Prostate cancer

Background. The role of autophagy markers in prostate tumor recurrence has not been sufficiently investigated. We hypothesized that autophagy activation may be one mechanism by which prostate cancer cells survive exposure to high-intensity focused ultrasound (HIFU).Aim. To compare tissue expression of autophagic LC3B marker in prostate biopsies before and after treatment of localized prostate cancer by HIFU ablation.Materials and methods. 45 patients with localized morphologically confirmed prostate cancer were examined: group 1 – 25 patients of 65.6 ± 8.4 years without signs of recurrence or progression of the disease; group 2 – 20 patients of 67.5 ± 7.9 years with tumor recurrence proven during morphological examination. Immunohistochemical examination was performed by streptavidin-biotin method. In all cases, Anti-LC3B antibody ab48394 was used. The reaction results were quantified using the Histochemical score (Hs) system.Results. Prior to treatment, all patients of group 1 showed moderate cytoplasmic expression (Hs = 111 [111; 115]) of antibodies against LC3B in prostate adenocarcinoma cells, 5 % of patients – weak cytoplasmic expression in muscle connective stromal cells (Hs = 47 [43; 50]), 10 % of patients – weak positive LC3B reaction in the vessel wall (Hs = 28 [20; 35]). After treatment, the expression of LC3B in adenocarcinoma cells became negative, in the cytoplasm of muscle connective stromal cells weak (Hs = 75 [67.5; 80.0]), in the endothelium of the vascular wall even weaker (Hs = 55 [45.5; 60.0]) (p <0.001). Prior to treatment in group 2, LC3B expression in tumor tissue was moderate in 89 % of patients (Hs = 151.5 [137.5; 160.0]), weak in muscle connective stromal cells in 12 % of patients (Hs = 44 [35; 51.5]), and weak in the vascular wall in 5 % of patients (Hs = 30 [25; 35]). After treatment, LC3B expression in adenocarcinoma cells became pronounced (Hs = 260 [250; 285]), in muscle connective stromal cells – moderate (Hs = 118 [100; 130]), in the vascular wall – weak (Hs = 45 [30; 55]) (p <0.001). There was a significant correlation between tumor recurrence and LC3B overexpression (r = 0.51; p <0.001).Conclusion. The development of prostate cancer recurrence is associated with increased expression of autophagic LC3B protein. Increased LC3B expression, which is interpreted as evidence of autophagy activation and correlates with the risk of disease progression, is used by the tumor as an oncogenic advantage.

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Tissue expression of LC3B autophagy marker as a potential biomarker of prostate cancer recurrence after treatment with high-intensity focused ultrasound (pilot study)

Воронина

Фомкин

Bucharskaya

et al. 2023

Onkourologiâ

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Multiparametric magnetic resonance imaging in diagnosis of local prostate cancer recurrence after radiation therapy and radical prostatectomy: a unified PI-RR reporting system

Rubtsova,

Alekseev,

Kaprin

et al. 2024

Onkourologiâ

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Multiparametric magnetic resonance imaging (mpMRI) has an indisputable advantage in diagnosis of local recurrences of prostate cancer (PCa). Even though mpMRI has been shown to be very informative for detection of local PCa recurrences, high variability in its application and interpretation remains. Experts from the European Society for Urogenital Radiology (ESUR), the Imaging Committee of the European Association of Urology (ESUI), and several members of the PI-RADS committee developed a unified report system called Prostate Imaging Recurrence Reporting (PI-RR) to measure the risk of local recurrence of PCa in men who have had radical prostatectomy and radiation therapy and are being followed up. The principles of mpMRI that form the basis of PI-RR are well known and are stated in the PI-RADS v.2.1 guidelines, which have proven their diagnostic efficiency. The PI-RR system has the potential to become an important tool for improving communication between specialists involved in the process of PCa diagnosis and treatment, for optimizing treatment strategy in patients with local PCa recurrence, and for improving survival rates in patients with PCa after specialized anticancer treatment.

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

Aboyan,

Redkin,

Nazaruk

et al. 2024

Onkourologiâ

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Aim. To improve the diagnosis of prostate cancer by training a neural network to identify malignant tumor lesions using the results of magnetic resonance imaging (MRI) studies with the same or greater accuracy than an experienced radiologist, using as the truth histological mapping of slides performed by a morphologist.Materials and methods. The work was performed at the “Zdorovie” Clinical and Diagnostic Center in Rostov-on-Don. Patients selected for the study underwent MRI in the Philips Ingenia 3.0T machine according to the prostate multiparametric MRI protocol, which complies with the requirements of PI-RADS v.2.1. The obtained data was used to train a convolutional neural network based on the U-Net architecture. The correct map of the actual locations of prostate cancer lesions was obtained using the “Morphologist’s digital mapping tool” software.Results. The research part of the work consisted of following stages:development of the “Morphologist’s digital mapping tool” software for virtualization of lesions;analysis of MRI data archive, retrospective selection of patients;mapping of data by a morphologist to identify lesions in the prostate with layer-by-layer transfer of visualized lesions in the histological preparation to the image of the prostate gland in the “Morphologist’s digital mapping tool”, as well as training of the neural network to identify the presence of a malignant neoplasm in the prostate, location of the lesion(s), clinically significant disease;data validationFor a certain amount of input data and high-quality mapping of this data, the neural network is capable of detecting prostate cancer lesions with the same accuracy as an experienced radiologist. Validation showed that the neural network correctly localized prostate cancer in 78 % of cases, while the radiologist did so in 55 % of cases. Comparative analysis also revealed the ability of the neural network to detect prostate cancer in areas of the prostate where the radiologist could not recognize any visual patterns indicating the presence of prostate cancer.Conclusion. Training a neural network without the participation of a radiologist is a fundamentally new approach that allows to sidestep the experience and qualifications of a radiologist in interpreting the obtained multiparametric MRI images.

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Prostate cancer

Cited by 5 publications

References 360 publications

Tissue expression of LC3B autophagy marker as a potential biomarker of prostate cancer recurrence after treatment with high-intensity focused ultrasound (pilot study)

Tissue expression of LC3B autophagy marker as a potential biomarker of prostate cancer recurrence after treatment with high-intensity focused ultrasound (pilot study)

Multiparametric magnetic resonance imaging in diagnosis of local prostate cancer recurrence after radiation therapy and radical prostatectomy: a unified PI-RR reporting system

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

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