Incorporating Magnetic Resonance Imaging and Biomarkers in Active Surveillance Protocols - Results From the Prospective Stockholm3 Active Surveillance Trial (STHLM3AS)

Outcomes of active surveillance for Japanese patients with prostate cancer (PRIAS-JAPAN)

OBJECTIVE

To report the outcomes of repeat biopsies, metastasis and survival in the Prostate Cancer Research International: Active Surveillance (PRIAS)-JAPAN study, a prospective observational study for Japanese patients, initiated in 2010.

PATIENTS AND METHODS

At the beginning, inclusion criteria were initially low-risk patients, prostate-specific antigen (PSA) density (PSAD) <0.2, and ≤2 positive biopsy cores. As from 2014, GS3+4 has also been allowed for patients aged 70 years and over. Since January 2021, the age limit for Gleason score (GS) 3 + 4 cases was removed, and eligibility criteria were expanded to PSA ≤20 ng/mL, PSAD <0.25 nd/mL/cc, unlimited number of positive GS 3 + 3 cores, and positive results for fewer than half of the total number of cores for GS 3 + 4 cases if magnetic resonance imaging fusion biopsy was performed at study enrolment or subsequent follow-up. For patients eligible for active surveillance, PSA tests were performed every 3 months, rectal examination every 6 months, and biopsies at 1, 4, 7 and 10 years, followed by every 5 years thereafter. Patients with confirmed pathological reclassification were recommended for secondary treatments.

RESULTS

As of February 2024, 1302 patients were enrolled in AS; 1274 (98%) met the eligibility criteria. The median (interquartile range) age, PSA level, PSAD, and number of positive cores were 69 (64-73) years, 5.3 (4.5-6.6) ng/mL, 0.15 (0.12-0.17) ng/mL, and 1 (1-2), respectively. The clinical stage was T1c in 1089 patients (86%) and T2 in 185 (15%). The rates of acceptance by patients for the first, second, third and fourth re-biopsies were 83%, 64%, 41% and 22%, respectively. The pathological reclassification rates for the first, second, third and fourth re-biopsies were 29%, 30%, 35% and 25%, respectively. The 1-, 5- and 10-year persistence rates were 77%, 45% and 23%, respectively. Six patients developed metastasis, and one patient died from prostate cancer.

CONCLUSION

Pathological reclassification was observed in approximately 30% of the patients during biopsy; however, biopsy acceptance rates decreased over time. Although metastasis occurred in six patients, only one death from prostate cancer was recorded.

Navigating the evolving diagnostic and therapeutic landscape of low- and intermediate-risk prostate cancer

ABSTRACT

In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients' treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure.

A Prostate-Specific Membrane Antigen PET-Based Approach for Improved Diagnosis of Prostate Cancer in Gleason Grade Group 1: A Multicenter Retrospective Study

The preoperative Gleason grade group (GG) from transrectal ultrasound-guided prostate biopsy is crucial for treatment decisions but may underestimate the postoperative GG and miss clinically significant prostate cancer (csPCa), particularly in patients with biopsy GG1. In such patients, an SUVmax of at least 12 has 100% specificity for detecting csPCa. In patients with an SUVmax of less than 12, we aimed to develop a model to improve the diagnostic accuracy of csPCa. Methods: The study retrospectively included 56 prostate cancer patients with transrectal ultrasound-guided biopsy GG1 and an SUVmax of less than 12 from 2 tertiary hospitals. All [68Ga]Ga-PSMA-HBED-CC PET scans were centrally reviewed in Xijing Hospital. A deep learning model was used to evaluate the overlap of SUVmax (size scale, 3 cm) and the level of Gleason pattern (size scale, 500 μm). A diagnostic model was developed using the PRIMARY score and SUVmax, and its discriminative performance and clinical utility were compared with other methods. The 5-fold cross-validation (repeated 1,000 times) was used for internal validation. Results: In patients with GG1 and an SUVmax of less than 12, significant prostate-specific membrane antigen (PSMA) histochemical score (H-score) H-score overlap occurred among benign gland, Gleason pattern 3, and Gleason pattern 4 lesions, causing SUVmax overlap between csPCa and non-csPCa. The model of 10 × PRIMARY score + 2 × SUVmax exhibited a higher area under the curve (AUC, 0.8359; 95% CI, 0.7233-0.9484) than that found using only the SUVmax (AUC, 0.7353; P = 0.048) or PRIMARY score (AUC, 0.7257; P = 0.009) for the cohort and a higher AUC (0.8364; 95% CI, 0.7114-0.9614) than that found using only the Prostate Imaging Reporting and Data System (PI-RADS) score of 5-4 versus 3-1 (AUC, 0.7036; P = 0.149) and the PI-RADS score of 5-3 versus 2-1 (AUC, 0.6373; P = 0.014) for a subgroup. The model reduced the misdiagnosis of the PI-RADS score of 5-4 versus 3-1 by 78.57% (11/14) and the PI-RADS score of 5-3 versus 2-1 by 77.78% (14/18). The internal validation showed that the mean 5-fold cross-validated AUC was 0.8357 (95% CI, 0.8357-0.8358). Conclusion: We preliminarily suggest that the model of 10 × PRIMARY score + 2 × SUVmax may enhance the diagnostic accuracy of csPCa in patients with biopsy GG1 and an SUVmax of less than 12 by maximizing PSMA information use, reducing the misdiagnosis of the PI-RADS score, and thereby aiding in making appropriate treatment decisions.

