An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy
Open accessAbstract
Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists' workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York's Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either "Suspicious" or "Not Suspicious" for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate's handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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-citationDigital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Aeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., Lujan, G. M., Molani, M. A., Parwani, A. V., Lillard, K., et al. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_82_18 · Cited by 408 · see citing works
Hegde, J. V., Mulkern, R. V., Panych, L. P., et al. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23860 · Cited by 241 · see citing works
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 · see citing works
Raciti, 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-citationAmin, 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
Brimo, F., Schultz, L., & Epstein, J. I. (2010) · Journal of Urology · doi:10.1016/j.juro.2010.03.021 · Cited by 80 · see citing works
Agreement and Error Rates Using Blinded Review to Evaluate Surgical Pathology of Biopsy Material
Renshaw, A. A., Granter, S. R., Gould, E. W., & Cartagena, N. (2003) · American Journal of Clinical Pathology · doi:10.1309/dcxaxfvcchvhyu41 · Cited by 58 · see citing works
Montironi, R., Hammond, E. H., Lin, D. W., Gore, J. L., Srigley, J. R., Samaratunga, H., Egevad, L., Rubin, M. A., Nacey, J. N., Klotz, L. H., et al. (2014) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-014-1668-5 · Cited by 42 · see citing works
Atypical Small Acinar Proliferation in the Prostate: Clinical Significance in 2006
Bostwick, D. G., & Meiers, I. (2006) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2006-130-952-asapit · Cited by 41 · see citing works
Prostate atypia: Does repeat biopsy detect clinically significant prostate cancer?
Dorin, R. P., Wiener, S. V., Harris, C. D., et al. (2015) · The Prostate · doi:10.1002/pros.22950 · Cited by 30 · see citing works
Lesions missed on prostate biopsies in cases sent in for consultation
Kronz, J. D., Milord, R. A., Wilentz, R. E., Weir, E. G., Schreiner, S. R., & Epstein, J. I. (2003) · The Prostate · doi:10.1002/pros.10182 · Cited by 29 · 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
Flagged first, then by citations
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-citationMachine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works
- Authors
- Perincheri, S., Levi, A. W., Celli, R., Gershkovich, P., Rimm, D., Morrow, J. S., Rothrock, B., Raciti, P., Klimstra, D., & Sinard, J.
- Affiliations
- Yale University (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Perincheri, Sudhir
- ORCID
- Venue
- Modern Pathology
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 34
- Issue
- 8
- Pages
- 1588-1595
- ISSN
- 0893-3952 (ISSN-L)
- PMC
- PMC8295034
- OpenAlex
- W3143116276
- Cited by
- 154
- References
- 14
- Citations by year
- 2026: 24 · 2025: 39 · 2024: 42 · 2023: 22 · 2022: 21 · 2021: 5
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Modern Pathology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Paige.ai, Department of Pathology, Yale School of Medicine
- 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, whole slide images, prostate adenocarcinoma, diagnostic accuracy, digital pathology, machine learning
- MeSH terms
- Adenocarcinoma, Aged, Aged, 80 and over, Artificial Intelligence, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Pathology, Surgical, Prostatic Neoplasms, Sensitivity and Specificity, Biopsy, Large-Core Needle
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
