Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477 · see citing works

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · see citing works

EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

Mottet, N., Joaquim Bellmunt, B., Bolla, M., Briers, E., Cumberbatch, M. G. K., Santis, M. D., Fossati, N., Gross, T., Henry, A. M., Joniau, S., et al. (2016) · European Urology · doi:10.1016/j.eururo.2016.08.003 · Cited by 3,223 · see citing works

MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis

Kasivisvanathan, V., Rannikko, A. S., Borghi, M., Panebianco, V., Mynderse, L. A., Vaarala, M. H., Briganti, A., Budäus, L., Hellawell, G. O., Hindley, R. G., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1801993 · Cited by 3,102 · see citing works

PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2

Weinreb, J. C., Barentsz, J. O., Choyke, P. L., Cornud, F., Haider, M. A., Macura, K. J., Margolis, D., Schnall, M. D., Shtern, F., Tempany, C. M., et al. (2015) · European Urology · doi:10.1016/j.eururo.2015.08.052 · Cited by 2,955 · see citing works

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30695-9 · Cited by 1,384 · see citing works

Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study

Rouvière, O., Puech, P., Renard‐Penna, R., Claudon, M., Roy, C., Mège‐Lechevallier, F., Decaussin‐Petrucci, M., Dubreuil-Chambardel, M., Magaud, L., Remontet, L., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30569-2 · Cited by 1,093 · see citing works

Evaluation of prostate segmentation algorithms for mri: The promise12 challenge

Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857 · see citing works

A large annotated medical image dataset for the development and evaluation of segmentation algorithmsPreprint

Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp‐Schneider, A., Landman, B. A., Litjens, G. J. S., Menze, B. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09063 · Cited by 703 · see citing works

Prostate Cancer: Multiparametric MR Imaging for Detection, Localization, and Staging

Hoeks, C. M. A., Barentsz, J. O., Hambrock, T., Yakar, D., Somford, D. M., Heijmink, S. W. T. P. J., Scheenen, T. W. J., Vos, P., Huisman, H. J., van Oort, I. M., et al. (2011) · Radiology · doi:10.1148/radiol.11091822 · Cited by 678 · see citing works

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

van Leeuwen, K. G., Schalekamp, S., Rutten, M. J. C. M., van Ginneken, B., & de Rooij, M. (2021) · European Radiology · doi:10.1007/s00330-021-07892-z · Cited by 461 · see citing works

Computer-Aided Detection of Prostate Cancer in MRI

Litjens, G. J. S., Debats, O. A., Barentsz, J. O., Karssemeijer, N., & Huisman, H. J. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2303821 · Cited by 453 · see citing works

MRI-Targeted or Standard Biopsy in Prostate Cancer Screening

Eklund, M., Jäderling, F., Discacciati, A., Bergman, M., Annerstedt, M., Aly, M., Glaessgen, A., Carlsson, S., Grönberg, H., & Nordström, T. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2100852 · Cited by 408 · see citing works

Prostate Imaging Quality (PI-QUAL): A New Quality Control Scoring System for Multiparametric Magnetic Resonance Imaging of the Prostate from the PRECISION trial

Giganti, F., ALLEN, C. M., Emberton, M., Moore, C. M., & Kasivisvanathan, V. (2020) · European Urology Oncology · doi:10.1016/j.euo.2020.06.007 · Cited by 318 · see citing works

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

Salim, M., Wåhlin, E., Dembrower, K., Azavedo, E., Foukakis, T., Liu, Y., Smith, K., Eklund, M., & Strand, F. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.3321 · Cited by 288 · see citing works

ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer

Salembier, C., Villeirs, G. M., Bari, B. D., Hoskin, P., Pieters, B. R., van Vulpen, M., Khoo, V. S., Henry, A. M., Bossi, A., Meerleer, G. D., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.01.014 · Cited by 250 · see citing works

Federated learning improves site performance in multicenter deep learning without data sharing

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · cited 1× · see citing works

Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet

Cao, R., Bajgiran, A. M., Mirak, S. A., Shakeri, S., Zhong, X., Enzmann, D. R., Raman, S. S., & Sung, K. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2901928 · Cited by 219 · see citing works

PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Armato, S. G., Huisman, H. J., Drukker, K., Hadjiiski, L. M., Kirby, J., Petrick, N., Redmond, G., Giger, M. L., Cha, K., Mamonov, A. B., et al. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.4.044501 · Cited by 196 · see citing works

Aspects of privacy for electronic health records

Haas, S., Wohlgemuth, S., Echizen, I., Sonehara, N., & Müller, G. (2010) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2010.10.001 · Cited by 183 · see citing works

