Kemėšienė, Jūratė

Despite advances in diagnostic tools, a reliable and standardized algorithm for accurately identifying clinically significant prostate cancer in patients prior to their first prostate biopsy has yet to be established. Traditional methods such as prostate-specific antigen testing, digital rectal examination, and systematic biopsy often suffer from limited sensitivity and specificity, leading to an increased risk of both unnecessary biopsies and missed significant cases. The aim of this study was to evaluate and compare the diagnostic value of multiparametric magnetic resonance imaging (mpMRI), urinary genetic biomarkers (PCA3 and T:E), and the PCPTRC2 risk calculator in detecting clinically significant prostate cancer. The study included men who underwent all of the mentioned diagnostic tests followed by a prostate biopsy. Findings revealed that mpMRI demonstrated high sensitivity, but low specificity. Combining all three methods yielded the highest sensitivity but significantly reduced specificity, making this combination suitable only for high-risk patients. The most effective early diagnostic approach was the combination of mpMRI and urinary biomarkers, which reduced unnecessary biopsies while maintaining detection of clinically significant cases. The study highlights the importance of a personalized and combined diagnostic strategy for the detection of clinically significant prostate cancer.

Introduction & Objectives: Prostate cancer (PCa) remains a significant health concern with its high incidence and associated mortality. Conventional screening approaches, like PSA testing, often lack specificity, resulting in unnecessary biopsies and overtreatment. This study seeks to overcome these limitations by assessing the integration of novel urinary biomarkers into established risk prediction models. This study is aimed to evaluate the performance of incorporating urinary biomarkers – prostate cancer antigen 3 (PCA3) and transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) fusion genes (T:E) – into the Prostate Cancer Prevention Trial Risk Calculator version 2 (PCPTRC2) in a Lithuanian cohort to enhance the detection of clinically significant prostate cancer (csPCa). Materials & Methods: A single-centre prospective study included 246 men scheduled for initial prostate biopsy between January 2021 and August 2024 due to elevated total PSA levels or abnormal digital rectal examination (DRE). Following ethical approval and informed consent, urinary samples were collected post-DRE and analysed for PCA3 and T:E. Each patient's risk was calculated using the basic PCPTRC2 and updated versions incorporating biomarkers. Biopsies were performed based on multiparametric magnetic resonance imaging (mpMRI) findings. Results: Of 209 biopsy samples analysed, 111 (53.1%) were diagnosed with csPCa. The AUC for PCa detection was 59.6% for the original PCPTRC2, improving to 76.2% with PCA3 and further to 79.5% when both PCA3 and T:E were included. Both updated versions demonstrated significantly higher sensitivity compared to the original (p<0.001). However, no significant differences were noted in distinguishing csPCa from non-csPCa. Conclusions: Incorporating PCA3 and T:E into PCPTRC2 substantially enhances diagnostic accuracy for detecting PCa in biopsy-naïve patients. Despite limitations, these findings underscore the potential for optimizing risk calculators in clinical practice, advocating for larger cohorts to validate these results.

Introduction & Objectives: Prostate cancer (PCa) remains a significant health concern with its high incidence and associated mortality. Conventional screening approaches, like PSA testing, often lack specificity, resulting in unnecessary biopsies and overtreatment. This study seeks to overcome these limitations by assessing the integration of novel urinary biomarkers into established risk prediction models. This study is aimed to evaluate the performance of incorporating urinary biomarkers – prostate cancer antigen 3 (PCA3) and transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) fusion genes (T:E) – into the Prostate Cancer Prevention Trial Risk Calculator version 2 (PCPTRC2) in a Lithuanian cohort to enhance the detection of clinically significant prostate cancer (csPCa). Materials & Methods: A single-centre prospective study included 246 men scheduled for initial prostate biopsy between January 2021 and August 2024 due to elevated total PSA levels or abnormal digital rectal examination (DRE). Following ethical approval and informed consent, urinary samples were collected post-DRE and analysed for PCA3 and T:E. Each patient's risk was calculated using the basic PCPTRC2 and updated versions incorporating biomarkers. Biopsies were performed based on multiparametric magnetic resonance imaging (mpMRI) findings. Results: Of 209 biopsy samples analysed, 111 (53.1%) were diagnosed with csPCa. The AUC for PCa detection was 59.6% for the original PCPTRC2, improving to 76.2% with PCA3 and further to 79.5% when both PCA3 and T:E were included. Both updated versions demonstrated significantly higher sensitivity compared to the original (p<0.001). However, no significant differences were noted in distinguishing csPCa from non-csPCa. Conclusions: Incorporating PCA3 and T:E into PCPTRC2 substantially enhances diagnostic accuracy for detecting PCa in biopsy-naïve patients. Despite limitations, these findings underscore the potential for optimizing risk calculators in clinical practice, advocating for larger cohorts to validate these results.

