Clinical Validation of Artificial Intelligence–Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection
Open accessAbstract
Context.—: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. Objective.—: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. Design.—: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning–based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists’ changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. Results.—: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. Conclusions.—: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.
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Using multiple-instance learning 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 WSIs.
“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 [10]
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Cites
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Self-citationDeep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069 · see citing works
Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney, D. M., Bostwick, D. G., Evans, A. J., Grignon, D. J., Humphrey, P. A., et al. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30738-7 · Cited by 620 · see citing works
Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., & Stumpe, M. C. (2018) · American Journal of Surgical Pathology · doi:10.1097/pas.0000000000001151 · Cited by 489 · see citing works
Pantanowitz, 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 361 · see citing works
Computational pathology: Challenges and promises for tissue analysis
Fuchs, T. J., & Buhmann, J. M. (2011) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.02.006 · Cited by 267 · see citing works
Self-citationRaciti, P., Sue, J., Ceballos, R., Godrich, R., Kunz, J. D., Kapur, S., Reuter, V., Grady, L., Kanan, C., Klimstra, D. S., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0551-y · Cited by 186 · see citing works
Self-citationArtificial intelligence—the third revolution in pathology
Salto‐Tellez, M., Maxwell, P., & Hamilton, P. (2018) · Histopathology · doi:10.1111/his.13760 · Cited by 176 · see citing works
Independent real‐world application of a clinical‐grade automated prostate cancer detection system
da Silva, L. M., Pereira, E. M., Salles, P. G., Godrich, R., Ceballos, R., Kunz, J. D., Casson, A., Viret, J., Chandarlapaty, S., Ferreira, C. G., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5662 · Cited by 175 · see citing works
Self-citationEvans, A. J., Bauer, T. W., Bui, M. M., Cornish, T. C., Duncan, H., Glassy, E. F., Hipp, J., McGee, R. S., Murphy, D., Myers, C., et al. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2017-0496-cp · Cited by 168 · see citing works
Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network
Retámero, J. A., Aneiros‐Fernández, J., & del Moral, R. G. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0541-oa · Cited by 161 · see citing works
Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics
Baidoshvili, A., Bucur, A., van Leeuwen, J., van der Laak, J. A. W. M., Kluin, P. M., & van Diest, P. J. (2018) · Histopathology · doi:10.1111/his.13691 · Cited by 129 · see citing works
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 · see citing works
Parwani, A. V. (2019) · Diagnostic Pathology · doi:10.1186/s13000-019-0921-2 · Cited by 102 · see citing works
The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice
Jackson, B. R., Ye, Y., Crawford, J. M., Becich, M. J., Roy, S., Botkin, J. R., de Baca, M. E., & Pantanowitz, L. (2021) · Academic Pathology · doi:10.1177/2374289521990784 · Cited by 83 · see citing works
Schüffler, P. J., Geneslaw, L., Yarlagadda, D. V. K., Hanna, M. G., Samboy, J., Stamelos, E., Vanderbilt, C., Philip, J., Jean, M.-H., Corsale, L., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab085 · Cited by 83 · see citing works
Self-citationTerabyte-scale Deep Multiple Instance Learning for Classification and Localization in PathologyPreprint
Campanella, G. S. V., Silva, V. W. K., & Fuchs, T. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06983 · Cited by 79 · see citing works
Self-citationMultireader multicase variance analysis for binary data
Gallas, B. D., Pennello, G. A., & Myers, K. J. (2007) · Journal of the Optical Society of America A · doi:10.1364/josaa.24.000b70 · Cited by 55 · see citing works
Validation of a whole‐slide image‐based teleconsultation network
Baidoshvili, A., Stathonikos, N., Freling, G., Bart, J., Hart, N. A. ‘., van der Laak, J. A. W. M., Doff, J. J., van der Vegt, B., Kluin, P. M., & van Diest, P. J. (2018) · Histopathology · doi:10.1111/his.13673 · Cited by 27 · see citing works
Contemporary prostate biopsy reporting: insights from a survey of clinicians’ use of pathology data
Varma, M., Narahari, K., Mason, M. D., Oxley, J., & Berney, D. M. (2018) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2018-205093 · Cited by 26 · see citing works
Microscope? No, Thanks: User Experience With Complete Digital Pathology for Routine Diagnosis
Retámero, J. A., Aneiros‐Fernández, J., & del Moral, R. G. (2020) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2019-0355-le · Cited by 11 · see citing works
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Research works printed but not linked: 1
- [3]Matulonis UA, Lorusso D, Oaknin A, et al. J Clin Oncol. 2023;41(13):2436-2445. doi:10.1200/JCO.22.01900 · DOI
Cited by
Sorted by citations
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 · see citing works
Self-citationGenerative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
Zhang, P., & Kamel Boulos, M. N. (2023) · Future Internet · doi:10.3390/fi15090286 · Cited by 411 · see citing works
Lennerz, J. K., Salgado, R., Kim, G. E., Sirintrapun, S. J., Thierauf, J. C., Singh, A., Indave, I., Bard, A., Weissinger, S. E., Heher, Y. K., et al. (2023) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-1151 · Cited by 29 · see citing works
Dogru, E. Ö., & Krämer, N. C. (2025) · Journal of Decision Systems · doi:10.1080/12460125.2025.2593251 · Cited by 15 · see citing works
“Artificial Intelligence (AI) is becoming increasingly integrated into human decision-making processes across various domains, from everyday tasks to high-stakes fields like healthcare and finance (Mello-Thoms & Mello, 2023; Raciti et al., 2023; Weber et al., 2024).”
- Authors
- Raciti, P., Sue, J., Retamero, J. A., Ceballos, R., Godrich, R., Kunz, J. D., Casson, A., Thiagarajan, D., Ebrahimzadeh, Z., Viret, J., Lee, D., Schüffler, P. J., DeMuth, G., Gulturk, E., Kanan, C., Rothrock, B., Reis-Filho, J., Klimstra, D. S., Reuter, V., & Fuchs, T. J.
- Affiliations
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
- Stat One (DeMuth), Morrisville, North Carolina
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Fuchs, Thomas J.
- ORCID
- Venue
- Archives of Pathology & Laboratory Medicine
- Published
- 2023-09-27
- Volume
- 147
- Issue
- 10
- Pages
- 1178-1185
- ISSN
- 0003-9985 (ISSN-L)
- 1543-2165
- OpenAlex
- W4312018365
- Cited by
- 119
- References
- 22
- Citations by year
- 2026: 34 · 2025: 38 · 2024: 34 · 2023: 13
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Archives of Pathology & Laboratory Medicine (publishedVersion)
- PubMed (publishedVersion)
- 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 › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Keywords
- prostate cancer, artificial intelligence in pathology, whole slide images, computer-aided diagnosis, core needle biopsy, diagnostic accuracy, deep learning, histologic grading, clinical validation
- MeSH terms
- Artificial Intelligence, Biopsy, Needle, Humans, Image Interpretation, Computer-Assisted, Male, Prostate, Prostatic Neoplasms
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