2025
Journal article
Open Access
Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector
Gaivão A. M., Bireiro C., Santiago I., Joana Ip, Belião S., Matos C., Vanneschi L., Tsiknakis M., Marias K., Regge D., Silva S., Sfakianakis S., Kalokyri V., Trivizakis E., Kalliatakis G., Dimitriadis A., Fotiadis D., Tachos N., Mylona E., Zaridis D., Kalantzopoulos C., Papanikolaou N., De Almeida J. G., Castro Verde A., Rodrigues A. C., Rodrigues N., Chambel M., Huisman H., De Rooij M., Saha A., Twilt J. J., Futterer J., Martí-Bonmatí L., Cerdá-Alberich L., Ribas G., Navarro S., Marfil M., Neri E., Aringhieri G., Tumminello L., Mendola M., Akata V., Özmen M., Karaosmanoglu A. D., Atak F., Karcaaltincaba M., Vilanova J. C., Usinskiene J., Briediene R., Untanas A., Slidevska K., Vasilis K., Georgios G., Koh D. -M., Emsley R., Vit S., Ribeiro A., Doran S., Jacobs T., García-Martí G., Giannini V., Mazzetti S., Cappello G., Maimone G., Napolitano V., Colantonio S., Pascali M. A., Pachetti E., Del Corso G., Germanese D., Berti A., Carloni G., Kalpathy-Cramer J., Bridge C., Correia J., Hernandez W., Giavri Z., Pollalis C., Agraniotis D., Jiménez Pastor A., Munuera Mora J., Saillant C., Henne T., Marquez R.
Prostate-Specific Antigen Multiparametric Magnetic Resonance Imaging Humans Article Science Male Unnecessary Procedures Deep Learning Deep learning Medicine Q Prostatic Neoplasms R Neoplasm Grading Biopsy Prostate cancer
Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in order to detect PCa. These tests are often invasive, requiring either a biopsy (Gleason score and ISUP) or blood tests (PSA). Computational methods have been shown to help this process, using multiparametric MRI (mpMRI) data to detect PCa, effectively providing value during the diagnosis and monitoring stages. While delineating lesions requires a high degree of experience and expertise from the radiologists, being subject to a high degree of inter-observer variability, often leading to inconsistent readings, these computational models can leverage the information from mpMRI to locate the lesions with a high degree of certainty. By considering as positive samples only those that have an ISUP2 we can train aggressive index lesion detection models. The main advantage of this approach is that, by focusing only on aggressive disease, the output of such a model can also be seen as an indication for biopsy, effectively reducing unnecessary biopsy screenings. In this work, we utilize both the highly heterogeneous ProstateNet dataset, and the PI-CAI dataset, to develop accurate aggressive disease detection models.
Source: SCIENTIFIC REPORTS, vol. 15 (issue 1)
Siegel, RL, Miller, KD, Fuchs, HE, Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin.. 2022; 72: 7-33
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Cao, R. Performance of deep learning and genitourinary radiologists in detection of prostate cancer using 3-T multiparametric magnetic resonance imaging. J. Magn. Reson. Imaging. 2021; 54: 474-483
Steenbergen, P. Prostate tumor delineation using multiparametric magnetic resonance imaging: Inter-observer variability and pathology validation. Radiother. Oncol.. 2015; 115: 186-190
Chen, MY, Woodruff, MA, Dasgupta, P, Rukin, NJ. Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med.. 2020; 9: 7172-7182
Kushol, R, Parnianpour, P, Wilman, AH, Kalra, S, Yang, Y-H. Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci. Rep.. 2023; 13: 16791
Netzer, N. Fully automatic deep learning in bi-institutional prostate magnetic resonance imaging: Effects of cohort size and heterogeneity. Invest. Radiol.. 2021; 56: 799-808
10.Meglič, J., Sunoqrot, M. R. S., Bathen, T. F. & Elschot, M. Label-set impact on deep learning-based prostate segmentation on MRI. Insights Imaging 14. https://doi.org/10.1186/s13244-023-01502-w (2023).
Rodrigues, NM. Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data. Comput. Biol. Med.. 2024; 171: 108216
12.Rodrigues, A. et al. Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness. Sci. Rep.13. https://doi.org/10.1038/s41598-023-33339-0 (2023).
13.Pachetti, E. & Colantonio, S. 3d-vision-transformer stacking ensemble for assessing prostate cancer aggressiveness from t2w images. Bioengineering 10. https://doi.org/10.3390/bioengineering10091015 (2023).