Intermediate-risk Prostate Cancer-A Sheep in Wolf's Clothing?

This case-based discussion describes a 65-year-old man newly diagnosed with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa). According to the European Association of Urology classification system, the patient harbors an intermediate-risk cancer. In step-by step discussion, we elaborate guideline-based treatment modalities for intermediate-risk PCa focused on debating active surveillance versus active treatment. Thereby, we discuss the importance of patient characteristics, including age, hereditary factors, life expectancy and comorbidity status, findings of multiparametric magnetic resonance imaging, as well as prostate-specific antigen (PSA) density and PSA kinetics, in predicting the clinical course of the disease. In addition, we focus on cribriform pathology as a predictor of adverse outcomes and critically discuss its relevance in patient management. Lastly, we outline genomic stratification in ISUP 2 cancer as a future tool to predict PCa aggressiveness. PATIENT SUMMARY: Based on current guidelines, patients with intermediate-risk prostate cancer are treated actively or can alternatively undergo an active surveillance approach when favorable risk factors are present. One major issue is to discriminate between patients who benefit from an active therapy approach and those who benefit from a deferred treatment. Therefore, reliable biomarkers and early predictors of disease progression are needed urgently.

Biomarkers of Aggressive Prostate Cancer at Diagnosis

In the United States, prostate cancer (CaP) remains the second leading cause of cancer deaths in men. CaP is predominantly indolent at diagnosis, with a small fraction (25-30%) representing an aggressive subtype (Gleason score 7-10) that is prone to metastatic progression. This fact, coupled with the criticism surrounding the role of prostate specific antigen in prostate cancer screening, demonstrates the current need for a biomarker(s) that can identify clinically significant CaP and avoid unnecessary biopsy procedures and psychological implications of being diagnosed with low-risk prostate cancer. Although several diagnostic biomarkers are available to clinicians, very few comparative trials have been performed to assess the clinical effectiveness of these biomarkers. It is of note, however, that a majority of these clinical trials have been over-represented by men of Caucasian origin, despite the fact that African American men have a 1.7 times higher incidence and 2.1 times higher rate of mortality from prostate cancer. Biomarkers for CaP diagnosis based on the tissue of origin include urine-based gene expression assays (PCA3, Select MDx, ExoDx Prostate IntelliScore, Mi-Prostate Score, PCA3-PCGEM1 gene panel), blood-based protein biomarkers (4K, PHI), and tissue-based DNA biomarker (Confirm MDx). Another potential direction that has emerged to aid in the CaP diagnosis include multi-parametric magnetic resonance imaging (mpMRI) and bi-parametric magnetic resonance imaging (bpMRI), which in conjunction with clinically validated biomarkers may provide a better approach to predict clinically significant CaP at diagnosis. In this review, we discuss some of the adjunctive biomarker tests along with newer imaging modalities that are currently available to help clinicians decide which patients are at risk of having high-grade CaP on prostate biopsy with the emphasis on clinical utility of the tests across African American (AA) and Caucasian (CA) men.

