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Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
Open accessAbstract
Background The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater concordance; however, such expertise is not widely available. Prostate cancer diagnostics could thus benefit from robust, reproducible Gleason grading. We aimed to investigate the potential of deep learning to perform automated Gleason grading of prostate biopsies.Methods In this retrospective study, we developed a deep-learning system to grade prostate biopsies following the Gleason grading standard. The system was developed using randomly selected biopsies, sampled by the biopsy Gleason score, from patients at the Radboud University Medical Center (pathology report dated between Jan 1, 2012, and Dec 31, 2017). A semi-automatic labelling technique was used to circumvent the need for manual annotations by pathologists, using pathologists' reports as the reference standard during training. The system was developed to delineate individual glands, assign Gleason growth patterns, and determine the biopsy-level grade. For validation of the method, a consensus reference standard was set by three expert urological pathologists on an independent test set of 550 biopsies. Of these 550, 100 were used in an observer experiment, in which the system, 13 pathologists, and two pathologists in training were compared with respect to the reference standard. The system was also compared to an external test dataset of 886 cores, which contained 245 cores from a different centre that were independently graded by two pathologists.Findings We collected 5759 biopsies from 1243 patients. The developed system achieved a high agreement with the reference standard (quadratic Cohen's kappa 0·918, 95% CI 0·891-0·941) and scored highly at clinical decision thresholds: benign versus malignant (area under the curve 0·990, 95% CI 0·982-0·996), grade group of 2 or more (0·978, 0·966-0·988), and grade group of 3 or more (0·974, 0·962-0·984). In an observer experiment, the deep-learning system scored higher (kappa 0·854) than the panel (median kappa 0·819), outperforming 10 of 15 pathologist observers. On the external test dataset, the system obtained a high agreement with the reference standard set independently by two pathologists (quadratic Cohen's kappa 0·723 and 0·707) and within inter-observer variability (kappa 0·71).Interpretation Our automated deep-learning system achieved a performance similar to pathologists for Gleason grading and could potentially contribute to prostate cancer diagnosis. The system could potentially assist pathologists by screening biopsies, providing second opinions on grade group, and presenting quantitative measurements of volume percentages.Funding Dutch Cancer Society.
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“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 [63]
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
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
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Self-citationDermatologist-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
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
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
Self-citationEpstein, 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
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
Epstein, J. I., Allsbrook, W. C., Amin, M. B., & Egevad, L. L. (2005) · The American Journal of Surgical Pathology · doi:10.1097/01.pas.0000173646.99337.b1 · Cited by 2,763
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639
Research works printed but not linked: 1
- Naik, 2007, Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information, Proceedings of 2nd Workshop on Microsopic Image Analysis with Applications in Biology
Cited by
Sorted by citations
Items 201–300 of 713
Arita, Y., Roest, C., Kwee, T. C., Paudyal, R., Lema-Dopico, A., Fransen, S. J., Hirahara, D., Takaya, E., Ueda, R., Ruby, L. C., et al. (2025) · Asian journal of urology · doi:10.1016/j.ajur.2024.12.001 · Cited by 31
Deep learning-based grading of ductal carcinoma in situ in breast histopathology images
Wetstein, S. C., Stathonikos, N., Pluim, J. P. W., Heng, Y. J., ter Hoeve, N. D., Vreuls, C. P. H., van Diest, P. J., & Veta, M. (2021) · Laboratory Investigation · doi:10.1038/s41374-021-00540-6 · Cited by 31
Flinner, N., Gretser, S., Quaas, A., Bankov, K., Stoll, A. K., Heckmann, L., Mayer, R. S., Döring, C., Demes, M. C., Buettner, R., et al. (2022) · The Journal of Pathology · doi:10.1002/path.5879 · Cited by 31
Xu, X., Xi, L., Wei, L., Wu, L., Xu, Y., Liu, B., Li, B., Liu, K., Hou, G., Lin, H., et al. (2022) · European Radiology · doi:10.1007/s00330-022-09355-5 · Cited by 32