Bernatz, S. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric mri using clinical assessment categories and radiomic features. Eur. Radiol.. 2020; 30: 6757-6769
15.Pellicer-Valero, O. J. et al. Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images. arXiv:2103.12650 (2022).
Dai, Z. Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance imaging using mask region-based convolutional neural networks. Adv. Radiat. Oncol.. 2020; 5: 473-481
17.Cao, R. et al. Prostate cancer detection and segmentation in multi-parametric MRI via cnn and conditional random field. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 1900–1904. https://doi.org/10.1109/ISBI.2019.8759584 (2019).
Hambarde, P. Prostate lesion segmentation in MR images using radiomics based deeply supervised u-net. Biocybern. Biomed. Eng.. 2020; 40: 1421-1435
Cao, R. Joint prostate cancer detection and Gleason score prediction in MP-MRI via focalnet. IEEE Trans. Med. Imaging. 2019; 38: 2496-2506
Hosseinzadeh, M. Deep learning-assisted prostate cancer detection on bi-parametric MRI: Minimum training data size requirements and effect of prior knowledge. Eur. Radiol.. 2022; 32: 2224-2234
Seetharaman, A. Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging. Med. Phys.. 2021; 48: 2960-2972
Khan, Z, Yahya, N, Alsaih, K, Ali, SSA, Meriaudeau, F. Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI. Sensors. 2020; 20: 3183
Khan, Z, Yahya, N, Alsaih, K, Al-Hiyali, MI, Meriaudeau, F. Recent automatic segmentation algorithms of MRI prostate regions: A review. IEEE Access. 2021; 9: 97878-97905
Bashkanov, O. Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI. Comput. Methods Programs Biomed.. 2023; 239: 107624
25.Saha, A. et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study. Lancet Oncol. (2024).
26.Saha, A. et al. The PI-CAI Challenge: Public Training and Development Dataset. https://doi.org/10.5281/zenodo.6517398 (2022).
27.Armato, S. G. et al. PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J. Med. Imaging (Bellingham)5, 044501 (2018).
28.Engels, R. R., Israël, B., Padhani, A. R. & Barentsz, J. O. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: What urologists need to know. Part 1: Acquisition. Eur. Urol.77, 457–468. https://doi.org/10.1016/j.eururo.2019.09.021 (2020).
Isensee, F, Jaeger, PF, Kohl, SAA, Petersen, J, Maier-Hein, KH. nnU-net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 2020; 18: 203-211
30.Zhu, Q., Du, B., Turkbey, B. I., Choyke, P. L. & Yan, P. Deeply-supervised cnn for prostate segmentation. In 2017 International Joint Conference on Neural Networks (IJCNN). 178–184 (2017).
31.Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems. Vol. 32. 8024–8035 (Curran Associates, Inc., 2019).
32.Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. arXiv:1606.00915 (2017).
33.Rodrigues, N. M., Silva, S., Vanneschi, L. & Papanikolaou, N. A comparative study of automated deep learning segmentation models for prostate MRI. Cancers 15. https://doi.org/10.3390/cancers15051467 (2023).
34.Dosovitskiy, A. et al. An image is worth 16 x 16 words: Transformers for image recognition at scale. https://doi.org/10.48550/ARXIV.2010.11929 (2020).
Murugesan, B, Liu, B, Galdran, A, Ayed, IB, Dolz, J. Calibrating segmentation networks with margin-based label smoothing. Med. Image Anal.. 2023; 87: 102826
Müller, R, Kornblith, S, Hinton, G. When Does Label Smoothing Help?. 2019
Bosma, JS. Semisupervised learning with report-guided pseudo labels for deep learning-based prostate cancer detection using biparametric MRI. Radiol. Artif. Intell.. 2023; 5: e230031
38.Yeghiazaryan, V. & Voiculescu, I. Family of boundary overlap metrics for the evaluation of medical image segmentation. J. Med. Imaging (Bellingham)5, 015006 (2018).
39.Maier, O. et al. loli/medpy: Medpy 0.4.0. https://doi.org/10.5281/zenodo.2565940 (2019).
