Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review

Authors

  • Ali Talyshinskii Astana Medical University, 49a, ul. Beibitshilik, Astana, 010000, Kazakhstan ; St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation
  • Irina Kamyshanskaya St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation ;St. Petersburg City Mariinsky Hospital, 56, Liteiny pr., St. Petersburg, 191014, Russian Federation
  • Andrey Mischenko St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation ;St. Petersburg City Mariinsky Hospital, 56, Liteiny pr., St. Petersburg, 191014, Russian Federation;National Medical Research Center of Oncology named after N. N. Petrov, 68, Leningradskaya ul., St. Petersburg, 197758, Russian Federation
  • Bakhman Guliev St. Petersburg City Mariinsky Hospital, 56, Liteiny pr., St. Petersburg, 191014, Russian Federation
  • Rustam Bakhtiozin I. M. Sechenov First Moscow State Medical University (Sechenov University), 8, ul. Trubetskaya, Moscow, 119048, Russian Federation

DOI:

https://doi.org/10.21638/spbu11.2023.204

Abstract

This review examines the current methodologies employed in utilizing artificial intelligence for the identification and classification of prostate cancer using magnetic resonance imaging data. It outlines the volume of data utilized and highlights the most commonly sought-after sequences employed for training neural networks. The review further presents the accuracy metrics of the neural networks analyzed, accompanied by a succinct explanation of each metric. Furthermore, the review pinpoints the limitations associated with contemporary neural networks devised for the detection and classification of prostate cancer using magnetic resonance imaging data, as well as the challenges encountered during their creation and implementation.In summary, this comprehensive analysis delves into the existing approaches in leveraging artificial intelligence for prostate cancer detection and stratification through magnetic resonance imaging data. It addresses the data scale and preferred magnetic resonance imaging sequences employed for neural network training. The review provides a breakdown of accuracy indicators for the neural networks evaluated, elucidating their respective capabilities. Moreover, the review identifies the drawbacks associated with current neural network models developed for prostate cancer detection and stratification via magnetic resonance imaging data, while also recognizing the complexities involved in their development and practical application.

Keywords:

рак предстательной железы, МРТ, искусственный интеллект.

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References

Shatylko T. V., Popkov V. M., Fomkin R. N. Integrative approach to pre-operative determination of clinically significant prostate cancer. Saratovskii nauchno-meditsinskii zhurnal, 2015, vol. 11, no. 3,pp. 345–348. (In Russian)

Westphalen A. C., McCulloch C. E., Anaokar J. M., Arora S., Barashi N. S., Barentsz J. O., Bathala T. K., Bittencourt L. K., Booker M. T., Braxton V. G., Carroll P. R., Casalino D. D., Chang S. D., Coakley F. V., Dhatt R., Eberhardt S. C., Foster B. R., Froemming A. T., Fütterer J. J., Ganeshan D. M., Gertner M. R., Mankowski Gettle L., Ghai S., Gupta R. T., Hahn M. E., Houshyar R., Kim C., Kim C. K., Lall C., Margolis D. J. A., McRae S. E., Oto A., Parsons R. B., Patel N. U., Pinto P. A., Polascik T. J., Spilseth B., Starcevich J. B., Tammisetti V. S., Taneja S. S., Turkbey B., Verma S., Ward J. F., Warlick C. A., Weinberger A. R., Yu J., Zagoria R. J., Rosenkrantz A. B. Variability of the Positive Predictive Value of PIRADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology, 2020, vol. 296, no. 1, pp. 76–84.

Harish V., Morgado F., Stern A. D., Das S. Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty. Acad. Med., 2021, vol. 96, no. 1, pp. 31–36.

Reva S. A., Shaderkin I. A., Ziatchin I. V., Petrov S. B. Artificial intelligence in oncourology.Eksperimental’naia i klinicheskaia urologiia, 2021, vol. 14, no. 2, pp. 46–51. (In Russian)

Wang J., Wu C. J., Bao M. L., Zhang J., Wang X. N., Zhang Y. D. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur. Radiol., 2017, vol. 27, no. 10, pp. 4082–4090.

Alvarez-Jimenez C., Barrera C., Munera N., Viswanath S. E., Romero E. Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI. Annu. Int. Conf. IEEE Eng.Med. Biol. Soc., 2019, pp. 2695–2698.