Zhdanovich, Y., Ackermann, J., Wild, P. J., Köllermann, J. W., Bankov, K., Döring, C., Flinner, N., Reis, H., Wenzel, M., Hoeh, B., et al. (2023) · BMC Bioinformatics · doi:10.1186/s12859-022-05124-9 · Cited by 32
Graham, S., Minhas, F., Bilal, M., Ali, M., Tsang, Y. W., Eastwood, M., Wahab, N., Jahanifar, M., Hero, E., Dodd, K., et al. (2023) · Gut · doi:10.1136/gutjnl-2023-329512 · Cited by 33
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Albrecht, T., Rossberg, A., Albrecht, J. D., Nicolay, J. P., Straub, B. K., Gerber, T. S., Albrecht, M., Brinkmann, F., Charbel, A., Schwab, C., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.07.026 · Cited by 31
Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance
Yang, Y., Sun, K., Gao, Y., Wang, K., & Yu, G. (2023) · Diagnostics · doi:10.3390/diagnostics13193115 · Cited by 35
Otálora, S., Marini, N., Müller, H., & Atzori, M. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00609-0 · Cited by 33
Peyster, E. G., Janowczyk, A. R., Swamidoss, A., Kethireddy, S., Feldman, M. D., & Margulies, K. B. (2022) · Circulation · doi:10.1161/circulationaha.121.058459 · Cited by 28
Tavolara, T. E., Gürcan, M. N., & Niazi, M. K. K. (2022) · Cancers · doi:10.3390/cancers14235778 · Cited by 34
Smit, M. A., Ciompi, F., Bokhorst, J., van Pelt, G. W., Geessink, O. G. F., Putter, H., Tollenaar, R. A. �. �., van Krieken, J. H., Mesker, W. E., & van der Laak, J. A. W. M. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100191 · Cited by 30
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Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.006 · Cited by 28
Eminağa, O., Abbas, M., Kunder, C. A., Tolkach, Y., Han, R., Brooks, J. D., Nolley, R., Semjonow, A., Boegemann, M., West, R. B., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-55228-w · Cited by 32
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
Kong, F., Wang, X., Xiang, J., Yang, S., Wang, X., Yue, M., Zhang, J., Zhao, J., Han, X., Dong, Y., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.03.028 · Cited by 36
Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms “in the Wild”
Faryna, K., Tessier, L., Retámero, J. A., Bonthu, S., Samanta, P., Singhal, N., Kammerer‐Jacquet, S., Radulescu, C., Agosti, V., Collin, A., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100563 · Cited by 28
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Elemento, O., Khozin, S., & Sternberg, C. N. (2025) · NEJM AI · doi:10.1056/aira2401164 · Cited by 28
Leo, P., Janowczyk, A. R., Elliott, R. M., Janaki, N., Bera, K., Shiradkar, R., Farré, X., Fu, P., El‐Fahmawi, A., Shahait, M., et al. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00174-3 · Cited by 29
Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology
Pettersen, H. S., Belevich, I. N., Røyset, E. S., Smistad, E., Simpson, M. R., Jokitalo, E., Reinertsen, I., Bakke, I., & Pedersen, A. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.816281 · Cited by 28
Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer
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Huo, X., Ong, K. H., Lau, K. W., Golé, L., Young, D. M., Tan, C. L., Zhu, X., Zhang, C., Zhang, Y., Li, L., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00502-1 · Cited by 27
Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer
Zhu, M. (2024) · American Journal of Clinical and Experimental Urology · doi:10.62347/jsae9732 · Cited by 34
Lami, K., Bychkov, A., Matsumoto, K., Attanoos, R. L., Berezowska, S., Brčić, L., Cavazza, A., English, J. C., Fabro, A. T., Ishida, K., et al. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0051-oa · Cited by 27
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Li, J., Lin, T., & Xu, Y. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87196-3_1 · Cited by 26
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Korzynska, A., Roszkowiak, L., Zak, J., & Siemion, K. (2021) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.04.012 · Cited by 26
Steinbuß, G., Kriegsmann, K., & Kriegsmann, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186652 · Cited by 26
Deep learning-based grading of ductal carcinoma in situ in breast histopathology images
Wetstein, S. C., Stathonikos, N., Pluim, J. P. W., Heng, Y. J., ter Hoeve, N. D., Vreuls, C. P. H., van Diest, P. J., Veta, M., Pathologie, Support, P., et al. (2021) · Utrecht University Repository (Utrecht University) · Cited by 26
Liu, B., Polack, M., Coudray, N., Claudio Quiros, A., Sakellaropoulos, T., Le, H., Karimkhan, A., Crobach, A. S. L. P., van Krieken, J. H. J. M., Yuan, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57541-y · Cited by 25
“Deep convolutionalneural networks,in particular, have bene fi ted the diagnostic process, initially by minimizing inter-rater disagreement and workload 7 – 9 .”