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Venkadesh, K. V., Setio, A. A. A., Schreuder, A., Scholten, E. T., Chung, K., Wille, M. M. W., Saghir, Z., van Ginneken, B., Prokop, M., & Jacobs, C. (2021) · Radiology · doi:10.1148/radiol.2021204433 · Cited by 181 · see citing works

Reconsidering Prostate Cancer Mortality — The Future of PSA Screening

Welch, H. G., & Albertsen, P. C. (2020) · New England Journal of Medicine · doi:10.1056/nejmms1914228 · Cited by 180 · see citing works

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

de Vente, C., Vos, P., Hosseinzadeh, M., Pluim, J. P. W., & Veta, M. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2993528 · Cited by 177 · see citing works

End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction

Saha, A., Hosseinzadeh, M., & Huisman, H. J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102155 · Cited by 165 · see citing works

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

Tătaru, O. S., Vartolomei, M. D., Rassweiler, J. J., Osan, V. G., Lucarelli, G. T., Porpiglia, F., Amparore, D., Manfredi, M., Carrieri, G., Falagario, U. G., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11020354 · Cited by 163 · see citing works

International data-sharing norms: from the OECD to the General Data Protection Regulation (GDPR)

Phillips, M. A. (2018) · Human Genetics · doi:10.1007/s00439-018-1919-7 · Cited by 157 · see citing works

Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI

Greer, M. D., Shih, J. H., Lay, N. S., Barrett, T., Bittencourt, L. K., Borofsky, S., Kabakus, I. M., Law, Y. M., Marko, J., Shebel, H. M., et al. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20536 · Cited by 138 · see citing works

The Learning Curve in Prostate MRI Interpretation: Self-Directed Learning Versus Continual Reader Feedback

Rosenkrantz, A. B., Ayoola, A., Hoffman, D. C., Khasgiwala, A., Prabhu, V., Smereka, P. N., Somberg, M. B., & Taneja, S. S. (2016) · American Journal of Roentgenology · doi:10.2214/ajr.16.16876 · Cited by 134 · see citing works

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Hosseinzadeh, M., Saha, A., Brand, P., Slootweg, I., de Rooij, M., & Huisman, H. J. (2021) · European Radiology · doi:10.1007/s00330-021-08320-y · Cited by 122 · see citing works

Prostate cancer detection with biparametric magnetic resonance imaging (bpMRI) by readers with different experience: performance and comparison with multiparametric (mpMRI)

Gatti, M., Faletti, R., Calleris, G., Giglio, J., Berzovini, C., Gentile, F., Marra, G., Misischi, F., Molinaro, L., Bergamasco, L., et al. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-01934-3 · Cited by 120 · see citing works

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study

Winkel, D. J., Tong, A., Lou, B., Kamen, A., Comaniciu, D., Disselhorst, J. A., Rodríguez-Ruiz, A., Huisman, H., Szolar, D., Shabunin, I., et al. (2021) · Investigative Radiology · doi:10.1097/rli.0000000000000780 · Cited by 118 · see citing works

Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images

Pellicer-Valero, Ó. J., Jiménez, J. L. M., González‐Pérez, V., Ramón-Borja, J. L. C., García, I. M., Benito, M. B., Gómez, P. P., Rubio‐Briones, J., Rupérez, M. J., & Martín‐Guerrero, J. D. (2022) · Scientific Reports · doi:10.1038/s41598-022-06730-6 · Cited by 116 · see citing works

Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy

Aryanto, K. Y. E., Oudkerk, M., & van Ooijen, P. M. A. (2015) · European Radiology · doi:10.1007/s00330-015-3794-0 · Cited by 111 · see citing works

Supervised uncertainty quantification for segmentation with multiple annotations

Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., & Welling, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_16 · Cited by 105 · see citing works

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Twilt, J. J., van Leeuwen, K. G., Huisman, H. J., Fütterer, J. J., & de Rooij, M. (2021) · Diagnostics · doi:10.3390/diagnostics11060959 · Cited by 97 · see citing works

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

T., J. M. C., Arif, M. H., Niessen, W. J., Schoots, I. G., & Veenland, J. F. (2020) · Cancers · doi:10.3390/cancers12061606 · Cited by 90 · see citing works

FLOWER: A FRIENDLY FEDERATED LEARNING FRAMEWORKPreprint

Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernández-Marqués, J., Gao, Y., Sani, L., Li, K. H., Parcollet, T., de Gusmão, P. P. B., et al. (2022) · HAL · Cited by 75 · see citing works

