Detection of perineural invasion in prostate needle biopsies with deep neural networks

Kimmo Kartasalo, Peter Ström, Pekka Ruusuvuori, Hemamali Samaratunga, Brett Delahunt, Toyonori Tsuzuki, Martin Eklund, Lars Egevad

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    Abstract

    The presence of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI are, however, labor intensive. To aid pathologists in this task, we developed an artificial intelligence (AI) algorithm based on deep neural networks. We collected, digitized, and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7406 men who underwent biopsy in a screening trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n = 8318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build the AI algorithm, while 20% were used to evaluate its performance. For detecting PNI in prostate biopsy cores, the AI 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 1652 PNI negative cores in the independent test set. For a 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. The concordance of the AI with pathologists, measured by mean pairwise Cohen’s kappa (0.74), was comparable to inter-pathologist concordance (0.68 to 0.75). The proposed algorithm detects PNI in prostate biopsies with acceptable performance. This could aid pathologists by reducing the number of biopsies that need to be assessed for PNI and by highlighting regions of diagnostic interest.

    Original languageEnglish
    Number of pages10
    JournalVIRCHOWS ARCHIV
    Volume481
    Issue number1
    DOIs
    Publication statusPublished - 2022
    Publication typeA1 Journal article-refereed

    Keywords

    • Artificial intelligence
    • Pathology
    • Perineural invasion
    • Prostate cancer

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Pathology and Forensic Medicine
    • Molecular Biology
    • Cell Biology

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