Zhao, L. Predicting clinically significant prostate cancer with a deep learning approach: A multicentre retrospective study. Eur. J. Nucl. Med. Mol. Imaging. 2023; 50: 727-741
Hamm, CA. Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology. 2023; 307: e222276
Yu, R. PI-RADSAI: Introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI. Br. J. Cancer. 2023; 128: 1019-1029
Yu, AC, Mohajer, B, Eng, J. External validation of deep learning algorithms for radiologic diagnosis: A systematic review. Radiol. Artif. Intell.. 2022; 4: e210064
Bedoya, AD. Machine learning for early detection of sepsis: An internal and temporal validation study. JAMIA Open. 2020; 3: 252-260
Foote, HP. Development and temporal validation of a machine learning model to predict clinical deterioration. Hosp. Pediatr.. 2024; 14: 11-20
Vela, D. Temporal quality degradation in AI models. Sci. Rep.. 2022; 12: 11654
Kostick-Quenet, KM, Gerke, S. AI in the hands of imperfect users. NPJ Digit. Med.. 2022; 5: 197
Dratsch, T. Automation bias in mammography: The impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology. 2023; 307: e222176
Saha, A, Hosseinzadeh, M, Huisman, H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med. Image Anal.. 2021; 73: 102155
51.Hamm, C. A. et al. Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur. Radiol. (2024).
Hamm, CA. Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur. Radiol.. 2024
10.Meglič, J., Sunoqrot, M. R. S., Bathen, T. F. & Elschot, M. Label-set impact on deep learning-based prostate segmentation on MRI. Insights Imaging 14. 10.1186/s13244-023-01502-w (2023).
12.Rodrigues, A. et al. Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness. Sci. Rep.13. 10.1038/s41598-023-33339-0 (2023).
13.Pachetti, E. & Colantonio, S. 3d-vision-transformer stacking ensemble for assessing prostate cancer aggressiveness from t2w images. Bioengineering 10. 10.3390/bioengineering10091015 (2023).
17.Cao, R. et al. Prostate cancer detection and segmentation in multi-parametric MRI via cnn and conditional random field. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 1900–1904. 10.1109/ISBI.2019.8759584 (2019).
26.Saha, A. et al. The PI-CAI Challenge: Public Training and Development Dataset. 10.5281/zenodo.6517398 (2022).
28.Engels, R. R., Israël, B., Padhani, A. R. & Barentsz, J. O. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate c ancer: What urologists need to know. Part 1: Acquisition. Eur. Urol.77, 457–468. 10.1016/j.eururo.2019.09.021 (2020).
31.Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems. Vo l. 32. 8024–8035 (Curran Associates, Inc., 2019).
33.Rodrigues, N. M., Silva, S., Vanneschi, L. & Papanikolaou, N. A comparative study of automated deep learning segmentation models for prostate MRI. Cancers 15. 10.3390/cancers15051467 (2023).
34.Dosovitskiy, A. et al. An image is worth 16 x 16 words: Transformers for image recognition at scale. 10.48550/ARXIV.2010.11929 (2020).
39.Maier, O. et al. loli/medpy: Medpy 0.4.0. 10.5281/zenodo.2565940 (2019).
Resnick, MJ. Long-term functional outcomes after treatment for localized prostate cancer. N. Engl. J. Med.. 2013; 368: 436-445
3.Scott, R., Misser, S. K., Cioni, D. & Neri, E. PI-RADS v2.1: What has changed and how to report. SA J. Radiol.25, 2062 (2021).
4.Drost, F.-J. H. et al. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst. Rev.4, CD012663 (2019).
Cao, R. Performance of deep learning and genitourinary radiologists in detection of prostate cancer using 3-T multiparametric magnetic resonance imaging. J. Magn. Reson. Imaging. 2021; 54: 474-483
Steenbergen, P. Prostate tumor delineation using multiparametric magnetic resonance imaging: Inter-observer variability and pathology validation. Radiother. Oncol.. 2015; 115: 186-190
Chen, MY, Woodruff, MA, Dasgupta, P, Rukin, NJ. Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med.. 2020; 9: 7172-7182
Kushol, R, Parnianpour, P, Wilman, AH, Kalra, S, Yang, Y-H. Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci. Rep.. 2023; 13: 16791
Netzer, N. Fully automatic deep learning in bi-institutional prostate magnetic resonance imaging: Effects of cohort size and heterogeneity. Invest. Radiol.. 2021; 56: 799-808
10.Meglič, J., Sunoqrot, M. R. S., Bathen, T. F. & Elschot, M. Label-set impact on deep learning-based prostate segmentation on MRI. Insights Imaging 14. https://doi.org/10.1186/s13244-023-01502-w (2023).
Rodrigues, NM. Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data. Comput. Biol. Med.. 2024; 171: 108216
12.Rodrigues, A. et al. Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness. Sci. Rep.13. https://doi.org/10.1038/s41598-023-33339-0 (2023).
13.Pachetti, E. & Colantonio, S. 3d-vision-transformer stacking ensemble for assessing prostate cancer aggressiveness from t2w images. Bioengineering 10. https://doi.org/10.3390/bioengineering10091015 (2023).
