FROM DIAGNOSIS TO ADVANCED DISEASE: A COMPREHENSIVE REVIEW OF CONTEMPORARY MANAGEMENT OF PROSTATE CANCER, INCLUDING SURGICAL TECHNIQUES, SYSTEMIC THERAPIES, AND MODERN TECHNOLOGIES
DOI:
https://doi.org/10.31435/ijitss.1(49).2026.5017Keywords:
Prostate Cancer, Robotic-Assisted Radical Prostatectomy (RARP), Laparoscopic Radical Prostatectomy (LRP), Artificial Intelligence, Machine Learning, Biochemical Recurrence, Advanced Prostate CancerAbstract
Background: Prostate cancer management has profoundly evolved from generalized protocols to a personalized, technology-driven paradigm. The integration of robotic surgery, artificial intelligence (AI), and novel systemic therapies necessitates a synthesis of the current state of care.
Aim: To synthesize evidence on contemporary prostate cancer management, focusing on the comparative effectiveness of surgical techniques, the impact of surgeon experience, and the role of emerging technologies like AI and novel systemic therapies.
Material and methods: A structured analysis of 27 provided scientific documents (systematic reviews, trials, guidelines) was performed. Key data on diagnostic, functional, and oncological outcomes were extracted and synthesized.
Results: AI-powered analysis of mpMRI achieves high accuracy (AUROC >0.90) in detecting significant disease. Robotic radical prostatectomy (RARP) is superior to laparoscopy (LRP) with fewer complications and better outcomes for continence (RR 0.43), erectile function (RR 1.38), and biochemical recurrence (RR 0.59). Surgeon experience is a key factor (250-case learning curve). For mHSPC, adding ARPIs to ADT improves survival (HR for mortality ~0.63). In mCRPC, PARP inhibitors and 177Lu-PSMA-617 offer survival benefits in selected patients.
Conclusions: Contemporary prostate cancer care emphasizes precision. RARP is the surgical standard for localized disease, but outcomes depend heavily on surgeon volume. AI shows promise but requires extensive validation for clinical adoption. Early combination therapies are standard in advanced disease. A multidisciplinary, evidence-based approach is required to integrate these modalities and optimize patient outcomes.
References
Moglia, A., Georgiou, K., Georgiou, E., Satava, R. M., & Cuschieri, A. (2021). A systematic review on artificial intelligence in robot-assisted surgery. International Journal of Surgery, 95, 106151. https://doi.org/10.1016/j.ijsu.2021.106151
Turco, F., Armstrong, A., Attard, G., et al. (2022). What experts think about prostate cancer management during the COVID-19 pandemic: Report from the Advanced Prostate Cancer Consensus Conference 2021. European Urology, 82(1), 6–11. https://doi.org/10.1016/j.eururo.2022.02.010
Ma, J., Xu, W., Chen, R., et al. (2023). Robotic-assisted versus laparoscopic radical prostatectomy for prostate cancer: The first separate systematic review and meta-analysis of randomised controlled trials and non-randomised studies. International Journal of Surgery, 109(5), 1350–1359. https://doi.org/10.1097/JS9.0000000000000193
Cornford, P., Tilki, D., van den Bergh, R. C. N., et al. (2025). EAU - EANM - ESTRO - ESUR - ISUP - SIOG guidelines on prostate cancer. European Association of Urology. Available from EAU Guidelines on Prostate Cancer - Uroweb (accessed January 13, 2026).
Vickers, A. J., Bianco, F. J., Serio, A. M., et al. (2007). The surgical learning curve for prostate cancer control after radical prostatectomy. Journal of the National Cancer Institute, 99(15), 1171–1177. https://doi.org/10.1093/jnci/djm060
Cao, L., Yang, Z., Qi, L., & Chen, M. (2019). Robot-assisted and laparoscopic vs open radical prostatectomy in clinically localized prostate cancer: Perioperative, functional, and oncological outcomes: A systematic review and meta-analysis. Medicine, 98(22), e15770. https://doi.org/10.1097/MD.0000000000015770
Kaushik, D., Boorjian, S. A., Thompson, R. H., et al. (2016). Oncological outcomes following radical prostatectomy for patients with pT4 prostate cancer. International Brazilian Journal of Urology, 42(6), 1091–1098. https://doi.org/10.1590/S1677-5538.IBJU.2016.0290
Liu, J., Zhang, H., Woon, D. T. S., Perera, M., & Lawrentschuk, N. (2024). Predicting biochemical recurrence of prostate cancer post-prostatectomy using artificial intelligence: A systematic review. Cancers, 16(21), 3596. https://doi.org/10.3390/cancers16213596
Thenault, R., Kaulanjan, K., Darde, T., et al. (2020). The application of artificial intelligence in prostate cancer management-what improvements can be expected? A systematic review. Applied Sciences, 10(18), 6428. https://doi.org/10.3390/app10186428