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Stathonikos, N., Nguyen, T. Q., & van Diest, P. J. (2020) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2020-206715 · Cited by 25
Lindvall, M., Lundström, C. F., & Löwgren, J. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450681 · Cited by 33
Golfe, A., del Amor, R., Colomer, A., Sales, M. A., Terrádez-Mas, L., & Naranjo, V. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107695 · Cited by 24
Durán-López, L., Dominguez-Morales, J. P., Gutiérrez-Galán, D., Ríos-Navarro, A., Jimenez-Fernandez, A. F., Vicente-Díaz, S., & Linares-Barranco, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104743 · Cited by 25
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
Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
Mercan, C., Balkenhol, M. C. A., Salgado, R., Sherman, M. E., Vielh, P. R., Vreuls, W., Polónia, A., Horlings, H. M., Weichert, W., Carter, J. M., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00488-w · Cited by 26
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
Wang, Z., Bi, Y., Pan, T., Wang, X., Bain, C., Bassed, R., Imoto, S., Yao, J., Daly, R. J., & Song, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad114 · Cited by 27
Chaurasia, A. K., Harris, H., Toohey, P. W., & Hewitt, A. W. (2025) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-025-00957-w · Cited by 23
Schuhmacher, D., Schörner, S., Küpper, C., Großerueschkamp, F., Sternemann, C., Lugnier, C., Kraeft, A., Jütte, H., Tannapfel, A., Reinacher‐Schick, A., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102594 · Cited by 26
Ding, H., Feng, Y., Huang, X., Xu, J., Zhang, T., Liang, Y., Wang, H., Chen, B., Mao, Q., Xia, W., et al. (2023) · Histopathology · doi:10.1111/his.14918 · Cited by 25
Zhao, L., Liu, Z., Xie, W., Shao, L., Lu, J. G., Tian, J., & Liu, J. (2023) · Military Medical Research · doi:10.1186/s40779-023-00464-w · Cited by 23
Distante, A., Marandino, L., Bertolo, R. G., Ingels, A., Pavan, N., Pecoraro, A., Marchioni, M., Carbonara, U., Erdem, S., Amparore, D., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13132294 · Cited by 28
Lami, K., Ota, N., Yamaoka, S., Bychkov, A., Matsumoto, K., Uegami, W., Munkhdelger, J., Seki, K., Sukhbaatar, O., Attanoos, R. L., et al. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.07.002 · Cited by 22
Chandramouli, S., Leo, P., Lee, G., Elliott, R. M., Davis, C., Zhu, G., Fu, P., Epstein, J. I., Veltri, R. W., & Madabhushi, A. (2020) · Cancers · doi:10.3390/cancers12092708 · Cited by 23
Deep-Learning–Based Screening and Ancillary Testing for Thyroid Cytopathology
Dov, D., Range, D. E., Cohen, J., Bell, J. L., Rocke, D. J., Kahmke, R. R., Weiss‐Meilik, A., Lee, W. T., Henao, R., Carin, L., et al. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.05.011 · Cited by 23
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
Liu, B., Wang, Y., Weitz, P., Lindberg, J., Hartman, J., Wang, W., Egevad, L., Grönberg, H., Eklund, M., & Rantalainen, M. (2022) · iScience · doi:10.1016/j.isci.2022.104663 · Cited by 22
Hansson, S. O. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009247290 · Cited by 23
Dual contribution of the mTOR pathway and of the metabolism of amino acids in prostate cancer
Schcolnik‐Cabrera, A., & Juárez-López, D. (2022) · Cellular Oncology · doi:10.1007/s13402-022-00706-4 · Cited by 25
Ren, S., Li, J., Dorado, J., Sierra, A., González‐Díaz, H., Duardo-Sánchez, A., & Shen, B. (2023) · Health Information Science and Systems · doi:10.1007/s13755-023-00264-5 · Cited by 22
Applications of Digital Pathology in Cancer: A Comprehensive Review
Omar, M., Alexanderani, M. K., Valencia, I., Loda, M. F., & Marchionni, L. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-062822-010523 · Cited by 24