Bernatz, S. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric mri using clinical assessment categories and radiomic features. Eur. Radiol.. 2020; 30: 6757-6769
15.Pellicer-Valero, O. J. et al. Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images. arXiv:2103.12650 (2022).
Dai, Z. Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance imaging using mask region-based convolutional neural networks. Adv. Radiat. Oncol.. 2020; 5: 473-481
17.Cao, R. et al. Prostate cancer detection and segmentation in multi-parametric MRI via cnn and conditional random field. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 1900–1904. https://doi.org/10.1109/ISBI.2019.8759584 (2019).
Hambarde, P. Prostate lesion segmentation in MR images using radiomics based deeply supervised u-net. Biocybern. Biomed. Eng.. 2020; 40: 1421-1435
Cao, R. Joint prostate cancer detection and Gleason score prediction in MP-MRI via focalnet. IEEE Trans. Med. Imaging. 2019; 38: 2496-2506
Hosseinzadeh, M. Deep learning-assisted prostate cancer detection on bi-parametric MRI: Minimum training data size requirements and effect of prior knowledge. Eur. Radiol.. 2022; 32: 2224-2234
Seetharaman, A. Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging. Med. Phys.. 2021; 48: 2960-2972
Khan, Z, Yahya, N, Alsaih, K, Ali, SSA, Meriaudeau, F. Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI. Sensors. 2020; 20: 3183
Khan, Z, Yahya, N, Alsaih, K, Al-Hiyali, MI, Meriaudeau, F. Recent automatic segmentation algorithms of MRI prostate regions: A review. IEEE Access. 2021; 9: 97878-97905
Bashkanov, O. Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI. Comput. Methods Programs Biomed.. 2023; 239: 107624
25.Saha, A. et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study. Lancet Oncol. (2024).
26.Saha, A. et al. The PI-CAI Challenge: Public Training and Development Dataset. https://doi.org/10.5281/zenodo.6517398 (2022).
27.Armato, S. G. et al. PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J. Med. Imaging (Bellingham)5, 044501 (2018).
28.Engels, R. R., Israël, B., Padhani, A. R. & Barentsz, J. O. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: What urologists need to know. Part 1: Acquisition. Eur. Urol.77, 457–468. https://doi.org/10.1016/j.eururo.2019.09.021 (2020).
Isensee, F, Jaeger, PF, Kohl, SAA, Petersen, J, Maier-Hein, KH. nnU-net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 2020; 18: 203-211
30.Zhu, Q., Du, B., Turkbey, B. I., Choyke, P. L. & Yan, P. Deeply-supervised cnn for prostate segmentation. In 2017 International Joint Conference on Neural Networks (IJCNN). 178–184 (2017).
31.Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems. Vol. 32. 8024–8035 (Curran Associates, Inc., 2019).
32.Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. arXiv:1606.00915 (2017).
33.Rodrigues, N. M., Silva, S., Vanneschi, L. & Papanikolaou, N. A comparative study of automated deep learning segmentation models for prostate MRI. Cancers 15. https://doi.org/10.3390/cancers15051467 (2023).
34.Dosovitskiy, A. et al. An image is worth 16 x 16 words: Transformers for image recognition at scale. https://doi.org/10.48550/ARXIV.2010.11929 (2020).
Murugesan, B, Liu, B, Galdran, A, Ayed, IB, Dolz, J. Calibrating segmentation networks with margin-based label smoothing. Med. Image Anal.. 2023; 87: 102826
Müller, R, Kornblith, S, Hinton, G. When Does Label Smoothing Help?. 2019
Bosma, JS. Semisupervised learning with report-guided pseudo labels for deep learning-based prostate cancer detection using biparametric MRI. Radiol. Artif. Intell.. 2023; 5: e230031
38.Yeghiazaryan, V. & Voiculescu, I. Family of boundary overlap metrics for the evaluation of medical image segmentation. J. Med. Imaging (Bellingham)5, 015006 (2018).
39.Maier, O. et al. loli/medpy: Medpy 0.4.0. https://doi.org/10.5281/zenodo.2565940 (2019).