Prostate cancer (PCa) remains a significant health concern due to its high incidence and associated mortality. Conventional screening approaches, like PSA testing, often lack specificity, resulting in unnecessary biopsies and overtreatment. This study seeks to overcome these limitations by assessing the integration of novel urinary biomarkers into established risk prediction models.

This study aimed to investigate the accuracy of multiparametric magnetic resonance imaging (mpMRI), genetic urinary test (GUT), and prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2) for the clinically significant prostate cancer (csPCa) diagnostic in biopsy-naïve patients.

Purpose: This study aimed to investigate the accuracy of magnetic resonance imaging (MRI), genetic urinary test (GUT) and prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2) for the clinically significant prostate cancer (csPCa) diagnostic in biopsynaïve patients. *Methods and Materials: In a single center study between 2021 and 2024 participants underwent prostate mpMRI, GUT and ultrasound (US) guided biopsy. The csPCa risk was calculated using PCPTRC2. Post digital rectal examination (DRE) GUT was performed measuring RNA levels of PCA3 and T:E fusion genes. The McNemar test compared detection rates between modalities. Results: 208 (mean age 62.9 years +/- 8.2) men were included prospectively. A positive GUT score was found in 67.8% and PIRADS ≥3 in 81.7% of all cases. The combination of GUT with mpMRI showed significantly higher sensitivity (99.1%) than GUT and mpMRI alone, 84.4% and 93.8%, respectively (p ≤ 0.05). Similarly, very high sensitivity (99.0%) was achieved by combining mpMRI with PCPTCR2. Nevertheless, mpMRI plus GUT combination exceeded mpMRI plus PCPTCR2 by allowing to save a higher fraction of unnecessary biopsies, 25% and 2,4%, respectively. Conclusions: GUT and mpMRI combination would allow saving a substantial fraction of unnecessary biopsies with minimal risk of missing csPCa cases. Clinical Relevance/Application: The combination of genetic urinary testing (GUT) and MRI enhanced the detection sensitivity of csPCa while substantially reducing unnecessary biopsies, thereby improving diagnostic efficiency and patient outcomes.

Introduction & Objectives: Magnetic resonance imaging (MRI), genetic urinary test (GUT), and prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2) may help decide the need for an invasive procedure in biopsy-naïve patients. This study aimed to investigate the accuracy of different techniques (MRI, GUT, PCPTRC2) for the clinically significant prostate cancer (csPCa) diagnostic. Materials & Methods: 208 patients were included prospectively. All subjects underwent prostate mpMRI, GUT and ultrasound (US) guided biopsy. The csPCa risk was calculated using PCPTRC2. Post digital rectal examination (DRE) GUT was performed measuring RNA levels of PCA3 and T:E fusion genes. Results: A positive GUT score was found in 67.8% and PIRADS 3 and more in 81.7% of all cases. The combination of GUT with mpMRI showed significantly higher sensitivity (99.1%) than GUT and mpMRI alone, 84.4% and 93.8%, respectively (p ≤ 0.05). Similarly, very high sensitivity (99,0%) was achieved by combining mpMRI with PCPTCR2. Nevertheless, mpMRI plus GUT combination exceeded mpMRI plus PCPTCR2 by allowing to save a higher fraction of unnecessary biopsies, 25% and 2,4%, respectively. Conclusions: GUT and mpMRI combination would allow saving a substantial fraction of unnecessary biopsies with minimal risk of missing csPCa cases.

Purpose Accurately diagnosing clinically significant prostate cancer (csPCa) remains challenging. Although prostate biopsy is considered the gold standard for prostate cancer (PCa) diagnosis, it is nevertheless an invasive procedure associated with various potential complications. Therefore, non-invasive diagnostic methods, such as the examination of genetic urine biomarkers, are becoming increasingly relevant in daily clinical practice. While current guidelines recommend pre-biopsy mpMRI in any case, its availability is usually limited. In contrast, urine tests have shown promising results in predicting high-grade PCa.

Urine has the potential to be used as a versatile body fluid for developing new biomarkers for detecting csPCa. Genetic urinary tests could be performed before (pre-DRE) or after (post-DRE) prostate massage. If both alternatives have similar diagnostic accuracy, the pre-DRE test may be preferred as it can be performed at home, avoiding the inconvenient DRE procedure. Otherwise, when selecting a test version, it is critical to consider the sensitivity and specificity of the test as they relate to the number of detected cases and saved unnecessary biopsies, respectively.

