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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]
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“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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“Previous work has shown that CNNs can be used to classify WSI histology data, particularly in epithelial cancers, including the prediction of driver mutations in some cancers 20–29 .”
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Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer
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Fell, C., Mohammadi, M., MORRISON, D. L., Arandjelović, O., Syed, S., Konanahalli, P., Bell, S., Bryson, G., Harrison, D. J., & Harris‐Birtill, D. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282577 · Cited by 25
“One of the first AI algorithms with regulatory approval for use on histopathology WSI is for assisting diagnosis of prostate cancer [5].”
Flach, R. N., Fransen, N. L., Sonnen, A. F. ‐., Nguyen, T. Q., Breimer, G. E., Veta, M., Stathonikos, N., van Dooijeweert, C., & van Diest, P. J. (2022) · Diagnostics · doi:10.3390/diagnostics12051042 · Cited by 24
“AI assistance of pathologists on this task may not only save on IHC, but it may lower pathologists’ workload, as it has been shown that AI-assisted grading is less time consuming than traditional grading [8].”
Applications of artificial intelligence in prostate cancer histopathology
Busby, D., Grauer, R., Pandav, K. B., Khosla, A., Jain, P., Menon, M., Haines, G. K., Cordon‐Cardo, C., Gorin, M. A., & Tewari, A. K. (2023) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2022.12.002 · Cited by 26
Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry
Poalelungi, D. G., Neagu, A., Fulga, A., Neagu, M., Tutunaru, D., Nechita, A., & Fulga, I. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14070693 · Cited by 25
“Although not currently employed for IHC detection of PC, Raciti P. et al.’s study illustrated that in the absence of Paige Prostate Alpha, pathologists exhibited an average sensitivity of 74% and an average specificity of 97% [92].”
BackgroundMethodInfluentialAnonymization of whole slide images in histopathology for research and education
Bisson, T., Franz, M. M., Isil Dogan, O., Romberg, D., Jansen, C., Hufnagl, P., & Zerbe, N. (2023) · Digital Health · doi:10.1177/20552076231171475 · Cited by 19
“In publications, WSI anonymization is often performed by the physical covering of the slide label before scanning, which prevents the patient identification number from being captured by the scanning device [9, 10, 11].”
BackgroundA deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)
Ercan, C., Kordy, K., Knuuttila, A., Zhou, X., Kumar, D., Koponen, V., Mesenbrink, P. G., Eppenberger‐Castori, S., Amini, P., Pedrosa, M. C., et al. (2024) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-024-03841-5 · Cited by 19
“It has been shown that the diagnostic performance of a pathologist is improved when they have visual output overlay of AI model’s predictions along with slide images [13].”
Future artificial intelligence tools and perspectives in medicine
Chaddad, A., Katib, Y., & Hassan, L. (2021) · Current Opinion in Urology · doi:10.1097/mou.0000000000000884 · Cited by 18
Shen, B., Saito, A., Ueda, A., Fujita, K., Nagamatsu, Y., Hashimoto, M., Kobayashi, M., Mirza, A. H., Graf, H. P., Cosatto, E., et al. (2023) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.314 · Cited by 16
Patel, A., Mohanty, S. K., & Parwani, A. V. (2022) · Surgical pathology clinics · doi:10.1016/j.path.2022.08.001 · Cited by 16
Chervenkov, L., Sirakov, N., Kostov, G., Velikova, T. V., & Hadjidekov, G. (2023) · World Journal of Radiology · doi:10.4329/wjr.v15.i5.136 · Cited by 15
Harder, C., Pryalukhin, A. E., Quaas, A., Eich, M., Tretiakova, M., Klein, S., Seper, A., Heidenreich, A., Netto, G. J., Hulla, W., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100564 · Cited by 16
Dov, D., Assaad, S., Syedibrahim, A., Bell, J. L., Huang, J., Madden, J. F., Bentley, R. C., McCall, S. J., Henao, R., Carin, L., et al. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0850-oa · Cited by 15
Requa, J., Godard, T., Mandal, R. V., Balzer, B. L., Whittemore, D. E., George, E. V., Barcelona, F., Lambert, C., Lee, J. W. J., Lambert, A., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100159 · Cited by 14
“In contrast, studies that have evaluated the potential of AI in clinical pathologyworkflows have shown that the use of AI significantly decreases time to diagnoses and increases pathologist accuracy and diagnostic confidence.(85,86) Likewise, our preliminary results show significant reductions in the average time spen…”
ResultFrewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Corte, D. D. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0460-ra · Cited by 17
Gallo, M., Gallo, M., Nenutil, R., Němeček, M., Holub, P., & Brázdil, T. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.09.008 · Cited by 16
Regen med therapeutic opportunities for fighting COVID-19
Atala, A., Henn, A. D., Lundberg, M. S., Ahsan, T., Greenberg, J. M., Krukin, J., Lynum, S., Lutz, C., Cetrulo, K. J., Albanna, M. Z., et al. (2020) · Stem Cells Translational Medicine · doi:10.1002/sctm.20-0245 · Cited by 14
Current applications and challenges of artificial intelligence in pathology
Hanna, M. G., & Hanna, M. H. (2022) · Human Pathology Reports · doi:10.1016/j.hpr.2022.300596 · Cited by 14
Patel, A., Shaker, N., Mohanty, S. K., Sharma, S., Gangal, S., Eloy, C., & Parwani, A. V. (2022) · Diagnostics · doi:10.3390/diagnostics12081778 · Cited by 15
“…of patient cases for training, testing, and validation data sets, interpretations by multiple expert pathologists to establish ‘ground truth’ for diagnosis, use of slides from multiple institutions, and use of differing scanners including scanners from external institutions [23,26,28,30,81].”