Decoding pathology: the role of computational pathology in research and diagnostics
Hölscher, D. L., & Bülow, R. D. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03002-2 · Cited by 23
Ji, X., Salmon, R. M., Mulliqi, N., Khan, U., Wang, Y., Blilie, A., Olsson, H., Pedersen, B. G., Sørensen, K. D., Ulhøi, B. P., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100715 · Cited by 30
McGarry, S. D., Bukowy, J. D., Iczkowski, K. A., Lowman, A. K., Brehler, M., Bobholz, S. A., Nencka, A. S., Barrington, A., Jacobsohn, K. M., Unteriner, J., et al. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.5.054501 · Cited by 21
A data-driven ultrasound approach discriminates pathological high grade prostate cancer
Akatsuka, J., Numata, Y., Morikawa, H., Sekine, T., Kayama, S., Mikami, H., Yanagi, M., Endo, Y., Takeda, H., Toyama, Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-04951-3 · Cited by 22
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Sandeman, K., Blom, S., Koponen, V., Manninen, A., Juhila, J., Rannikko, A. S., Ropponen, T., & Mirtti, T. K. (2022) · Diagnostics · doi:10.3390/diagnostics12051031 · Cited by 22
Hu, L., Fu, C., Song, X., Grimm, R., von Busch, H., Benkert, T., Kamen, A., Lou, B., Huisman, H. J., Tong, A. Y., et al. (2023) · Cancer Imaging · doi:10.1186/s40644-023-00527-0 · Cited by 21
Oh, Y., Bae, G. E., Kim, K., Yeo, M., & Ye, J. C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3276778 · Cited by 22
Lami, K., Yoon, H., Parwani, A. V., Pham, H. H. N., Tachibana, Y., Linhart, C., Grinwald, M., Vecsler, M., & Fukuoka, J. (2024) · Pathology · doi:10.1016/j.pathol.2024.02.009 · Cited by 21
Ugli, I. K. K., Bhattacharjee, S., Sumon, R. I., Hwang, Y.-B., Rahman, H., Lee, M.-J., Kim, H., Park, E., Cho, N.-H., & Choi, H. (2023) · Cancers · doi:10.3390/cancers15030762 · Cited by 21
Nishio, M., Matsuo, H., Kurata, Y., Sugiyama, O., & Fujimoto, K. (2023) · Cancers · doi:10.3390/cancers15051535 · Cited by 20
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Moghadam, P. A., Bashashati, A., & Goldenberg, S. L. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.06.001 · Cited by 22
Farris, A. B., Alexander, M. P., Balis, U. J., Barisoni, L. M. C., Boor, P., Bülow, R. D., Cornell, L. D., Demetris, A. J., Farkash, E. A., Hermsen, M., et al. (2023) · Transplant International · doi:10.3389/ti.2023.11783 · Cited by 21
Zeng, H., Qiu, S.-Q., Zhuang, S., Wei, X., Wu, J., Zhang, R., Chen, K., Wu, Z., & Zhuang, Z. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1279982 · Cited by 22
Yan, C., Nakane, K., Wang, X., Fu, Y. S., Lu, H., Fan, X., Feldman, M. D., Madabhushi, A., & Xu, J. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105528 · Cited by 20
Şerbănescu, M.-S., Oancea, C. N., Streba, C. T., Pleşea, I. E., Pirici, D. N., Streba, L., & Pleşea, R. M. (2020) · Romanian Journal of Morphology and Embryology · doi:10.47162/rjme.61.2.21 · Cited by 21
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Kurian, N. C., Sethi, A., Konduru, A. R., Mahajan, A., & Rane, S. U. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1410 · Cited by 22
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Patil, A., Talha, M., Bhatia, A., Kurian, N. C., Mangale, S., Patel, S., & Sethi, A. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434121 · Cited by 20
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Hammouda, K., Khalifa, F., El-Melegy, M., Ghazal, M. A., Darwish, H. E., El‐Ghar, M. A., & El-Baz, A. S. (2021) · Sensors · doi:10.3390/s21206708 · Cited by 20
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Boge, F. J., & Mosig, A. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03033-9 · Cited by 24