Machine learning in prostate MRI for prostate cancer: current status and future opportunities

Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020289 · Cited by 74 · see citing works

A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

Mehralivand, S., Yang, D., Harmon, S. A., Xu, D., Xu, Z., Roth, H. R., Masoudi, S., Sanford, T., Kesani, D., Lay, N. S., et al. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.08.019 · Cited by 73 · see citing works

Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology

Montagne, S., Hamzaoui, D., Allera, A., Ezziane, M., Luzurier, A., Quint, R., Kalai, M., Ayache, N., Delingette, H., & Renard‐Penna, R. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01010-9 · Cited by 67 · see citing works

MSD-Net: Multi-Scale Discriminative Network for COVID-19 Lung Infection Segmentation on CT

Zheng, B., Liu, Y., Zhu, Y. M., Yu, F., Jiang, T., Yang, D., & Xu, T. (2020) · IEEE Access · doi:10.1109/access.2020.3027738 · Cited by 56 · see citing works

EMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for 3D medical image segmentation

Baldeon-Calisto, M., & Lai-Yuen, S. K. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102154 · Cited by 54 · see citing works

The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG)

Pedano, N., Flanders, A. E., Scarpace, L., Mikkelsen, T., Eschbacher, J. M., Hermes, B., Sisneros, V., Barnholtz-Sloan, J., & Ostrom, Q. T. (2016) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2016.l4ltd3tk · Cited by 53 · see citing works

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

Schelb, P., Wang, X., Radtke, J. P., Wiesenfarth, M., Kickingereder, P., Stenzinger, A., Hohenfellner, M., Schlemmer, H., Maier-Hein, K. H., & Bonekamp, D. (2020) · European Radiology · doi:10.1007/s00330-020-07086-z · Cited by 50 · see citing works

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept

Winkel, D. J., Wetterauer, C., Matthias, M., Lou, B., Shi, B., Kamen, A., Comaniciu, D., Seifert, H., Rentsch, C. A., & Boll, D. T. (2020) · Diagnostics · doi:10.3390/diagnostics10110951 · Cited by 49 · see citing works

SPIE-AAPM PROSTATEx Challenge Data

Litjens, G., Debats, O. A., Barentsz, J. O., Karssemeijer, N., & Huisman, H. (2017) · The Cancer Imaging Archive · doi:10.7937/k9tcia.2017.murs5cl · Cited by 47 · see citing works

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation

Fedorov, A., Fluckiger, J. U., Ayers, G. D., Li, X., Gupta, S., Tempany, C. M., Mulkern, R. V., Yankeelov, T. E., & Fennessy, F. M. (2014) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2014.01.004 · Cited by 45 · see citing works

A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation

McCrindle, B. W., Zukotynski, K., Doyle, T. E., & Noseworthy, M. D. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210031 · Cited by 38 · see citing works

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)

Saha, A., Twilt, J. J., Bosma, J. S., van Ginneken, B., Yakar, D., Elschot, M., Veltman, J., Futterer, J., de Rooij, M., & Huisman, H. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6522363 · Cited by 36 · see citing works

NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures (ISBI-MR-Prostate-2013)

Bloch, B., Madabhushi, A., Huisman, H., Freymann, J., Kirby, J., Grauer, M., Enquobahrie, A. A., Jaffe, C. C., Clarke, L., & Farahani, K. (2015) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2015.zf0vlopv · Cited by 36 · see citing works

Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy)

Natarajan, S., Priester, A. M., Margolis, D., Huang, J., & Marks, L. (2020) · The Cancer Imaging Archive · doi:10.7937/tcia.2020.a61ioc1a · Cited by 32 · see citing works

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition

Sunoqrot, M. R. S., Nketiah, G. A., Selnæs, K. M., Bathen, T. F., & Elschot, M. (2020) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-020-00871-3 · Cited by 29 · see citing works

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI

Sunoqrot, M. R. S., Selnæs, K. M., Sandsmark, E., Nketiah, G. A., Zavala‐Romero, O., Stoyanova, R. S., Bathen, T. F., & Elschot, M. (2020) · Diagnostics · doi:10.3390/diagnostics10090714 · Cited by 26 · see citing works

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)

Saha, A., Twilt, J. J., Bosma, J. S., et al. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6522364 · Cited by 21 · see citing works

Data From PROSTATE-MRI

Choyke, P., Turkbey, B., Pinto, P., Merino, M., & Wood, B. (2016) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2016.6046gudv · Cited by 21 · see citing works

Data From PROSTATE-DIAGNOSIS

Bloch, B., Jain, A., & Jaffe, C. C. (2015) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2015.foqeujvt · Cited by 18 · see citing works

