Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.
Sci Rep
; 10(1): 9911, 2020 06 18.
Article
em En
| MEDLINE
| ID: mdl-32555410
ABSTRACT
Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Prostatectomia
/
Neoplasias da Próstata
/
Algoritmos
/
Diagnóstico por Computador
/
Aprendizado de Máquina
/
Recidiva Local de Neoplasia
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
/
Male
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Canadá