "luminal", "basal/squamous" and "stroma-rich". By training on correlations to individual molecular subtypes, rather than training on one subtype assignment per case, the AI prediction of subtypes could be significantly improved.DISCUSSION:
Follow-up studies with RNA extraction from various areas of AI-predicted molecular heterogeneity may improve molecular classifications and thereby AI algorithms trained on these classifications.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Vejiga Urinaria
/
Carcinoma de Células Transicionales
Tipo de estudio:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Idioma:
De
Revista:
Pathologie (Heidelb)
Año:
2024
Tipo del documento:
Article