BackgroundParwani, A. V., Patel, A., Zhou, M., Cheville, J. C., Tizhoosh, H. R., Humphrey, P. A., Reuter, V. E., & True, L. D. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100177 · Cited by 15
“304 expertly annotated prostate needle biopsy WSIs were used to establish ground truth for evaluation of the deep learning system.(21) General pathologists had an average sensitivity of 74% and specificity of 97%without the system, and an increased sensitivity to 90% with no change in specificity with the system.”
MethodSelf-citationArtificial intelligence: a new era in prostate cancer diagnosis and treatment
Vidiyala, N., Parupathi, P. R., Sunkishala, P., Muppavarapu, C. S., Gujja, A., Kanagala, P., Meduri, S. K., & Nyavanandi, D. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126024 · Cited by 13
Artificial Intelligence for Clinical Flow Cytometry
Seifert, R. P., Gorlin, D., & Borkowski, A. (2023) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2023.04.009 · Cited by 13
Marrying Global–Local Spatial Context for Image Patches in Computer-Aided Assessment
Yu, J., Ma, T., Chen, H., Lai, M., Ju, Z., & Xu, Y. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3290205 · Cited by 13
Prostate cancer: Novel genetic and immunologic biomarkers
Samare‐Najaf, M., Kouchaki, H., Mahini, S. M., Rounkian, M. S., Tavakoli, Y., Samareh, A. N., Azadbakht, M. K., & Jamali, N. (2024) · Clinica Chimica Acta · doi:10.1016/j.cca.2024.117824 · Cited by 13
The state of the art in artificial intelligence and digital pathology in prostate cancer
Ni, H. M., Kouzy, R., Sabbagh, A., Rooney, M. K., Feng, J., Castillo, S. P., Gadoue, S. M., Kouzi, Z. E., Hoffman, K. E., Yuan, Y., et al. (2025) · Nature Reviews Urology · doi:10.1038/s41585-025-01070-2 · Cited by 13
Trahearn, N., Sakr, C., Banerjee, A., Lee, S. H., Baker, A., Kocher, H. M., Angerilli, V., Morano, F., Bergamo, F., Maddalena, G., et al. (2025) · The Journal of Pathology · doi:10.1002/path.6378 · Cited by 15
Rakovic, K., Colling, R. T., Browning, L., Dolton, M., Horton, M. R., Protheroe, A. S., Lamb, A. D. G., Bryant, R. J., Scheffer, R., Crofts, J., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12051225 · Cited by 12
“Specific AI applications exist as adjuncts to the reporting of prostate core biopsies, such as suggestion of diagnosis and attribution of Gleason Score [15] and the pre-emptive requesting of immunohistochemistry in morphologically equivocal cases [16].”
BackgroundBellahsen-Harrar, Y., Lubrano, M., Lépine, C., Beaufrère, A., Bocciarelli, C., Brunet, A., Decroix, E., El-Sissy, F. N., Fabiani, B., Morini, A., et al. (2025) · PLOS One · doi:10.1371/journal.pone.0323270 · Cited by 11
“In line with a previous work of Raciti et al. on the binary diagnostic task of prostate cancer detection [20], we show that the benefice/risk balance is in favor of the use of AI even on expert fields such as squamous head and neck lesions, showing that AI improves overall performances while…”
Chang, J., & Hatfield, B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.05.006 · Cited by 13
Paik, I., Lee, G., Lee, J.-H., Kwak, T.-Y., & Ha, H. K. (2025) · Prostate International · doi:10.1016/j.prnil.2025.02.002 · Cited by 11
Flach, R. N., van Dooijeweert, C., Nguyen, T. Q., Lynch, M., Jonges, T. G. N., Meijer, R. P., Suelmann, B. B. M., Willemse, P.-P. M., Stathonikos, N., & van Diest, P. J. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00193 · Cited by 11
Recognizing Epithelial Cells in Prostatic Glands Using Deep Learning
Devnath, L., Arora, P., Carraro, A., Korbelik, J., Keyes, M., Wang, G., Guillaud, M. D., & MacAulay, C. E. (2025) · Cells · doi:10.3390/cells14100737 · Cited by 11
Irmakci, I., Nateghi, R., Zhou, R., Vescovo, M., Saft, M., Ross, A. E., Yang, X. J., Cooper, L., & Goldstein, J. A. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100422 · Cited by 12
Kotwal, J. G., Sharma, N. K., Somkunwar, R. K., Bavkar, D. M., Deshmukh, S., Khan, V., & Korade, N. B. (2025) · Iran Journal of Computer Science · doi:10.1007/s42044-025-00271-7 · Cited by 10
Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types
Chao, S. X., & Belanger, D. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00377 · Cited by 10
“Much effort has been devoted to automating cancer diagnosis by training neural networks to discern tumor from normal tissue in histopathology images [45, 25, 5, 54, 57, 30, 29, 70, 19, 7].”
BackgroundFlach, R. N., Stathonikos, N., Nguyen, T. Q., ter Hoeve, N. D., van Diest, P. J., & van Dooijeweert, C. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-067437 · Cited by 10
Wilbur, D. C., Smith, M. L., Cornell, L. D., Andryushkin, A., & Pettus, J. R. (2021) · Histopathology · doi:10.1111/his.14376 · Cited by 10
“Such machine-learning applications have already passed Food and Drug Administration (FDA) scrutiny and have been successfully deployed clinically.(7) In addition, the unique ability to view cases on a digital screen opens the possibility of efficient multiplex viewing of serial sections, which allows review of levels…”
BackgroundUsual interstitial pneumonia: a clinically significant pattern, but not the final word
Larsen, B. T. (2022) · Modern Pathology · doi:10.1038/s41379-022-01054-2 · Cited by 9
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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