Data From Prostate-3T

Litjens, G., Futterer, J., & Huisman, H. (2015) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2015.qjtv5il5 · Cited by 16 · see citing works

Supervised Uncertainty Quantification for Segmentation with Multiple AnnotationsPreprint

Shi, H., Worrall, D. E., Knegt, S. J. L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01949 · Cited by 15 · see citing works

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Wang, Y., Tadimalla, S., Hayden, A. J., Holloway, L. C., & Haworth, A. (2021) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13242 · Cited by 10 · see citing works

Fused Radiology-Pathology Prostate Dataset

Madabhushi, A., & Feldman, M. D. (2016) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2016.tlpmr1am · Cited by 10 · see citing works

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI

Sørland, K., Sunoqrot, M. R. S., Sandsmark, E., Langørgen, S., Bertilsson, H., Trimble, C. G., Lin, G., Selnæs, K. M., Goa, P. E., Bathen, T. F., et al. (2022) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-022-01003-9 · Cited by 8 · see citing works

Data From QIN-PROSTATE-Repeatability

Fedorov, A., Tempany, C. M., & Fennessy, F. (2019) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2018.mr1ckgnd · Cited by 6 · see citing works

Prostate158 - Training data

Bressem, K. K., Adams, L. C., & Engel, G. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6481141 · Cited by 4 · see citing works

Prostate158 - Training data

Bressem, K. K., Adams, L. C., & Engel, G. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6481140 · Cited by 4 · see citing works

Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRIPreprint

Bosma, J. S., Saha, A., Hosseinzadeh, M., Slootweg, I., de Rooij, M., & Huisman, H. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.05151 · Cited by 3 · see citing works

Original multi-parametric MRI images of prostate

Lemaitre, G., Martí, R., & Mériaudeau, F. (2016) · Universitat de Girona Digital Repository (Universitat de Girona) · doi:10.5281/zenodo.162231 · Cited by 2 · see citing works

Data From QIN PROSTATE

Fedorov, A., Tempany, C., Mulkern, R., & Fennessy, F. (2016) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2016.fads26kg · Cited by 2 · see citing works

Artificial 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

Underlying Features of Prostate Cancer—Statistics, Risk Factors, and Emerging Methods for Its Diagnosis

Berenguer, C. V., Pereira, F., Câmara, J. S., & Pereira, J. A. M. (2023) · Current Oncology · doi:10.3390/curroncol30020178 · Cited by 159 · see citing works

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis

Oerther, B., Nedelcu, A., Engel, H., Schmucker, C. M., Schwarzer, G., Brugger, T., Schoots, I. G., Eisenblätter, M., Sigle, A., Gratzke, C., et al. (2024) · Radiology · doi:10.1148/radiol.233337 · Cited by 73 · see citing works

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning

Koçak, B., Cuocolo, R., Santos, D. P. D., Stanzione, A., & Ugga, L. (2023) · Balkan Medical Journal · doi:10.4274/balkanmedj.galenos.2022.2022-11-51 · Cited by 64 · see citing works

Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

Rouvière, O., Jaouen, T., Baseilhac, P., Benomar, M. L., Escande, R., Crouzet, S., & Souchon, R. (2022) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.11.005 · Cited by 43 · see citing works

MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study

Lee, Y. J., Moon, H. W., Choi, M. H., Jung, S. E., Park, Y. H., Lee, J. Y., Kim, D. H., Rha, S. E., Kim, S. H., Lee, K. W., et al. (2025) · Radiology · doi:10.1148/radiol.232788 · Cited by 37 · see citing works

National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence

Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., et al. (2023) · Radiographics · doi:10.1148/rg.230180 · Cited by 35 · see citing works

Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists’ outcomes

Wenderott, K., Krups, J., Luetkens, J. A., Gambashidze, N., & Weigl, M. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111252 · Cited by 40 · see citing works

Technologies for non-invasive physiological sensing: Status, challenges, and future horizons

Yu, Y., Jain, B., Anand, G., Heidarian, M., Lowe, A., & Kalra, A. M. (2023) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2023.100420 · Cited by 34 · see citing works

Radiologists’ perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study

Wenderott, K., Krups, J., Luetkens, J. A., & Weigl, M. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104243 · Cited by 33 · see citing works

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges

Kim, H., Kang, S. W., Kim, J., Nagar, H., Sabuncu, M. R., Margolis, D., & Kim, C. K. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110887 · Cited by 32 · see citing works

