Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning.
Gleason cancer grading
Gleason score
ISUP grade
convolutional neural network
deep learning
pathology
prostate
transfer learning
uropathology
whole-slide image
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
17 Apr 2023
17 Apr 2023
Historique:
received:
13
02
2023
revised:
07
04
2023
accepted:
12
04
2023
medline:
16
5
2023
pubmed:
16
5
2023
entrez:
16
5
2023
Statut:
epublish
Résumé
Histopathological classification in prostate cancer remains a challenge with high dependence on the expert practitioner. We develop a deep learning (DL) model to identify the most prominent Gleason pattern in a highly curated data cohort and validate it on an independent dataset. The histology images are partitioned in tiles (14,509) and are curated by an expert to identify individual glandular structures with assigned primary Gleason pattern grades. We use transfer learning and fine-tuning approaches to compare several deep neural network architectures that are trained on a corpus of camera images (ImageNet) and tuned with histology examples to be context appropriate for histopathological discrimination with small samples. In our study, the best DL network is able to discriminate cancer grade (GS3/4) from benign with an accuracy of 91%, F
Identifiants
pubmed: 37190264
pii: cancers15082335
doi: 10.3390/cancers15082335
pmc: PMC10136774
pii:
doi:
Types de publication
Journal Article
Langues
eng
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