Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
Open accessAbstract
Background An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.Methods We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50-69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.Findings The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994-0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972-0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95-0·97) for the independent test dataset and 0·87 (0·84-0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60-0·73).Interpretation An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.Funding Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Multiple groups have reported Gleason scoring predictions from prostate cancer H&E images.
“multiple groups have reported on Gleason scoring predictions from prostate cancer H&E images (62)(63)(64).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [64]
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2015) · The American Journal of Surgical Pathology · doi:10.1097/pas.0000000000000530 · Cited by 3,246 · see citing works
Self-citationClinical-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
Deep 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
Complications After Prostate Biopsy: Data From SEER-Medicare
Loeb, S., Carter, H. B., Berndt, S. I., Ricker, W., & Schaeffer, E. M. (2011) · The Journal of Urology · doi:10.1016/j.juro.2011.06.057 · Cited by 683 · see citing works
Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495 · see citing works
Self-citationAutomated Gleason grading of prostate cancer tissue microarrays via deep learning
Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N. J., Hermanns, T., Fankhauser, C. D., Wey, N., Wild, P. J., Rüschoff, J. H., & Claassen, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-30535-1 · Cited by 447 · see citing works
Grönberg, H., Adolfsson, J., Aly, M., Nordström, T., Wiklund, P. N., Brandberg, Y., Thompson, J., Wiklund, F. E., Lindberg, J., Clements, M., et al. (2015) · The Lancet Oncology · doi:10.1016/s1470-2045(15)00361-7 · Cited by 410 · see citing works
Self-citationImprovement of pathology in sub-Saharan Africa
Adesina, A., Chumba, D., Nelson, A. M., Orem, J., Roberts, D. J., Wabinga, H., Wilson, M., & Rebbeck, T. R. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(12)70598-3 · Cited by 252 · see citing works
Robboy, S. J., Weintraub, S., Horvath, A. E., Jensen, B. W., Alexander, C. B., Fody, E. P., Crawford, J. M., Clark, J. R., Cantor-Weinberg, J., Joshi, M., et al. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2013-0200-oa · Cited by 219 · see citing works
Interobserver variability in Gleason histological grading of prostate cancer
Özkan, T. A., Eruyar, A. T., Cebeci, O. Ö., Memik, Ö., Özcan, L., & Kuskonmaz, I. (2016) · Scandinavian Journal of Urology · doi:10.1080/21681805.2016.1206619 · Cited by 199 · see citing works
Melia, J., Moseley, R., Ball, R. Y., Griffiths, D. F. R., Grigor, K. M., Harnden, P., Jarmulowicz, M., McWilliam, L. J., Montironi, R., Waller, M., et al. (2006) · Histopathology · doi:10.1111/j.1365-2559.2006.02393.x · Cited by 133 · see citing works
Kweldam, C. F., Nieboer, D., Algaba, F., et al. (2016) · Histopathology · doi:10.1111/his.12976 · Cited by 110 · see citing works
Ström, P., Nordström, T., Aly, M., Egevad, L., Grönberg, H., & Eklund, M. (2018) · European Urology · doi:10.1016/j.eururo.2017.12.028 · Cited by 110 · see citing works
Self-citationNir, G., Karimi, D., Goldenberg, S. L., Fazli, L., Skinnider, B., Tavassoli, P., Turbin, D. A., Villamil, C. F., Wang, G., Thompson, D. J. S., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.0442 · Cited by 107 · see citing works
del Toro, Ó. J., Atzori, M., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rönnquist, P., & Müller, H. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2255710 · Cited by 73 · see citing works
Towards grading gleason score using generically trained deep convolutional neural networks
Kallen, H., Molin, J., Heyden, A., et al. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493473 · Cited by 72 · see citing works
Utility of Pathology Imagebase for standardisation of prostate cancer grading
Egevad, L., Delahunt, B., Berney, D. M., et al. (2018) · Histopathology · doi:10.1111/his.13471 · Cited by 48 · see citing works
Self-citation(2018) · Nature · doi:10.1038/d41586-018-03067-x · Cited by 33 · see citing works
Pathology Imagebase—a reference image database for standardization of pathology
Egevad, L., Cheville, J. C., Evans, A. J., et al. (2017) · Histopathology · doi:10.1111/his.13313 · Cited by 30 · see citing works
Self-citationGummeson, A., Arvidsson, I., Ohlsson, M., Overgaard, N. C., Krzyzanowska, A., Heyden, A., Bjartell, A. S., & Åström, K. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253620 · Cited by 21 · see citing works
Egevad, L., Delahunt, B., Samaratunga, H., et al. (2019) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-019-02540-w · Cited by 17 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902 · see citing works
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749 · see citing works
Deep learning in cancer pathology: a new generation of clinical biomarkers
Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01122-x · Cited by 667 · see citing works
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446 · see citing works
ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model
Huang, H., Zheng, O., Wang, D., Yin, J., Wang, Z., Ding, S., Yin, H., Xu, C., Yang, R., Zheng, Q., et al. (2023) · International Journal of Oral Science · doi:10.1038/s41368-023-00239-y · Cited by 324 · see citing works
Improved breast cancer histological grading using deep learning
