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Oner MU, Ng MY, Giron DM, Chen Xi CE, Yuan Xiang LA, Singh M, Yu W, Sung WK, Wong CF, Lee HK. An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases. Patterns (N Y) 2022; 3:100642. [PMID: 36569545 PMCID: PMC9768677 DOI: 10.1016/j.patter.2022.100642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/30/2022] [Accepted: 11/01/2022] [Indexed: 12/03/2022]
Abstract
Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985-0.997) in the external validation study, showing the generalizability of our multi-resolution approach.
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Affiliation(s)
- Mustafa Umit Oner
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore,School of Computing, National University of Singapore, Singapore 117417, Singapore,Department of Artificial Intelligence Engineering, Bahcesehir University, Istanbul 34353, Turkey,Corresponding author
| | - Mei Ying Ng
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore
| | - Danilo Medina Giron
- Department of Pathology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Cecilia Ee Chen Xi
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore
| | - Louis Ang Yuan Xiang
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore
| | - Malay Singh
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore
| | - Weimiao Yu
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A∗STAR), Singapore 138673, Singapore
| | - Wing-Kin Sung
- School of Computing, National University of Singapore, Singapore 117417, Singapore,Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore
| | - Chin Fong Wong
- Department of Pathology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore,School of Computing, National University of Singapore, Singapore 117417, Singapore,Singapore Eye Research Institute (SERI), Singapore 169856, Singapore,Image and Pervasive Access Lab (IPAL), Singapore 138632, Singapore,Rehabilitation Research Institute of Singapore, Singapore 308232, Singapore,Singapore Institute for Clinical Sciences, Singapore 117609, Singapore
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