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Background: The detection of perineural invasion (PNI) by carcinoma inprostate biopsies has been shown to be associated with poor prognosis. Theassessment and quantification of PNI is; however, labor intensive. In the studywe aimed to develop an algorithm based on deep neural networks to aidpathologists in this task.  Methods: We collected, digitized and pixel-wise annotated the PNI findings ineach of the approximately 80,000 biopsy cores from the 7,406 men who underwentbiopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. Intotal, 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 of80% 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 anestimated 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 inthe independent test set. For the pre-specified operating point this translatesto sensitivity of 0.87 and specificity of 0.97. The corresponding positive andnegative predictive values were 0.67 and 0.99, respectively. For localizing theregions of PNI within a slide we estimated an average intersection over unionof 0.50 (CI: 0.46-0.55).  Conclusion: We have developed an algorithm based on deep neural networks fordetecting PNI in prostate biopsies with apparently acceptable diagnosticproperties. These algorithms have the potential to aid pathologists in theday-to-day work by drastically reducing the number of biopsy cores that need tobe assessed for PNI and by highlighting regions of diagnostic interest.

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