ArticleVirchows Archiv : an international journal of pathology2022
Detection of perineural invasion in prostate needle biopsies with deep neural networks.
Article in Virchows Archiv : an international journal of pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Semi-supervised learning for automated perineural invasion detection in multi-organ H&E whole slide images.Bioinformatics (Oxford, England) · 2026Article
- An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.Virchows Archiv : an international journal of pathology · 2026Article
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence.Frontiers in oncology · 2026Review
- The state of the art in artificial intelligence and digital pathology in prostate cancer.Nature reviews. Urology · 2026Review
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Article
- The Role of Artificial Intelligence in the Evaluation of Prostate Pathology.Pathology international · 2025Review
- Unveiling a novel cancer hallmark by evaluation of neural infiltration in cancer.Briefings in bioinformatics · 2025Article
- The Use of Artificial Intelligence in Urologic Oncology: Current Insights and Challenges.Research and reports in urology · 2025Review
- Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.Cancer discovery · 2024Review
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Clinical Evaluation of the Pancreatic Cancer Microenvironment: Opportunities and Challenges.Cancers · 2024Review
- Hallmarks of perineural invasion in pancreatic ductal adenocarcinoma: new biological dimensions.Frontiers in oncology · 2024Review
- Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence.Scientific reports · 2023Article
- Increased Nerve Density Adversely Affects Outcome in Oral Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Detection of Muscularis propria Invasion in Urothelial Carcinoma Using Artificial Intelligence.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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