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Li, Z., Mirjahanmardi, S. H., Sali, R., Eweje, F. R., Gopaulchan, M., Kloker, L. H., Zhang, X., Li, G., Jiang, Y., & Li, R. (2025) · Nature Communications · doi:10.1038/s41467-025-61349-1 · Cited by 21
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Wu, Y., Koyuncu, C., Toro, P., Corredor, G., Feng, Q., Buzzy, C. A., Old, M. O., Teknos, T. N., Connelly, S., Jordan, R. C., et al. (2022) · Oral Oncology · doi:10.1016/j.oraloncology.2022.105942 · Cited by 19
Mehmood, M., Abbasi, S. H., Aurangzeb, K., Majeed, M. F., Anwar, M. S., & Alhussein, M. A. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1225490 · Cited by 22
Bera, K., Katz, I. A., & Madabhushi, A. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00110 · Cited by 20
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Schuettfort, V. M., Pradère, B., Rink, M., Compérat, É., & Shariat, S. F. (2020) · Current Opinion in Urology · doi:10.1097/mou.0000000000000813 · Cited by 18
Kumar, N., Verma, R., Chen, C., Lu, C. Y., Fu, P., Willis, J. E., & Madabhushi, A. (2022) · The Journal of Pathology · doi:10.1002/path.5864 · Cited by 16
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Yang, Z., Wang, X., Xiang, J., Zhang, J., Yang, S., Wang, X., Yang, W., Li, Z., Han, X., & Liu, Y. (2023) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-023-03502-z · Cited by 18
Swillens, J. E. M., Nagtegaal, I. D., Engels, S., Lugli, A., Hermens, R., & van der Laak, J. A. W. M. (2023) · Oncogene · doi:10.1038/s41388-023-02797-1 · Cited by 18
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Schmidt, B., Soerensen, S. J. C., Bhambhvani, H. P., Fan, R. E., Bhattacharya, I., Choi, M. H., Kunder, C. A., Kao, C., Higgins, J., Rusu, M., et al. (2024) · British Journal of Urology · doi:10.1111/bju.16464 · Cited by 17
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Mundhada, A., Sundaram, S., Swaminathan, R., Cruze, L. D., Govindarajan, S., & Makaram, N. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100155 · Cited by 17
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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
Rocco, B. M. C., Cimadamore, A., Sarchi, L., Bonetti, L. R., Bertoni, L., Azzoni, P., Assumma, S., Turri, F. M., Bozzini, G., Eissa, A., et al. (2021) · Translational Andrology and Urology · doi:10.21037/tau-20-1237 · Cited by 15
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Metadata
- Authors
- Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G.
- Affiliations
- Radboud University Medical Center (NL)
- Corresponding author
- Bulten, Wouter
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 21
- Issue
- 2
- Pages
- 233-241
- ISSN
- 1470-2045 (ISSN-L)
- OpenAlex
- W2999091210
- Cited by
- 705
- References
- 39
- Citations by year
- 2026: 84 · 2025: 96 · 2024: 112 · 2023: 115 · 2022: 128 · 2021: 115 · 2020: 55
- FWCI
- 40.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Department of Health and Social Care
- KWF Kankerbestrijding (KUN 2015-7970)
- Radboud Universitair Medisch Centrum
- AbbVie
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Urology › Prostate Cancer Diagnosis and Treatment
- Keywords
- Gleason score, prostate cancer, deep learning, prostate biopsy, interobserver variability, external validation, benign versus malignant differentiation, urologic pathology, computer-aided diagnosis, recurrence prediction
- MeSH terms
- Deep Learning, Biopsy, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Male, Predictive Value of Tests, Prostatic Neoplasms, Retrospective Studies, Reproducibility of Results, Observer Variation, Automation, Laboratory, Neoplasm Grading
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