The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies
Open accessAbstract
Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated cancers. Recently, machine learning algorithms have been developed for detecting PrCa in whole slide images (WSIs) with high test accuracy. However, the impact of these artificial intelligence systems on pathologic diagnosis is not known. To address this, we investigated how pathologists interact with Paige Prostate Alpha, a state-of-the-art PrCa detection system, in WSIs of prostate needle core biopsies stained with hematoxylin and eosin. Three AP-board certified pathologists assessed 304 anonymized prostate needle core biopsy WSIs in 8 hours. The pathologists classified each WSI as benign or cancerous. After ~4 weeks, pathologists were tasked with re-reviewing each WSI with the aid of Paige Prostate Alpha. For each WSI, Paige Prostate Alpha was used to perform cancer detection and, for WSIs where cancer was detected, the system marked the area where cancer was detected with the highest probability. The original diagnosis for each slide was rendered by genitourinary pathologists and incorporated any ancillary studies requested during the original diagnostic assessment. Against this ground truth, the pathologists and Paige Prostate Alpha were measured. Without Paige Prostate Alpha, pathologists had an average sensitivity of 74% and an average specificity of 97%. With Paige Prostate Alpha, the average sensitivity for pathologists significantly increased to 90% with no statistically significant change in specificity. With Paige Prostate Alpha, pathologists more often correctly classified smaller, lower grade tumors, and spent less time analyzing each WSI. Future studies will investigate if similar benefit is yielded when such a system is used to detect other forms of cancer in a setting that more closely emulates real practice.
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“Using multiple-instance learning14,49,55 with labels at the level of groups of slides has enabled clinically relevant diagnostics by scaling to training datasets on the order of 10,000 WSIs10–14.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [13]
In the citing paper
“In a retrospective study, pathologists demonstrated improved sensitivity in diagnosing prostate cancer when using the AI platform, along with a reduction in review time and no statistically significant change in specificity.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [60]
In the citing paper
“Raciti and colleagues (60) reported on Paige Prostate Alpha, a commercial decision support system trained to detect prostate cancer from core-needle biopsies.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [60]
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Cites
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
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Self-citationDeep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
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Trends in the US and Canadian Pathologist Workforces From 2007 to 2017
Metter, D. M., Colgan, T. J., Leung, S. T., Timmons, C. F., & Park, J. Y. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.4337 · Cited by 293
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McNemar χ2test revisited: comparing sensitivity and specificity of diagnostic examinations
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Defining clinically significant prostate cancer on the basis of pathological findings
Matoso, A., & Epstein, J. I. (2018) · Histopathology · doi:10.1111/his.13712 · Cited by 198
Amin, M. B., Lin, D. W., Gore, J. L., Srigley, J. R., Samaratunga, H., Egevad, L., Rubin, M. A., Nacey, J. N., Carter, H. B., Klotz, L. H., et al. (2014) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2014-0219-sa · Cited by 123
Cited by
Sorted by citations
The Lancet Commission on prostate cancer: planning for the surge in cases
James, N. D., Tannock, I. F., N'Dow, J. M., Feng, F. Y.-C., Gillessen, S., Ali, A., Trujillo, B., Al‐Lazikani, B., Attard, G., Bray, F., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)00651-2 · Cited by 826
“73,74 If this taskshifting is implemented, diagnosis could be largely nursedelivered or physician assistantdelivered with key elements (such as pathology sample processing) provided by technicians.”
Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J. (2023) · Cell · doi:10.1016/j.cell.2023.01.035 · Cited by 597
“,(41) that uses CNNs and RNNs to diagnose prostate cancer from biopsy slides.(109) Other prostate CLIA-certified pathology ML tests include DeepDx Prostate, which uses semantic segmentation CNNs, and Galen Prostate, which uses multiscale CNNs and gradient boosting classifiers for automated Gleason scoring.”
A foundation model for clinical-grade computational pathology and rare cancers detection
Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03141-0 · Cited by 572
Self-citationPantanowitz, L., Quiroga‐Garza, G. M., Bien, L., Heled, R., Laifenfeld, D., Linhart, C., Sandbank, J., Shach, A. A., Shalev, V., Vecsler, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30159-x · Cited by 371
“15 Raciti and colleagues 16 showed that in a nonclinical setting, AI can increase the detec tion of pro state cancer in whole slide images of CNBs.”
BackgroundDeep learning models in medical image analysis
Tsuneki, M. (2022) · Journal of Oral Biosciences · doi:10.1016/j.job.2022.03.003 · Cited by 292
Metadata
- Authors
- Raciti, P., Sue, J., Ceballos, R., Godrich, R., Kunz, J. D., Kapur, S., Reuter, V., Grady, L., Kanan, C., Klimstra, D. S., & Fuchs, T. J.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Raciti, Patricia
- ORCID
- Venue
- Modern Pathology
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 33
- Issue
- 10
- Pages
- 2058-2066
- ISSN
- 0893-3952 (ISSN-L)
- PMC
- PMC9235852
- OpenAlex
- W3020916919
- Cited by
- 186
- References
- 18
- Citations by year
- 2026: 14 · 2025: 30 · 2024: 39 · 2023: 39 · 2022: 35 · 2021: 22 · 2020: 6
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Modern Pathology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Funding for this study was provided by Paige.AI, Inc.
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- prostate cancer, whole slide images, core needle biopsy, artificial intelligence, computer-aided diagnosis, H&E staining, sensitivity and specificity, pathologist workload
- MeSH terms
- Deep Learning, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Male, Pathology, Clinical, Prostatic Neoplasms, Biopsy, Large-Core Needle
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