  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.