Meta-Health: Learning-to-Learn (Meta-learning) as a Next Generation of Deep Learning Exploring Healthcare Challenges and Solutions for Rare Disorders: A Systematic Analysis

Singh, K., & Malhotra, D. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09927-8 · Cited by 34 · see citing works

Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives

Qiao, D., Wang, R. C., & Wang, Z. (2025) · Cells · doi:10.3390/cells14221804 · Cited by 38 · see citing works

Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy

Chan, T. H., Haworth, A., Wang, A., Osanlouy, M., Williams, S., Mitchell, C., Hofman, M. S., Hicks, R. J., Murphy, D. G., & Reynolds, H. M. (2023) · EJNMMI Research · doi:10.1186/s13550-023-00984-5 · Cited by 26 · see citing works

Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0

Türkbey, B., Huisman, H. J., Fedorov, A., Macura, K. J., Margolis, D., Panebianco, V., Oto, A., Schoots, I. G., Siddiqui, M. M., Moore, C. M., et al. (2025) · Radiology · doi:10.1148/radiol.240140 · Cited by 23 · see citing works

Medical machine learning operations: A framework to facilitate clinical AI development and deployment in radiology

de Almeida, J. G., Messiou, C., Withey, S. J., Matos, C., Koh, D.-M., & Papanikolaou, N. (2025) · European Radiology · doi:10.1007/s00330-025-11654-6 · Cited by 23 · see citing works

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies

Hamm, C. A., Baumgärtner, G. L., Padhani, A. R., Froböse, K. P., Dräger, F., Beetz, N. L., Savic, L. J., Posch, H., Lenk, J., Schallenberg, S., et al. (2024) · European Radiology · doi:10.1007/s00330-024-10700-z · Cited by 23 · see citing works

Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI‐algorithm for lesion detection and classification

Oerther, B., Engel, H., Nedelcu, A., Schlett, C. L., Grimm, R., von Busch, H., Sigle, A., Gratzke, C., Bamberg, F., & Benndorf, M. (2023) · The Prostate · doi:10.1002/pros.24528 · Cited by 20 · see citing works

Defining the Role of Large Language Models in Urologic Care and Research

Gupta, R., Pedraza, A. M., Gorin, M. A., & Tewari, A. K. (2023) · European Urology Oncology · doi:10.1016/j.euo.2023.07.017 · Cited by 20 · see citing works

A critical assessment of artificial intelligence in magnetic resonance imaging of cancer

Wu, C., Andaloussi, M. A., Hormuth, D. A., Lima, E. A. B. F., Lorenzo, G., Stowers, C. E., Ravula, S., Levac, B., Dimakis, A. G., Tamir, J. I., et al. (2025) · npj Imaging · doi:10.1038/s44303-025-00076-0 · Cited by 17 · see citing works

Towards Autonomous Healthcare: Integrating Artificial Intelligence (AI) for Personalized Medicine and Disease PredictionPreprint

Rane, N. L., Choudhary, S., & Rane, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4637894 · Cited by 18 · see citing works

Participatory AI Considerations for Advancing Racial Health Equity

Parker, A. G., Vardoulakis, L., Alla, J., & Harrington, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713165 · Cited by 15 · see citing works

Label-set impact on deep learning-based prostate segmentation on MRI

Meglič, J., Sunoqrot, M. R. S., Bathen, T. F., & Elschot, M. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01502-w · Cited by 15 · see citing works

Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges—A Narrative Review

Bolocan, V.-O., Mitoi, A., Nicu-Canareica, O., Băean, M.-L., Medar, C., & Popa, G.-A. (2025) · Journal of Imaging · doi:10.3390/jimaging11100335 · Cited by 14 · see citing works

Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking

Song, Y., Dornisch, A. M., Dess, R. T., Margolis, D., Weinberg, E., Barrett, T., Cornell, M., Fan, R. E., Harisinghani, M. G., Kamran, S. C., et al. (2025) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2025.03.024 · Cited by 15 · see citing works

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI

Yuan, Y., Ahn, E., Feng, D., Khadra, M. H., & Kim, J. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102510 · Cited by 11 · see citing works

The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging

Kaneko, M., Magoulianitis, V., Ramacciotti, L. S., Raman, A., Paralkar, D., Chen, A., Chu, T. N., Yang, Y., Xue, J., Yang, J., et al. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.08.001 · Cited by 13 · see citing works

Towards more reliable prostate cancer detection: Incorporating clinical data and uncertainty in MRI deep learning

Taguelmimt, K., Andrade-Miranda, G. X., Harb, H., Thanh, T. T., Dang, H.-P., Malavaud, B. A., & Bert, J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110440 · Cited by 10 · see citing works

Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications

Miao, K. H., Miao, J. H., Finkelstein, M., Chatterjee, A., & Oto, A. (2025) · Journal of Imaging · doi:10.3390/jimaging11110390 · Cited by 11 · see citing works

The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging

Canellas, R., Kohli, M. D., & Westphalen, A. C. (2023) · Current Oncology Reports · doi:10.1007/s11912-023-01371-y · Cited by 10 · see citing works

Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation

Andrade-Miranda, G. X., Vega, P. S., Taguelmimt, K., Dang, H.-P., Visvikis, D., & Bert, J. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102459 · Cited by 9 · see citing works

A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

Nayagam, R. D., & Selvathi, D. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23064 · Cited by 7 · see citing works

ProsGradNet: An effective and structured CNN approach for prostate cancer grading from histopathology images

Prabhu, A., Nedungatt, S., Lal, S., & Kini, J. R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107626 · Cited by 7 · see citing works

ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions

Zaridis, D. I., Mylona, E., Tsiknakis, N., Tachos, N. S., Matsopoulos, G. K., Kostas Marias, A., Tsiknakis, M., & Fotiadis, D. I. (2024) · Patterns · doi:10.1016/j.patter.2024.100992 · Cited by 6 · see citing works

Enhancing Prostate Cancer Classification: A Comprehensive Review of Multiparametric MRI and Deep Learning Integration

Valizadeh, G., Morafegh, M., Fatemi, F., Ghafoori, M., & Rad, H. S. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70004 · Cited by 5 · see citing works

Diagnostic efficiency of artificial intelligence for pulmonary nodules based on CT scans.

Pan, W. R., Fang, X., Zang, Z., Chi, B., Wei, X., & Li, C. (2023) · PubMed Central · Cited by 5 · see citing works

Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network

Yoshimura, T., Manabe, K., & Sugimori, H. (2023) · Applied Sciences · doi:10.3390/app13148028 · Cited by 5 · see citing works

Quantification of prostate tumour diameter and volume from MR images using 3D ellipsoid model and its impact on PI-RADS v2.1 assessment

Singh, D. K., Das, C. J., Kumar, V., Singh, A. K., & Mehndiratta, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-26065-6 · Cited by 5 · see citing works

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy

Cao, X., Fang, Y., Yang, C., Liu, Z., Xu, G., Jiang, Y., Wu, P., Song, W., Xing, H., & Wu, X. (2024) · International Neurourology Journal · doi:10.5213/inj.2346332.166 · Cited by 4 · see citing works

A survey of artificial intelligence/machine learning-based trends for prostate cancer analysis

Sailunaz, K., Beştepe, D., Alhajj, L., Özyer, T., Rokne, J. G., & Alhajj, R. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00471-4 · Cited by 4 · see citing works

Artificial intelligence in diagnosis of prostate cancer using magnetic resonance imaging. New approach

Aboyan, I. A., Redkin, V., Nazaruk, M. G., Polyakov, A. S., Pakus, S. M., Lemeshko, S. I., & Hasigov, A. (2024) · Cancer Urology · doi:10.17650/1726-9776-2024-20-2-35-43 · Cited by 4 · see citing works

Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases

Ramacciotti, L. S., Hershenhouse, J. S., Mokhtar, D., Paralkar, D., Kaneko, M., Eppler, M., Gill, K. S., Mogoulianitis, V., Duddalwar, V. A., Abreu, A. L., et al. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.08.003 · Cited by 4 · see citing works

LUND-PROBE – LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset

Rogowski, V., Olsson, L. E., Scherman, J. B., Persson, E., Kadhim, M., af Wetterstedt, S., Gunnlaugsson, A., Nilsson, M., Vass, N., Moreau, M., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-04954-5 · Cited by 4 · see citing works

Geometric multimodal learning to support prostate cancer diagnosis on limited and multicentric bi-parametric MRI data

Olmos, J., Antoine Manzanera, B., & Martínez, F. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11413-0 · Cited by 3 · see citing works

Prospective validation of an AI software for detecting clinically significant prostate cancer on biparametric MRI

Sunoqrot, M. R. S., Segre, R., Nketiah, G. A., Davik, P., Sjøbakk, T. E., Langørgen, S., Elschot, M., & Bathen, T. F. (2026) · Insights into Imaging · doi:10.1186/s13244-025-02199-9 · Cited by 3 · see citing works

Medical Imaging Data Strategies for Catalyzing AI Medical Device Innovation

Samala, R. K., Gallas, B. D., Zamzmi, G., Juluru, K., Khan, A., Bahr, C., Ochs, R. A., Carranza, D. L., Granstedt, J., Margerrison, E., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01374-6 · Cited by 3 · see citing works