The most researched urinary PCa-specific biomarkers are prostate cancer gene 3 (PCA3) and TMPRSS2:ERG (T:E) fusion. PCA3 was the first genetic biomarker approved as a diagnostic tool for PCa diagnosis.3 Unlike PSA, PCA3 is a genetic biomarker exhibiting high prostate cancer specificity. Gene fusion transcripts of transmembrane protease serine 2 (TMPRSS2) and erythroblastosis virus E26 oncogene homolog (ERG), also named TMPRSS2:ERG or T:E, have been known as promising urinary biomarkers in prostate cancer. T:E gene fusion is the most frequent genetic alteration found in PCa cases. The fusion leads to an overexpression of the transcription factor ERG, which is found in both early-stage and late-stage PCa. Using a post-DRE urine it was shown that the combination of PCA3 and T:E biomarkers has more diagnostic value than only PCA3. However, there is a lack of larger-size studies comparing these biomarkers in pre-DRE and post-DRE urine sampled from the same cohort of patients.

The main objective of this study was to compare the diagnostic value of two PCa-specific biomarkers (PCA3 and T:E) in post-DRE versus pre-DRE urine for the diagnosis of csPCa, as well as to compare the levels of the detected biomarkers. [...].

Purpose: The diagnosis and management of prostate cancer are evolving every year. Digital rectal examination (DRE) and prostate specific antigen (PSA) screening are usually followed by subsequent DRE-directed or transrectal ultrasound (TRUS)-guided biopsy sampling. However, this technique allows to detect prostate cancer only 30% to 40% of cases. Men undergoing TRUS-guided biopsy are at risk of several complications, including lower urinary tract symptoms (up to 25% of cases), hematuria, rectal bleeding, hematospermia, infection, pain, urinary retention, erectile dysfunction and even mortality. Avoiding unnecessary biopsy is critical to protect men to undergo an uncomfortable and potentially risky invasive procedure and to avoid labeling them with a cancer diagnosis which is highly related to psychological stress.

Magnetic resonance imaging (MRI) use in daily prostate cancer detection is increasing and has proven to be a valuable tool in cancer diagnostics. Multiparametric MRI (MpMRI) in prostate cancer patients has demonstrated better diagnosis while performing targeted biopsy and decreased diagnosis of clinically insignificant prostate cancer.

Urine is a versatile body fluid for non-invasive urological malignancies detection. It is known, that prostate cancer biomarkers are likely to be found in urine sample because of the anatomical localization of the prostate next to the bladder apex and the proximal part of the urethra. The most investigated biomarkers are PCA3 and TMPRSS2:ERG fusion. PCA3 is a – a prostate specific long non-coding RNR overexpressed in 95% of prostate cancer cases. TMPRSS2:ERG is androgen regulated gene fusion which is believed to play a major role in prostate tumorogenesis.

Non-invasive methods, such as urinary biomarkers’ detection along with mpMRI could be more suitable for diagnostics of prostate cancer in biopsy-naïve patients. The objective of this study was to evaluate mpMRI, genetic urinary test (GUT) separately and in combination as an indicator of early significant prostate cancer (PCa). [...].

Synopsis: MR diffusion weighted imaging (DWI) may provide important information regarding the pathophysiology of parenchymal abdominal organs. However, diffusion measurements may also be affected by intravoxel incoherent fluid movements. A model taking into account these effects, called intravoxel incoherent motion (IVIM) . Another model - DKI - provides complementary information on the microstructural complexity of the tissue by evaluating the non-Gaussian behaviour of water motion. Our study began a few years ago in Zurich, when first we tried to compare different diffusion models and how various external factors are affecting them. First of all, we aimed to investigate whether diffusion markers derived from different diffusion models are sensitive to the hydration state, based on measurements in abdominal organs of healthy volunteers. The results of our study imply that the tissue hydration does not affect the robustness of the estimation of diffusion-related parameters in the kidneys. Although no change in diffusion values was found for the renal parenchyma, significant changes were observed in the liver, spleen and the skeletal muscle after the water challenge, which should be considered when diffusion values are compared between subjects with unknown hydration state. Subsequently the second step of our study is to find whether advanced diffusion imaging is a reliable tool for prostate cancer detection. Perfusion fraction is a heterogeneous quantity that varies widely in both normal and cancerous tissue, it is hypothesized that a voxel-wise analysis with sufficient signal-to-noise is required to determine its precise role in distinguishing prostate cancer. However, there are a few potential disadvantages of this advanced diffusion model used in clinical practice.