Wang, Y., Acs, B., Robertson, S., Liu, B., Solorzano, L., Wählby, C., Hartman, J., & Rantalainen, M. (2022) · Annals of Oncology · doi:10.1016/j.annonc.2021.09.007 · Cited by 211 · see citing works
Self-citationArtificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · 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
Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto dos Santos, D. (2021) · Journal of Medical Internet Research · doi:10.2196/24221 · Cited by 153 · see citing works
Hoang, D.-T., Dinstag, G., Shulman, E. D., Hermida, L. C., Ben-Zvi, D. S., Elis, E., Caley, K., Sammut, S.-J., Sinha, S., Sinha, N., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00793-2 · Cited by 157 · see citing works
Quality control stress test for deep learning-based diagnostic model in digital pathology
Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00859-x · Cited by 146 · see citing works
Raciti, P., Sue, J., Retamero, J. A., Ceballos, R., Godrich, R., Kunz, J. D., Casson, A., Thiagarajan, D., Ebrahimzadeh, Z., Viret, J., et al. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0066-oa · Cited by 119 · see citing works
Olsson, H., Kartasalo, K., Mulliqi, N., Capuccini, M., Ruusuvuori, P., Samaratunga, H., Delahunt, B., Lindskog, C., Janssen, E. A. M., Blilie, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34945-8 · Cited by 111 · see citing works
Self-citationSandbank, J., Bataillon, G., Nudelman, A., Krasnitsky, I., Mikulinsky, R., Bien, L., Thibault, L., Albrecht Shach, A., Sebag, G., Clark, D. P., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00496-w · Cited by 112 · see citing works
Evaluation of artificial intelligence on a reference standard based on subjective interpretation
Chen, P.-H. C., Mermel, C. H., & Liu, Y. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00216-8 · Cited by 63 · see citing works
Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34 · see citing works
Detection of perineural invasion in prostate needle biopsies with deep neural networks
Kartasalo, K., Ström, P., Ruusuvuori, P., Samaratunga, H., Delahunt, B., Tsuzuki, T., Eklund, M., & Egevad, L. (2022) · Virchows Archiv · doi:10.1007/s00428-022-03326-3 · Cited by 29 · see citing works
Self-citationMachine learning at the edge for AI-enabled multiplexed pathogen detection
Ganjalizadeh, V., Meena, G. G., Stott, M. A., Hawkins, A. R., & Schmidt, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-31694-6 · Cited by 24 · see citing works
Xie, C., Vanderbilt, C., Feng, C., Ho, D., Campanella, G., Egger, J., Plodkowski, A., Girshman, J., Sawan, P., Arbour, K., et al. (2022) · doi:10.21203/rs.3.rs-1251762/v1 · Cited by 8 · see citing works
- Authors
- 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., Iczkowski, K. A., Kench, J. G., Kristiansen, G., van der Kwast, T. H., Leite, K. R. M., McKenney, J. K., Oxley, J., Pan, C.-C., Samaratunga, H., Srigley, J. R., Takahashi, H., Tsuzuki, T., Varma, M., Zhou, M., Lindberg, J., Lindskog, C., Ruusuvuori, P., Wählby, C., Grönberg, H., Rantalainen, M., Egevad, L., & Eklund, M.
- Affiliations
- Karolinska Institutet (SE)
- Tampere University (FI)
- Uppsala University (SE)
- University of Otago (NZ)
- Queen Mary University of London (GB)
- Bostwick Laboratories (US)
- University Health Network (CA)
- Indiana University School of Medicine (US)
- Yale University (US)
- Medical College of Wisconsin (US)
- The University of Sydney (AU)
- University Hospital Bonn (DE)
- Cleveland Clinic (US)
- Southmead Hospital (GB)
- Taipei Veterans General Hospital (TW)
- The University of Queensland (AU)
- University of Toronto (CA)
- Jikei University School of Medicine (JP)
- Aichi Medical University (JP)
- University Hospital of Wales (GB)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Eklund, Martin
- ORCID
- Ström, Peter
- Kartasalo, Kimmo
- Olsson, Henrik
- Solorzano, Leslie
- Delahunt, Brett
- Berney, Daniel M
- Bostwick, David G
- Evans, Andrew J
- Grignon, David J
- Humphrey, Peter A
- Iczkowski, Kenneth A
- Kench, James G
- Kristiansen, Glen
- McKenney, Jesse K
- Oxley, Jon
- Pan, Chin-Chen
- Samaratunga, Hemamali
- Takahashi, Hiroyuki
- Tsuzuki, Toyonori
- Varma, Murali
- Zhou, Ming
- Lindberg, Johan
- Lindskog, Cecilia
- Ruusuvuori, Pekka
- Wählby, Carolina
- Grönberg, Henrik
- Rantalainen, Mattias
- Egevad, Lars
- Eklund, Martin
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 21
- Issue
- 2
- Pages
- 222-232
- ISSN
- 1470-2045 (ISSN-L)
- OpenAlex
- W2999399991
- Cited by
- 620
- References
- 31
- Citations by year
- 2026: 60 · 2025: 95 · 2024: 102 · 2023: 103 · 2022: 111 · 2021: 98 · 2020: 51
- FWCI
- 35.68
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-02-01)
- Open access
- green
- trepo.tuni.fi
- Licence
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by-nc-nd)
- Funders
- Tampereen Yliopisto
- European Research Council (ERC-2015-CoG 682810)
- Walter Ahlströmin Säätiö
- Emil Aaltosen Säätiö
- Academy of Finland (313921)
- Tekniikan Edistämissäätiö
- Cancerfonden
- Syöpäjärjestöt
- Vetenskapsrådet
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd
- Tampereen Teknillinen Yliopisto
- EIT Health
- Orionin Tutkimussäätiö
- KAUTE-Säätiö
- 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, Gleason grading, core needle biopsy, deep neural network, computer-aided diagnosis, tumor localization, observer variability, external validation, overtreatment and undertreatment, prostate cancer screening, histopathology image analysis
- MeSH terms
- Aged, Artificial Intelligence, Biopsy, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Prostatic Neoplasms, Sweden, Reproducibility of Results, Neoplasm Grading
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
