Pathologist interobserver discordance is significant in grading of prostate cancer, limiting reliability. Diagnostic reproducibility may be improved with digital images, but adoption faces workflow, cost, and quality challenges. A novel digital method using an alternative tissue processing approach and novel laser microscopy system potentially addresses these issues.
To evaluate the capability of this new method for primary diagnostic interpretation in clinical prostate biopsy specimens.
Forty patients with a high likelihood of prostate cancer based on magnetic resonance imaging consented to investigational core biopsy. A subset of samples was used for direct comparison of physical slide preparation effects and time-tracking determination with multiphoton microscopy. Twenty samples were processed for diagnostic comparison between multilevel digital slides and subsequently produced physical slides. A reference diagnosis based on all data was established using grade groups. Level of diagnostic match and requests for immunohistochemistry were compared between physical and digital diagnoses. Immunohistochemical staining and length measurements were secondary outcomes.
Interpretations based on direct multiphoton imaging yielded diagnoses that were at least as accurate as standard histology; cancer diagnosis correlation was 89% (51 of 57) by physical slides and 95% (53 of 56) by multiphoton microscopy. Grade-level concordance was 73% (44 of 60) by either method. Immunohistochemistry for routine prostate cancer–associated markers on these alternatively processed tissues was unaffected. Alternatively processed tissues resulted in longer measured core and cancer lengths, suggestive of improved orientation and visualization.
Findings support high potential for complete interpretation of prostate core biopsies using solely multiphoton microscopy of intact specimens, with potential diagnostic benefits as well as reduced processing time and reduced processing complexity.
Torres, Olson, and Levene declare an ownership interest in Applikate Technologies, LLC, a startup company developing systems for digital histology. The other authors have no relevant financial interest in the products or companies described in this article.
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Torres and Levene recognize support from R44 CA189522-01 (NIH/NCI). Torres recognizes support from 3UL1TR001863-02W1 (NIH/NCATS).