Deep learning performance on MRI prostate gland segmentation: evaluation of two commercially available algorithms compared with an expert radiologist

Thimansson, E., Baubeta, E., Engman, J., Bjartell, A. S., & Zackrisson, S. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.1.015002 · Cited by 3 · see citing works

The Applications of Artificial Intelligence in Radiology: Opportunities and Challenges

Yordanova, M. Z. (2024) · European Journal of Medical and Health Sciences · doi:10.24018/ejmed.2024.6.2.2085 · Cited by 3 · see citing works

Significance and Implications of Prostate Cancer: Critical Overview for Future Directions in Africa

Gayi, B. K., Aiduenu, A. F., Dofuor, A. K., Tawiah-Nyarko, S., Loh, S. K., Yamoah, J. A. A., Ekloh, W., Akomea, A., Anovunga, S. A., & Asafu-Adjaye, A. (2026) · Cancer Investigation · doi:10.1080/07357907.2025.2598046 · Cited by 3 · see citing works

Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy

Moradi, A., Zerka, F., Bosma, J. S., Yakar, D., Geerdink, J., Huisman, H. J., Bathen, T. F., & Elschot, M. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2688568 · Cited by 2 · see citing works

Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection

Seo, S., Lee, I. K., Kim, H., Min, J., & Jung, C.-H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_15 · Cited by 1 · see citing works

Deep Learning Classification of Prostate Cancer Using MRI Histopathologic Data

Nguyen, C., Hulsey, G., James, K. M., James, T. W., & Carlson, J. M. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.240381 · Cited by 1 · see citing works

AI in Prostate MRI: A Task-Based Review

Choi, M. H. (2025) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2025.0062 · Cited by 1 · see citing works

Cross-Supervision Similarity Network for Medical Image Classification on Imbalanced Small Datasets

Liu, X., Luo, Y., Yi, Y., Zhang, J., Gao, X., & Zhuang, X. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3656364 · Cited by 1 · see citing works

Evaluating deep learning-based image segmentation for radiotherapy planning in pelvic and abdominal cancers

Chen, X., & Lai, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2025.1632370 · Cited by 1 · see citing works

Cooperative morphology-boundary network for accurate prostate segmentation from 3D MR images

Deng, Y., Song, T., Wang, X., Chen, Y., He, Q., & Huang, J. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109918 · Cited by 1 · see citing works

ProAttNet: a novel network of prostate segmentation with multi-attention residual U-Net using magnetic resonance images

Nayagam, R. D., Selvathi, D., & Shalini, S. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-11899-2 · Cited by 1 · see citing works

Comparative Analysis of Few-Shot Learning and Transfer Learning for Medical Imaging Tumour Classification

Middha, S., & Kumar, A. (2026) · International Conference on Signal Processing and Electronics Design (ICSPED) · doi:10.1109/icsped65817.2026.11448473 · Cited by 1 · see citing works

AI ‐Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI ‐ RADS v2.1: Performance Comparison Between Novice‐Level and Experienced Readers

Li, K., Mi, S., Chen, L., Jiang, M., Wang, B. Y., Huang, Y., Wang, Y., Fei, G., & Fu, K. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70411 · Cited by 1 · see citing works

Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Zaridis, D. I., Mylona, E., Tachos, N. S., Kalantzopoulos, C., Pezoulas, V. C., Koutsouris, D., Matsopoulos, G. K., Kostas Marias, A., Tsiknakis, M., & Fotiadis, D. I. (2023) · IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology · doi:10.1109/ieeeconf58974.2023.10404284 · Cited by 1 · see citing works

Editorial for “Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI : A Diagnostic Comparison Study”

Nather, J. C., & Muglia, V. F. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28428

AI in Prostate MRI Analysis: A Short, Subjective Review of Potential, Status, Urgent Challenges, and Future Directions

Jóźwiak, R., Mykhalevych, I., Gorbenko, I., Sobecki, P., Mitura, J., Lorenc, T., & Tupikowski, K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-37649-8_8

Prediction of Prostate Cancer Risk Stratifications Based on A Non-Linear Transformation Stacking Learning StrategyPreprint

cao, X., Fang, Y., Yang, C., Liu, Z., & Wu, X. (2023) · Research Square · doi:10.21203/rs.3.rs-3294510/v1

Clinical applications of artificial intelligence in Radiology: prostate magnetic resonance imaging