Clinical Applications of Liquid Biopsy in Prostate Cancer: From Screening to Predictive Biomarker

Prostate cancer (PC) remains the most common malignancy and the second most common cause of cancer death in men. As a result of highly variable biological behavior and development of resistance to available agents under therapeutic pressure, optimal management is often unclear. Traditional surgical biopsies, even when augmented by genomic studies, may fail to provide adequate guidance for clinical decisions as these can only provide a snapshot of a dynamic process. Additionally, surgical biopsies are cumbersome to perform repeatedly and often involve risk. Liquid biopsies (LB) are defined as the analysis of either corpuscular (circulating tumor cells, extracellular vesicles) or molecular (circulating DNA or RNA) tumor-derived material. LB could more precisely identify clinically relevant alterations that characterize the metastatic potential of tumors, predict response to specific treatments or actively monitor for the emergence of resistance. These tests can potentially be repeated as often as deemed necessary and can detect real-time response to treatment with minimal inconvenience to the patient. In the current review, we consider common clinical scenarios to describe available LB assays in PC as a platform to explore existing evidence for their use in guiding decision making and to discuss current limitations to their adoption in the clinic.

Biomarker in Active Surveillance for Prostate Cancer: A Systematic Review

Active surveillance (AS) in prostate cancer (PCa) represents a curative alternative for men with localised low-risk PCa. Continuous improvement of AS patient's selection and surveillance modalities aims at reducing misclassification, simplifying modalities of surveillance and decreasing need for invasive procedures such repeated biopsies. Biomarkers represent interesting tools to evaluate PCa diagnosis and prognosis, of which many are readily available or under evaluation. The aim of this review is to investigate the biomarker performance for AS selection and patient outcome prediction. Blood, urinary and tissue biomarkers were studied and a brief description of use was proposed along with a summary of major findings. Biomarkers represent promising tools which could be part of a more tailored risk AS strategy aiming to offer personalized medicine and to individualize the treatment and monitoring of each patient. The usefulness of biomarkers has mainly been suggested for AS selection, whereas few studies have investigated their role during the monitoring phase. Randomized prospective studies dealing with imaging are needed as well as larger prospective studies with long-term follow-up and strong oncologic endpoints.

Liquid biomarkers in active surveillance

PURPOSE

In the past two decades, new biomarkers for prostate cancer detection and risk prediction have become available for clinical use. While tissue-based gene expression assays offer molecular risk assessment after diagnoses, several serum- and urine-based 'liquid' biomarkers are available for the pre-biopsy setting which may also play a role for active surveillance (AS).

METHODS

The medical literature was queried utilizing PubMed (pubmed.ncbi.nlm.nih.gov) for all relevant original publications describing prostate cancer biomarkers that can be identified in the blood, urine, or semen. Referenced studies must have defined patient inclusion criteria and descriptions of the biomarkers. Included studies investigated the utility of liquid biomarkers for selection or monitoring of men with prostate cancer for active surveillance.

RESULTS

PSA is the most common and readily available biomarker for prostate cancer diagnosis and treatment. Contemporary AS guidelines consider diagnostic PSA level in addition to other clinical factors when selecting men for this approach, with most recommending that initial PSA should be under 10 ng/ml. Serum PSA changes are associated with outcomes on AS but are not adequately sensitive so drive men to secondary treatment in isolation. PSA derivates including the Prostate Health Index (phi) and the 4K Score can predict higher grade cancer and may help tailor repeat prostate biopsy strategies, but further data are needed prior to routine clinic use. Several urine-based biomarkers including PCA3 and TMPRSS2:ERG levels have also been studied in the AS setting.

CONCLUSIONS

Multiple serum- and urine-based liquid biomarkers are available for use in men with prostate cancer. For AS, serum PSA is utilized in part for patient selection as well as to monitor disease over time. Models that incorporate PSA kinetics with other clinical characteristics may help tailor surveillance strategies to reduce disease burden and health care costs over time. Several novel liquid biomarkers demonstrate promise and may eventually have applications for prostate cancer surveillance as well.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Simple Summary

The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted.

Abstract

The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.