Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Author:

Gomes Egleidson F. A.1,Paulino Junior Eduardo2,de Lima Mário F. R.3,Reis Luana A.1,Paranhos Giovanna1,Mamede Marcelo4,Longford Francis G. J.5,Frey Jeremy G.5,de Paula Ana Maria1ORCID

Affiliation:

1. Departamento de Física, Instituto de Ciências Exatas Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

2. Departamento de Anatomia Patológica e Medicina Legal, Faculdade de Medicina Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

3. Laboratório Analys Patologia Belo Horizonte MG Brazil

4. Departamento Anatomia e Imagem, Faculdade de Medicina Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

5. University of Southampton Southampton UK

Abstract

AbstractProstate carcinoma, a slow‐growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non‐neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry

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