Introduction. Approaches to the diagnosis and staging of localized and locally advanced high-risk prostate cancer (PCa-НR) continue to be actively researched and improved. Materials and methods. In order to understand some of the controversial and controversial issues regarding the diagnosis of PCа-НR, a survey was conducted, in which 250 specialists took part – oncourologists, urologists, andrologists, specializing in the treatment / observation of patients with prostate cancer (PCа). The survey was conducted within the urological information portal Uroweb.ru by filling out a questionnaire. Results. The results obtained indicate that the most significant differences were obtained in views on the role of positron emission tomography combined with computed tomography (PET/CT) in the primary diagnosis of non-metastatic PCа and the importance of local prevalence in determining the risk of progression, while the attitude to genetic testing, primary local staging and prognosis criteria after radical prostatectomy in the majority respondents were similar. Conclusions. Most Russian oncourologists specialists involved in the treatment of PCа do not recommend that patients with PCа-HR perform PET/CT with prostate specific membrane antigen (68Ga-PSMA) and do not prescribe geneticist consultation and genetic counseling for non-metastatic PCа. To assess the local prevalence of the process in prostate cancer, based on the results of MRI, digital rectal examination and the percentage of tumor tissue in the biopsy sample. Most specialists determine the prognosis of a patient after RP by summing up the pathomorphological (PSA + radiological diagnostics + the result of histological examination) and clinical (PSA + radiological diagnostics + biopsy) indicators, which quite correlates with the global data.
Introduction. Artificial intelligence (AI) refers to computing technologies that simulate human intellectual processes. The use of AI in the near future will contribute to the widespread introduction of telemedicine technologies into practice. Materials and methods. The authors analyzed publications in PubMed and in the Electronic Scientific Library for the keywords oncology , urology , cancer urology , artificial intelligence . In PubMed, out of 127 articles that met the queries, 32 publications were selected, in the Electronic Scientific Library 3 articles were selected. Results. In kidney cancer, CT texture analysis with support vector method (SVM) can be considered promising; in order to predict the recurrence of bladder cancer, machine learning algorithms (support vector method) are used to identify the recurrence of bladder cancer by detecting urine micro-RNA. In order to reduce unnecessary biopsies based on clinical characteristics, an artificial neural network has been developed to predict the presence of prostate cancer. Conclusion. Artificial intelligence methods are constantly evolving, the range of their application in the field of oncourology is expanding. In the near future, we are not talking about replacing traditional methods, but in addition to them, artificial intelligence can provide more information about the patient. For the widespread introduction of these methods, mechanisms for overseeing the safety and efficiency of artificial intelligence algorithms should be developed. More research is needed to clinically and statistically compare the results obtained with AI with those obtained using traditional methods.
Objective: to assess safety, pathological response rate, and long-term oncologic outcomes of radical prostatectomy (RP) after neoadjuvant chemotherapy using docetaxel in prostate cancer (PCa) patients of high and very high risk groups.
Materials and methods: 86 patients with high and very high risk PCa (PSA>20 ng/ml, Gleason score 8 and more, or clinical stage cT2c and more) were included, among them 46 received neoadjuvant (NCGT/RP group) treatment followed by RP and 40 patients received RP only. with a median follow-up of 11.4 years after RP. Neoadjuvant treatment included 3-weekly docetaxel (75 mg/m2 for up to 6 cycles) with concomitant degarelix (6 monthly injections). Results: NCGT cycle was started in 39 patients and completed in full dose and planned regimen in 34 (87.2%) patients. Toxicities were moderate. A statistically significant reduction of PSA>50% post-chemohormonal therapy was observed in all 39 cases. Among patients with completed neoadjuvant treatment RP was performed in 33 (97.1%) patients. Lower postoperative stage was noticed in 38.5% in NCGT/RP group compared with 2.7% in RP group. Similarly, positive surgical margin rate was higher in group without neoadjuvant therapy - 43.2% and 25.6% (RP group). Adjuvant or deferred treatment received 25 (67.6%) and 13 (39.4%) in RP and NCGT/RP group, respectively.
Conclusion: The use of neoadjuvant chemohormonal therapy before the RP in selected regimen and dose represents a safe strategy resulting in benefit in early oncological results. Given the limitations of the study this concept should be evaluated in large prospective controlled studies.
Introduction. This article reviews an application of highly technological methods of virtual reality (VR) in clinical practice based on various studies and experiments of foreign and Russian researchers in recent years. The aim of this review is to demonstrate application of virtual reality technologies for further transformation of classical medicine into digital one. Materials and methods. There is significant growth of interest in the use of VR in medicine. Particularly, only in PubMed library such dynamics can be traced by using key words «VR technology in medicine»: in 2017 year there were 58 articles, in 2018 – 65, in 2019 – 106, in 2020 – 127, and currently in the first half of 2021 year there are already 145 articles. For this paper 37 articles from international journals and 28 from Russian ones were selected. The accent was made on the usage of VR technologies in different fields of clinical medicine, education of medical staff and patients. Results. In this paper we described wide range of experiments on using VR technologies during various medical manipulations such as diagnosis, planning of surgical interventions, cognitive therapy, pain management, preventing medicine and conservative treatment. Examples of successful clinical management of patients during rehabilitation and health maintaining were shown. Medical fields where VR is currently widely used were chosen, promising directions for further research were indicated. We also described opportunities of VR application for teaching medical staff. Conclusion. Nearly all researchers who applied virtual reality (VR) in clinical practice have come to similar conclusion. This innovative tool is a breakthrough in medicine and it has high potential for using it by physicians, patients and health care organizers. Authors have articulated issues which should be managed for further successful introduction VR technologies into modern clinical practice.
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