Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks

• URL: http://arxiv.org/abs/2004.01589v1
• Date: Fri, 3 Apr 2020 14:27:53 GMT
• Title: Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks
• Authors: Peter Str\"om (1), Kimmo Kartasalo (1,2), Pekka Ruusuvuori (2,3), Henrik Gr\"onberg (1,4), Hemamali Samaratunga (5), Brett Delahunt (6), Toyonori Tsuzuki (7), Lars Egevad (8), Martin Eklund (1) ((1) Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, (2) Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland, (3) Institute of Biomedicine, University of Turku, Turku, Finland, (4) Department of Oncology, St G\"oran Hospital, Stockholm, Sweden, (5) Aquesta Uropathology and University of Queensland, Brisbane, Qld, Australia, (6) Department of Pathology and Molecular Medicine, Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand, (7) Department of Surgical Pathology, School of Medicine, Aichi Medical University, Nagoya, Japan, (8) Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden)
• Abstract summary: Perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. We developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI is; however, labor intensive. In the study we aimed to develop an algorithm based on deep neural networks to aid pathologists in this task. Methods: We collected, digitized and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7,406 men who underwent biopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n=8,318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build deep neural networks, and the remaining 20% were used to evaluate the performance of the algorithm. Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1,652 PNI negative cores in the independent test set. For the pre-specified operating point this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. For localizing the regions of PNI within a slide we estimated an average intersection over union of 0.50 (CI: 0.46-0.55). Conclusion: We have developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties. These algorithms have the potential to aid pathologists in the day-to-day work by drastically reducing the number of biopsy cores that need to be assessed for PNI and by highlighting regions of diagnostic interest.

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