Cuocolo R., Stanzione A., Faletti R., Gatti M., Calleris G., Fornari A., Gentile F., Motta A., Dell’Aversana S., Creta M., Longo N., Gontero P., Cirillo S., Fonio P., Imbriaco M. MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur. Radiol., 2021, vol. 31, no. 10, pp. 7575–7583.

Gholizadeh N., Simpson J., Ramadan S., Denham J., Lau P., Siddique S., Dowling J., Welsh J., Chalup S.,Greer P. B. Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI. J. Appl. Clin. Med. Phys., 2020, vol. 21, no. 10, pp. 179–191.

Giannini V., Mazzetti S., Defeudis A., Stranieri G., Calandri M., Bollito E., Bosco M., Porpiglia F.,Manfredi M., De Pascale A., Veltri A., Russo F., Regge D. A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation. Front. Oncol., 2021, vol. 11, p. 718155.

Varghese B., Chen F., Hwang D., Palmer S. L., De Castro Abreu A. L., Ukimura O., Aron M., Aron M.,Gill I., Duddalwar V., Pandey G. Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Sci. Reports, 2019, vol. 9, no. 1,pp. 1–10.

Kwon D., Reis I. M., Breto A. L., Tschudi Y., Gautney N., Zavala-Romero O., Lopez C., Ford J. C., Punnen S., Pollack A., Stoyanova R. Classification of suspicious lesions on prostate multiparametric MRI using machine learning. J. Med. Imaging, 2018, vol. 5, no. 3, p. 034502.

Hectors S. J., Chen C., Chen J., Wang J., Gordon S., Yu M., Al Hussein Al Awamlh B., Sabuncu M. R., Margolis D. J. A., Hu J. C. Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions. J. Magn. Reson. Imaging.2021, vol. 54, no. 5, pp. 1466–1473.

Zhang L., Zhe X., Tang M., Zhang J., Ren J., Zhang X., Li L. Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature. Contrast Media Mol Imaging, 2021, 7830909.

Liu B., Cheng J., Guo D. J., He X. J., Luo Y. D., Zeng Y., Li C. M. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI. Clin. Radiol., 2019, vol. 74, no. 11, pp. 896.e1–896.e8.

Wu M., Krishna S., Thornhill R. E., Flood T. A., McInnes M. D. F., Schieda N. Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis. J. Magn. Reson. Imaging, 2019, vol. 50, no. 3, pp. 940–950.

Iyama Y., Nakaura T., Katahira K., Iyama A., Nagayama Y., Oda S., Utsunomiya D., Yamashita Y. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI. Eur. Radiol, 2017, vol. 27, no. 9,pp. 3600–3608.

Akamine Y., Ueda Y., Ueno Y., Sofue K., Murakami T., Yoneyama M., Obara M., Van Cauteren M. Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy. Magn. Reson. Imaging, 2020, vol. 74, pp. 90–95.

Liu S., Zheng H., Feng Y., Li W. Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI. arXiv:1703.04078.

Stanzione A., Cuocolo R., Cocozza S., Romeo V., Persico F., Fusco F., Longo N., Brunetti A., Imbriaco M. Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results. Acad. Radiol., 2019, vol. 26, no. 10, pp. 1338–1344.

Ishioka J., Matsuoka Y., Uehara S., Yasuda Y., Kijima T., Yoshida S., Yokoyama M., Saito K., Kihara K.,Numao N., Kimura T., Kudo K., Kumazawa I., Fujii Y. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. B. J. U. Int., 2018,vol. 122, no. 3, pp. 411–417.

Zabihollahy F., Ukwatta E., Krishna S., Schieda N. Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method.J. Magn. Reson. Imaging, 2020, vol. 51, no. 4, pp. 1223–1234.

Mehrtash A., Sedghi A., Ghafoorian M., Taghipour M., Tempany C. M., Wells W. M. 3rd, Kapur T., Mousavi P., Abolmaesumi P., Fedorov A. Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks. Proc. SPIE Int. Soc. Opt. Eng., 2017, pp. 10134–101342A.

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, vol. 73, p. 102155.

Chen Q., Hu S., Long P., Lu F., Shi Y., Li Y. A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI. Technol. Cancer Res. Treat., 2019, vol. 18, p. 1533033819858363.

