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

Prostate 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. H...

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Bibliographic Details
Published in:Journal of biophotonics 2023-06, Vol.16 (6), p.e202200382-n/a
Main Authors: Gomes, Egleidson F. A., Paulino Junior, Eduardo, Lima, Mário F. R., Reis, Luana A., Paranhos, Giovanna, Mamede, Marcelo, Longford, Francis G. J., Frey, Jeremy G., Paula, Ana Maria
Format: Article
Language:English
Subjects:
Biopsy
Carcinoma - pathology
Diagnosis
Discriminant analysis
Humans
Image analysis
Image processing
Machine Learning
Male
Medical imaging
multiphoton imaging
Parameters
Prostate - diagnostic imaging
Prostate - pathology
Prostate cancer
Prostate carcinoma
Prostatic Neoplasms - diagnostic imaging
Prostatic Neoplasms - pathology
reactive stroma
Stroma
Tumors
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Summary:Prostate 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. Multiphoton imaging of prostate tumour samples allows us to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis recognises and quantifies the stromal fibre and neoplastic cell regions in the images and provides a set of metrics to distinguish between non‐neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with good accuracy. The results show the importance of the stromal parameters as additional criteria for a more accurate diagnosis.
ISSN:1864-063X
1864-0648
DOI:10.1002/jbio.202200382