Taaffe, D. R., Newton, R. U., Spry, N., et al. (2017). Effects of different exercise modalities on fatigue in prostate cancer patients undergoing androgen deprivation therapy: A year-long randomised controlled trial. European Urology, 72(2), 293–299. https://doi.org/10.1016/j.eururo.2017.02.019
Hong, M. K. H., Yao, H. H. I., Rzetelski-West, K., et al. (2012). Prostate weight is the preferred measure of prostate size in radical prostatectomy cohorts. BJU International, 109(Suppl. 3), 57–63. https://doi.org/10.1111/j.1464-410X.2012.11049.x
Alqahtani, S. (2024). Systematic review of AI-assisted MRI in prostate cancer diagnosis: Enhancing accuracy through second opinion tools. Diagnostics, 14(22), 2576. https://doi.org/10.3390/diagnostics14222576
Frewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Della Corte, D. (2024). Don’t fear the artificial intelligence: A systematic review of machine learning for prostate cancer detection in pathology. Archives of Pathology & Laboratory Medicine, 148(5), 603–612. https://doi.org/10.5858/arpa.2022-0460-RA
Rodler, S., Kidess, M. A., Westhofen, T., et al. (2023). A systematic review of new imaging technologies for robotic prostatectomy: From molecular imaging to augmented reality. Journal of Clinical Medicine, 12(16), 5425. https://doi.org/10.3390/jcm12165425
Lee, S. H., Seo, H. J., Lee, N. R., Son, S. K., Kim, D. K., & Rha, K. H. (2017). Robot-assisted radical prostatectomy has lower biochemical recurrence than laparoscopic radical prostatectomy: Systematic review and meta-analysis. Investigative and Clinical Urology, 58(3), 152–163. https://doi.org/10.4111/icu.2017.58.3.152
Liu, Y., Deng, X. Z., Qin, J., et al. (2023). Erectile function, urinary continence and oncologic outcomes of neurovascular bundle sparing robot-assisted radical prostatectomy for high-risk prostate cancer: A systematic review and meta-analysis. Frontiers in Oncology, 13, 1161544. https://doi.org/10.3389/fonc.2023.1161544
Hu, J. C., Gu, X., Lipsitz, S. R., et al. (2009). Comparative effectiveness of minimally invasive vs open radical prostatectomy. JAMA, 302(14), 1557–1564. https://doi.org/10.1001/jama.2009.1451
Zdrojowy, R., Dembowski, J., Małkiewicz, B., Tupikowski, K., & Krajewski, W. (2016). Salvage local therapy for radiation-recurrent prostate cancer - where are we? Central European Journal of Urology, 69(3), 264–270. https://doi.org/10.5173/ceju.2016.832
Rajwa, P., Pradere, B., Gandaglia, G., et al. (2022). Intensification of systemic therapy in addition to definitive local treatment in nonmetastatic unfavourable prostate cancer: A systematic review and meta-analysis. European Urology, 82(1), 82–96. https://doi.org/10.1016/j.eururo.2022.03.031
Gillessen, S., Omlin, A., Attard, G., et al. (2022). Management of patients with advanced prostate cancer: Report from the Advanced Prostate Cancer Consensus Conference 2021. European Urology, 82(1), 115–141. https://doi.org/10.1016/j.eururo.2022.04.002
Peltier, A., Aoun, F., Lemort, M., et al. (2015). MRI-targeted biopsies versus systematic transrectal ultrasound guided biopsies for the diagnosis of localized prostate cancer in biopsy naïve men. BioMed Research International, 2015, 571708. https://doi.org/10.1155/2015/571708
Wilt, T. J., Jones, K. M., Barry, M. J., et al. (2017). Follow-up of prostatectomy versus observation for early prostate cancer. New England Journal of Medicine, 377(2), 132–142. https://doi.org/10.1056/NEJMoa1615869
Yu, E. Y., Kolinsky, M. P., Berry, W. R., et al. (2022). Pembrolizumab plus docetaxel and prednisone in patients with metastatic castration-resistant prostate cancer: Long-term results from the Phase 1b/2 KEYNOTE-365 Cohort B study. European Urology, 82(1), 22–30. https://doi.org/10.1016/j.eururo.2022.02.023
Zhang, Z. F., Li, S. B., & Ji, Y. (2025). Robotic vs laparoscopic radical prostatectomy: The impact of surgeon volume on surgical margins and quality of life. American Journal of Cancer Research, 15(12), 5374–5385. https://doi.org/10.62347/CIJG3615
Eklund, M., Jäderling, F., Discacciati, A., et al. (2021). MRI-targeted or standard biopsy in prostate cancer screening. New England Journal of Medicine, 385(10), 908–920. https://doi.org/10.1056/NEJMoa2100852
Siddiqui, M. M., Rais-Bahrami, S., Turkbey, B., et al. (2015). Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA, 313(4), 390–397. https://doi.org/10.1001/jama.2014.17942
Menard, J., Durlach, A., Barbe, C., et al. (2011). Endothelin-1: A predictor of extracapsular extension in clinically localized prostate cancer? BJU International, 108(8 Pt 2), E104–E109. https://doi.org/10.1111/j.1464-410X.2010.09879.x
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Michał Pietrasz, Patryk Iglewski, Klaudia Brzoza, Filip Matusiak, Michał Kociński, Anna Komarczewska

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.

