The incidence of prostate cancer (PC) has risen annually. PC mortality is explained by the metastatic disease (mPC). There is an intermediate scenario in which patients have non-mPC but have initiated a metastatic cascade through epithelial–mesenchymal transition. There is indeed a need for more and better tools to predict which patients will progress in the future to non-localized clinical disease or already have micrometastatic disease and, therefore, will clinically progress after primary treatment. Biomarkers for the prediction of mPC are still under development; there are few studies and not much evidence of their usefulness. This review is focused on tissue-based genomic biomarkers (TBGB) for the prediction of metastatic disease. We develop four main research questions that we attempt to answer according to the current evidence. Why is it important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are the current prostate cancer treatments? The importance of predicting metastasis is fundamental given that, once metastasis is diagnosed, quality of life (QoL) and survival drop dramatically. There is still a need and space for more cost-effective TBGB tests that predict mPC disease.

Background: Prostate cancer (PCa) represents one of the most frequent malignancies and the fifth leading cause of cancer death in adult men worldwide. PCa mortality rates have been declining in several Western countries; one of the possible reasons may be related to the application of prostate-specific antigen early detection policies. These early detection protocols increase PCa-specific patient survival; however, a high percentage of these cases corresponds to low-risk PCa that grows very slowly and is unlikely to metastasize to threaten survival. Many low-risk PCa patients receive aggressive therapies, such as radical prostatectomy and radiotherapy, that are costly for patients and/or health systems and generate side effects that affect the quality of life. An alternative to surgery and radiotherapy treatments for low-risk PCa is active surveillance (AS), a strategy based on close disease monitoring and intervention only if the disease progresses. However, proper identification of low-risk PCa patients at the time of diagnosis is essential for the effectiveness AS. The selection of AS candidates remains challenging; thus, effective prognostic biomarkers are needed. Summary: This review article addresses the characteristics of the current and emerging PCa prognostic biomarkers, including tests available for tissue, blood, and urine analyses, for the appropriate selection of PCa patients for AS. In addition, and based on published literature, we performed a selection of potential new biomarkers that can distinguish low-risk PCa. Key Messages: The literature search yielded four tissue-based tests, two blood-based tests, and six urine-based tests that can be used to determine PCa risk classification. However, most available tests are expensive; thus, cost-effective analyses are needed in order to obtain the approval of government agencies and to be financed by the health systems. Available prognostic urine tests have shown great progress over the last years, and they have the advantage of being minimally invasive; therefore, they may become a routine disease progression test for patients under AS. In addition, new research conducted in the last decade has shown promising biomarkers, including mRNA, miRNA, long noncoding RNA, and metabolites, that could improve existing tests or allow the development of new tools for AS patient selection.

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL²) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI–ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics’ (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.

Tradicionalmente, se describe que 4-10% presenta compromiso de la vena cava (VC). En 1987 Neves & Zincke² describieron la primera clasificación de este tipo de tumores (conocida como la clasificación de la clínica Mayo), de acuerdo a la altura del compromiso de la VC: 0- vena renal, I- hasta 2 cm sobre la renal, II- 2 cm bajo la vena hepática, III- VC intrahepática y tipo IV- sobre el diafragma. Sólo 1% de estos tumores son tipo IV con compromiso atrial³,⁴.

Background: The incidence of prostate cancer (PC) has been risen annually. Despite the diagnosis is made mainly with non-metastatic PC, mortality is explained by the metastatic disease (mPC). Without a doubt there is an intermediate scenario in which patients have no mPC but will have initiated a metastatic cascade through an epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict what patients will progress in the future to non-localized clinical disease or already have micrometastatic disease, and therefore, will clinically progress after primary treatment. Biomarkers for predicting mPC are still under development; there are few studies and not much evidence of their usefulness. Summary: This review is focused on tissue-based genomic biomarkers (TBGB) for predicting metastatic disease. We developed four main research questions that will attempt to answer according to the current evidence. Why is important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are current prostate cancer treatments? Key Messages: Knowing useful predict tools could help determine which patients may need multimodal or adjuvant treatment even with a localized disease, and in consequence, what patients do not need more than a single modality of treatment. The importance of predicting metastasis is fundamental, given that once metastasis is diagnosed, the quality of life (QoL) and survival drop dramatically.