Sobecki P., Jóźwiak R., Sklinda K., Przelaskowski A. Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images. PeerJ, 2021, no. 9, p. e11006.

Sanyal J., Banerjee I., Hahn L., Rubin D. An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence. AMIA J. Summits Transl. Sci. Proc., 2020,pp. 552–560.

Bhattacharya I., Seetharaman A., Shao W., Sood R., Kunder C. A., Fan R. E., Soerensen S. J. C., Wang J. B., Ghanouni P., Teslovich N. C., Brooks J. D., Sonn G. A., Rusu M. CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis. Lect. Notes Comput. Sci. (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics),2020, 12262 LNCS, pp. 315–325.

Yu X., Lou B., Shi B., Winkel D., Arrahmane N., Diallo M., Meng T., von Busch H., Grimm R., Kiefer B.,Comaniciu D., Kamen A., Huisman H., Rosenkrantz A., Penzkofer T., Shabunin I., Yang Q., Szolar D. False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection in Multi-Parametric MRI Scans. Proc. — Int. Symp. Biomed. Imaging, 2020, pp. 1355–1359.

Yoo S., Gujrathi I., Haider M. A., Khalvati F. Prostate Cancer Detection using Deep Convolutional Neural Networks. Sci. Reports, 2019, vol. 9, no. 1, pp. 1–10.

Zhong X., Cao R., Shakeri S., Scalzo F., Lee Y., Enzmann D. R., Wu H. H., Raman S. S., Sung K. Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI. Abdom. Radiol., 2019, vol. 44, no. 6, pp. 2030–2039.

Khosravi P., Lysandrou M., Eljalby M., Li Q., Kazemi E., Zisimopoulos P., Sigaras A., Brendel M., Barnes J., Ricketts C., Meleshko D., Yat A., McClure T. D., Robinson B. D., Sboner A., Elemento O., Chughtai B., Hajirasouliha I. A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion. J. Magn. Reson. Imaging, 2021, vol. 54, no. 2, pp. 462–471.

Arif M., Schoots I. G., Castillo Tovar J., Bangma C. H., Krestin G. P., Roobol M. J., Niessen W., Veenland J. F. Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI. Eur. Radiol., 2020, vol. 30, no. 12, pp. 6582–6592.

Wang Z., Liu C., Cheng D., Wang L., Yang X., Cheng K. T. Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network. IEEE Trans. Med. Imaging, 2018, vol. 37, no. 5, pp. 1127–1139.

Abdelmaksoud I. R., Shalaby A., Mahmoud A., Elmogy M., Aboelfetouh A., Abou El-Ghar M., El-Melegy M., Alghamdi N. S., El-Baz A. Precise Identification of Prostate Cancer from DWI Using Transfer Learning. Sensors (Basel), 2021, vol. 21, no. 11, p. 3664.

Aldoj N., Lukas S., Dewey M., Penzkofer T. Semi-automatic classification of prostate cancer on multiparametric MR imaging using a multi-channel 3D convolutional neural network. Eur. Radiol., 2020,vol. 30, no. 2, pp. 1243–1253.

Song Y., Zhang Y. D., Yan X., Liu H., Zhou M., Hu B., Yang G. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J. Magn. Reson. Imaging,2018, vol. 48, no. 6, pp. 1570–1577.

Pellicer-Valero O. J., Marenco Jiménez J. L., Gonzalez-Perez V., Casanova Ramón-Borja J. L., MartínnGarcía I., Barrios Benito M., Pelechano Gómez P., Rubio-Briones J., Rupérez M. J., Martín-Guerrero J. D. Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images. Sci. Rep., 2022, vol. 12, no. 1, p. 2975.

Xu H., Baxter J. S. H., Akin O., Cantor-Rivera D. Prostate cancer detection using residual networks. Int.J. Comput. Assist. Radiol. Surg., 2019, vol. 14, no. 10, pp. 1647–1650.

Brunese L., Mercaldo F., Reginelli A., Santone A. Radiomics for Gleason Score Detection through Deep Learning. Sensors (Basel), 2020, vol. 20, no. 18, p. 5411.

Cao R., Mohammadian Bajgiran A., Afshari Mirak S., Shakeri S., Zhong X., Enzmann D., Raman S.,Sung K. Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet. IEEE Trans. Med. Imaging, 2019, vol. 38, no. 11, pp. 2496–2506.