Zhao, L. Predicting clinically significant prostate cancer with a deep learning approach: A multicentre retrospective study. Eur. J. Nucl. Med. Mol. Imaging. 2023; 50: 727-741
Hamm, CA. Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology. 2023; 307: e222276
Yu, R. PI-RADSAI: Introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI. Br. J. Cancer. 2023; 128: 1019-1029
Yu, AC, Mohajer, B, Eng, J. External validation of deep learning algorithms for radiologic diagnosis: A systematic review. Radiol. Artif. Intell.. 2022; 4: e210064
Bedoya, AD. Machine learning for early detection of sepsis: An internal and temporal validation study. JAMIA Open. 2020; 3: 252-260
Foote, HP. Development and temporal validation of a machine learning model to predict clinical deterioration. Hosp. Pediatr.. 2024; 14: 11-20
Vela, D. Temporal quality degradation in AI models. Sci. Rep.. 2022; 12: 11654
Kostick-Quenet, KM, Gerke, S. AI in the hands of imperfect users. NPJ Digit. Med.. 2022; 5: 197
Dratsch, T. Automation bias in mammography: The impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology. 2023; 307: e222176
Saha, A, Hosseinzadeh, M, Huisman, H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med. Image Anal.. 2021; 73: 102155
51.Hamm, C. A. et al. Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur. Radiol. (2024).
Hamm, CA. Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur. Radiol.. 2024
10.Meglič, J., Sunoqrot, M. R. S., Bathen, T. F. & Elschot, M. Label-set impact on deep learning-based prostate segmentation on MRI. Insights Imaging 14. 10.1186/s13244-023-01502-w (2023).
12.Rodrigues, A. et al. Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness. Sci. Rep.13. 10.1038/s41598-023-33339-0 (2023).
13.Pachetti, E. & Colantonio, S. 3d-vision-transformer stacking ensemble for assessing prostate cancer aggressiveness from t2w images. Bioengineering 10. 10.3390/bioengineering10091015 (2023).
17.Cao, R. et al. Prostate cancer detection and segmentation in multi-parametric MRI via cnn and conditional random field. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 1900–1904. 10.1109/ISBI.2019.8759584 (2019).
26.Saha, A. et al. The PI-CAI Challenge: Public Training and Development Dataset. 10.5281/zenodo.6517398 (2022).
28.Engels, R. R., Israël, B., Padhani, A. R. & Barentsz, J. O. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate c ancer: What urologists need to know. Part 1: Acquisition. Eur. Urol.77, 457–468. 10.1016/j.eururo.2019.09.021 (2020).
31.Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems. Vo l. 32. 8024–8035 (Curran Associates, Inc., 2019).
33.Rodrigues, N. M., Silva, S., Vanneschi, L. & Papanikolaou, N. A comparative study of automated deep learning segmentation models for prostate MRI. Cancers 15. 10.3390/cancers15051467 (2023).
34.Dosovitskiy, A. et al. An image is worth 16 x 16 words: Transformers for image recognition at scale. 10.48550/ARXIV.2010.11929 (2020).
39.Maier, O. et al. loli/medpy: Medpy 0.4.0. 10.5281/zenodo.2565940 (2019).
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BibTeX entry
@article{oai:iris.cnr.it:20.500.14243/556009,
title = {Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector},
author = {Gaivão A. M. and Bireiro C. and Santiago I. and Joana Ip and Belião S. and Matos C. and Vanneschi L. and Tsiknakis M. and Marias K. and Regge D. and Silva S. and Sfakianakis S. and Kalokyri V. and Trivizakis E. and Kalliatakis G. and Dimitriadis A. and Fotiadis D. and Tachos N. and Mylona E. and Zaridis D. and Kalantzopoulos C. and Papanikolaou N. and De Almeida J. G. and Castro Verde A. and Rodrigues A. C. and Rodrigues N. and Chambel M. and Huisman H. and De Rooij M. and Saha A. and Twilt J. J. and Futterer J. and Martí-Bonmatí L. and Cerdá-Alberich L. and Ribas G. and Navarro S. and Marfil M. and Neri E. and Aringhieri G. and Tumminello L. and Mendola M. and Akata V. and Özmen M. and Karaosmanoglu A. D. and Atak F. and Karcaaltincaba M. and Vilanova J. C. and Usinskiene J. and Briediene R. and Untanas A. and Slidevska K. and Vasilis K. and Georgios G. and Koh D. -M. and Emsley R. and Vit S. and Ribeiro A. and Doran S. and Jacobs T. and García-Martí G. and Giannini V. and Mazzetti S. and Cappello G. and Maimone G. and Napolitano V. and Colantonio S. and Pascali M. A. and Pachetti E. and Del Corso G. and Germanese D. and Berti A. and Carloni G. and Kalpathy-Cramer J. and Bridge C. and Correia J. and Hernandez W. and Giavri Z. and Pollalis C. and Agraniotis D. and Jiménez Pastor A. and Munuera Mora J. and Saillant C. and Henne T. and Marquez R.},
doi = {10.1038/s41598-025-99795-y},
year = {2025}
}
0000-0002-0089-6420
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