Cereser, L., Monterubbiano, L., Peruzzi, V., Zuiani, C., & Girometti, R. (2023) · Journal of Radiological Review · doi:10.23736/s2723-9284.23.00255-0

Rationale and quality assessment of automated morphometry of the prostate gland on magnetic resonance imaging

Nasibian, N. M., Vladzymyrskyy, A. V., & Arzamasov, K. M. (2025) · Journal of clinical practice · doi:10.17816/clinpract677719

AI in Emergency Radiology

Sharma, J., Shrivastava, D., Motan, S. N., & Choudhary, V. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5801-7.ch006

Accuracy of prostate measurements based on magnetic resonance imaging results using artificial intelligence technologies: retrospective diagnostic research

Nasibian, N. M., Vladzymyrskyy, A. V., & Arzamasov, K. M. (2025) · Diagnostic radiology and radiotherapy · doi:10.22328/2079-5343-2025-16-2-64-73

Model-Oriented Orchestration for Federated Learning: Personalization Through Intelligent Client Matching

Zaoui, C., Aharrane, N., Benabbou, F., Zaouch, A., & Bouaine, C. (2025) · IEEE Access · doi:10.1109/access.2025.3606865 · Cited by 1 · see citing works

Cross-Scanner Harmonization of AI/DL Accelerated Quantitative Bi-Parametric Prostate MRI

Malyarenko, D. I., Swanson, S. D., Richardson, J., Lowe, S., O’Connor, J. E., Jiang, Y., Chahine, R., Wells, S. A., & Chenevert, T. L. (2025) · Sensors · doi:10.3390/s25185858

Segmentation of Prostate Tumors via U-Net Architecture with Convolutional Neural Network Backbone

Dang, P. U. V., & Thuân, N. Đ. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4724-1_18

Additional file 1 of Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy

Chan, T. H., Haworth, A., Wang, A., Osanlouy, M., Williams, S., Mitchell, C., Hofman, M. S., Hicks, R. J., Murphy, D. G., & Reynolds, H. M. (2024) · Figshare · doi:10.6084/m9.figshare.26585145

Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation

Yang, J., Thorley, N., Abbasi, A. N., Punwani, S., Tse, Z., Hu, Y., & Saeed, S. U. (2026) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-026-03628-w

Recent advances in artificial intelligence-assisted medical imaging education

Zhu, Z., Zhang, T., Deng, L., Yuan, Y., Tang, H., Wei, Y. Y., & Li, Q. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1873826

Clinical readiness of artificial intelligence in urology and andrology: A PubMed-indexed scoping review and evidence map

Liegertová, M., Hujová, A., Vostrý, M., & Broul, M. (2026) · Therapeutic Advances in Urology · doi:10.1177/17562872261496667

Recent trends and techniques in prostate imaging

Nayak, K., Dkhar, W., Kadavigere, R., Venugopal, A., Praveen, N., Mayya, V., David, L. R., Pradhan, A., Uzun, B., Ozsahin, D. U., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-28967-5.00033-2

Recent trends and techniques in thorax and abdomen imaging

Pradhan, A., Dkhar, W., Sharath, S., Kadavigere, R., Venugopal, A., Praveen, N., David, L. R., Uzun, B., Ozsahin, D. U., Nayak, K., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-28967-5.00027-7

Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases

Ramacciotti, L. S., Hershenhouse, J. S., Mokhtar, D., Paralkar, D., Kaneko, M., Eppler, M., Gill, K. S., Mogoulianitis, V., Duddalwar, V. A., Abreu, A. L., et al. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.08.003 · Cited by 4 · see citing works

Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI Applications

Turkbey, B., & Haider, M. A. (2021) · American Journal of Roentgenology · doi:10.2214/ajr.21.26917 · Cited by 47 · see citing works

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Twilt, J. J., van Leeuwen, K. G., Huisman, H. J., Fütterer, J. J., & de Rooij, M. (2021) · Diagnostics · doi:10.3390/diagnostics11060959 · Cited by 97 · see citing works

Artificial Intelligence in Magnetic Resonance Imaging–based Prostate Cancer Diagnosis: Where Do We Stand in 2021?

Suarez-Ibarrola, R., Sigle, A., Eklund, M., Eberli, D., Miernik, A. J., Benndorf, M., Bamberg, F., & Gratzke, C. (2021) · European Urology Focus · doi:10.1016/j.euf.2021.03.020 · Cited by 54 · see citing works

Deep learning-based artificial intelligence applications in prostate MRI: brief summary

Türkbey, B., & Haider, M. A. (2021) · British Journal of Radiology · doi:10.1259/bjr.20210563 · Cited by 66 · see citing works