UDC 616.65-006.6-089-07:616.633-07

At present, the identification of high-risk groups of localized prostate cancer (PCa) is highly relevant. Our previous research demonstrated that prostate cancer antigen 3 (PCA3) scores depend on the tumor zone of origin (TZO) and the tumor growth dominant pattern (TGDP).
The aim: to assess the prognostic value of PCA3 score for identifying postoperative 4–5 grade group according to the International Society of Urological Pathology 2014 (ISUP) classification in patients with localized peripheral zone prostate cancer with posterior TGDP (pPZ-PCa).
Materials and methods. PCA3 scores and correlations were assessed and compared in different PCa patient groups and subgroups based on TZO, TGDP, and ISUP grade. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic significance of the model and determine the optimal PCA3 score cutoff for identifying ISUP 4–5.
Results. The PCA3 scores showed a significant (p<0.01) positive correlation (r=0.71) with ISUP grade in pPZPCa. PCA3 scores differed significantly (p<0.01) between ISUP 1–3 and 4–5 pPZ-PCa subgroups. ROC analysis demonstrated excellent performance with an AUC of 0.98 (95% CI: 0.95–0.99) for identifying ISUP 4–5 pPZ-PCa.
Conclusions. PCA3 scores demonstrated prognostic value for identifying postoperative ISUP 4–5 in pPZ-PCa.

Urinary bladder cancer (UBC) is a prevalent malignancy worldwide, exhibiting high recurrence rates and significant morbidity and mortality. Traditional diagnostic and prognostic methods often fall short in providing the precision required for effective patient stratification and personalized treatment. Genomic and transcriptomic studies have revolutionized our understanding of UBC by unveiling molecular alterations that drive tumor initiation, progression, and therapeutic response. This systematic review explores the role and application of genomic and transcriptomic analyses in the diagnostics and survival prediction of non-invasive and invasive UBC. We conducted a comprehensive literature search in MEDLINE, Web of Science, and Scopus up to October 2023, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search yielded 1,256 records (412 in MEDLINE, 378 in Web of Science, and 466 in Scopus), and 356 duplicates were removed. Our findings highlight key mechanisms of action, including mutations in FGFR3, TP53, and RB1 genes, and alterations in pathways such as PI3K/AKT/mTOR and MAPK/ERK, which are pivotal in UBC pathogenesis. Recent research advances, including liquid biopsies and single-cell sequencing, offer promising non-invasive diagnostic tools and deeper insights into tumor heterogeneity. This review underscores the critical importance of integrating genomic and transcriptomic data into clinical practice to improve diagnostics, prognostic assessments, and personalized treatment strategies for UBC patients. Future research should focus on integrating multi-omics data and validating molecular biomarkers in large clinical trials to further enhance patient outcomes.

Background: Prostate cancer is a leading cause of cancer-related morbidity and mortality among men worldwide. Fusion biopsy, combining magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS) guidance, has enhanced the detection of clinically significant prostate cancer. However, challenges such as inter-operator variability and accurate lesion targeting persist. Artificial intelligence (AI) and machine learning (ML) offer potential improvements in diagnostic accuracy and efficiency. Objective: To systematically review the role and perspectives of AI and ML in improving the efficiency of fusion biopsy in men with prostate cancer. Materials and Methods: Following PRISMA guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus up to October 2023. Studies assessing the application of AI and ML in fusion biopsy for prostate cancer were included. Results: A total of 1,236 records were identified (MEDLINE: 432; Web of Science: 398; Scopus: 406), with 312 duplicates removed. Titles and abstracts of 924 articles were screened, and 68 qualified for full-text eligibility assessment. Twenty-seven articles met the inclusion criteria and were qualitatively synthesized. Conclusion: AI and ML hold promise in improving the efficiency and accuracy of fusion biopsies in prostate cancer. Large-scale, prospective studies and standardized protocols are necessary to validate these technologies and facilitate their integration into clinical practice.

Purpose: This study aimed to assess the diagnostic performance of multiphase contrast-enhanced computed tomography (MCECT) in differentiating benign and malignant solid and cystic small renal masses (SRMs), predicting histologic subtypes, and grading, using signal intensity (SI) and tumour-to-cortex signal intensity (TCSI) ratio. Material and methods: A retrospective analysis was conducted on 181 patients with solid and cystic SRMs (≤ 4 cm).
MCECT imaging across 4 phases (non-contrast, corticomedullary, nephrographic, and excretory) was performed. SI and TCSI values were measured, and their diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Solid, Bosniak IIF, III, and IV SRMs underwent histopathological confirmation.
Results: Among solid SRMs, excretory phase SI achieved an area under the curve (AUC) of 0.848 for differentiating RCC from other SRMs, with 100% sensitivity and 61.3% specificity. For distinguishing renal cell carcinoma (RCC) from benign SRMs, the most effective parameter was the TCSI ratio obtained from computed tomography excretory phase (88.6% sensitivity, 52.4% specificity, 0.763 AUC). For Bosniak IIF cysts, the corticomedullary phase SI provided an AUC of 0.902, with 93% sensitivity and 87.5% specificity. RCC subtyping showed distinct SI characteristics across phases, particularly for clear cell RCC. Nephrographic phase SI differentiated low- versus high-grade RCC, with an AUC of 0.901, 90.2% sensitivity, and 86.4% specificity.
Conclusions: MCECT-derived imaging biomarkers, particularly SI and TCSI, are effective non-invasive tools for characterising SRMs, aiding in the differentiation of benign and malignant lesions, histological subtypes, and tumour grades. Their integration with advanced radiomics could further enhance diagnostic accuracy.
Key words: renal cell carcinoma, small renal masses, radiomics, imaging marker, tumour-to-cortex signal intensity ratio, multiphase contrast-enhanced CT.

УДК 016:615.1(092)

У ювілейному виданні висвітлено основні віхи життя, наукової, науковопедагогічної та організаційної діяльності відомого українського науковця в галузі фармацевтичної, органічної і біоорганічної хімії, ректора Львівського
національного медичного університету імені Данила Галицького (1998–2023 рр.), академіка Національної Академії медичних наук України, доктора фармацевтичних наук, професора, заслуженого працівника Вищої школи України, лавреата Державної премії України в галузі науки і техніки, заслуженого професора ЛНМУ ім. Данила Галицького, почесного доктора Люблінського медичного університету, дійсного члена НТШ та низки
громадських академій.
Хронологічний покажчик друкованих праць, авторських свідоцтв та патентів знайомить з науковим доробком ученого.
Це видання стане важливим корисним ресурсом для науково-педагогічної спільноти, аспірантів, студентів, а також істориків, бібліографів та всіх, хто цікавиться історією львівської медичної і фармацевтичної науки та освіти.