Hou Y., Zhang Y. H., Bao J., Yang G., Shi H.-B., Song Y., Yu-Dong Zhang Y.-D. Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI:a two-center comparative study. Eur. J. Nucl. Med. Mol. Imaging, 2021, vol. 48, no. 12, pp. 3805–3816.

Zong W., Lee J. K., Liu C., Carver E. N., Feldman A. M., Janic B., Elshaikh M. A., Pantelic M. V., Hearshen D., Chetty I. J., Movsas B., Wen N. A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network. Med. Phys., 2019, vol. 47, no. 9,pp. 4077–4086.

Somov A. N., Suslin S. A. Prostate cancer. Epidemiology, risk factors and early detection. Profilakticheskaya meditsina, 2020, vol. 23, no. 3, pp. 149–155. (In Russian)

Schelb P., Kohl S., Radtke J. P., Wiesenfarth M., Kickingereder P., Bickelhaupt S., Kuder T. A., Stenzinger A., Hohenfellner M., Schlemmer H. P., Maier-Hein K. H., Bonekamp D. Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment. Radiology, 2019, vol. 293, no. 3,pp. 607–617.

Youn S. Y., Choi M. H., Kim D. H., Lee Y. J., Huisman H., Johnson E., Penzkofer T., Shabunin I., Winkel D. J., Xing P., Szolar D., Grimm R., von Busch H., Son Y., Lou B., Kamen A. Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learningbased algorithm (DLA) and radiologists with various levels of experience. Eur. J. Radiol., 2021, vol. 142,p. 109894.

Sanford T., Harmon S. A., Turkbey E. B., Kesani D., Tuncer S., Madariaga M., Yang C., Sackett J., Mehralivand S., Yan P., Xu S., Wood B. J., Merino M. J., Pinto P. A., Choyke P. L., Turkbey B. Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation:A Development Study. J. Magn. Reson. Imaging, 2020, vol. 52, no. 5, pp. 1499–1507.

Le M. H., Chen J., Wang L., Wang Z., Liu W., Cheng K. T., Yang X. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks. Phys. Med. Biol., 2017, vol. 62, no. 16, pp. 6497–6514.

Hao R., Namdar K., Liu L., Haider M. A., Khalvati F. A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks.J. Digit. Imaging, 2020, vol. 34, no. 4, pp. 862–876.

Castillo T. J. M., Arif M., Starmans M. P. A., Niessen W. J., Bangma C. H., Schoots I. G., Veenland J. F. Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers (Basel), 2021, vol. 14, no. 1, p. 12.

Bonekamp D., Kohl S., Wiesenfarth M., Schelb P., Radtke J. P., Götz M., Kickingereder P., Yaqubi K.,Hitthaler B., Gählert N., Kuder T. A., Deister F., Freitag M., Hohenfellner M., Hadaschik B. A., Schlemmer H. P., Maier-Hein K. H. Radiomic machine learning for characterization of prostate lesions with MRI: Comparison to ADC values. Radiology, 2018, vol. 289, no. 1, pp. 128–137.

Meyer H. J., Wienke A., Surov A. Discrimination between clinical significant and insignificant prostate cancer with apparent diffusion coefficient — a systematic review and meta analysis. BMC Cancer,2020, vol. 20, no. 1, p. 482.

Algohary A., Shiradkar R., Pahwa S., Purysko A., Verma S., Moses D., Shnier R., Haynes A. M., Delprado W., Thompson J., Tirumani S., Mahran A., Rastinehad A. R., Ponsky L., Stricker P. D., MadabhushiA. Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel), 2020, vol. 12, no. 8,p. 2200.

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Published

2023-09-29

How to Cite

Talyshinskii, A., Kamyshanskaya, I., Mischenko, A. ., Guliev, B. ., & Bakhtiozin , R. . (2023). Application of artificial intelligence in the detection and stratification of prostate cancer: Literature review. Vestnik of Saint Petersburg University. Medicine, 18(2), 150–166. https://doi.org/10.21638/spbu11.2023.204

Issue

Vol. 18 No. 2 (2023)

Section

Surgery

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Articles of "Vestnik of Saint Petersburg University. Medicine" are open access distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors unrestricted distribution and self-archiving free of charge.