Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer

Key learnings from concordant systematic biopsies in prostate-specific membrane antigen positron emission tomography/computed tomography-guided prostate biopsies: Enhancing targeting accuracy

BACKGROUND

Prostate cancer (PCa) diagnosis and staging have evolved with the advent of 68Ga-Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT). This study investigates the role of complementary systematic biopsies (SB) during PSMA-PET/CT-guided targeted prostate biopsies (PET-TB) for PCa detection, grading, and distribution. We address the uncertainty surrounding the necessity of SB in conjunction with PET-TB.

METHODS

We analyzed PCa grading and distribution in 30 men who underwent PET-TB and SB because of contraindication to magnetic resonance imaging or high clinical suspicion of PCa. Tumor distribution was assessed in relation to the PET-highlighted lesions. Standardized reporting schemes, encompassing SUVmax , PRIMARY score, and miTNM classification, were evaluated.

RESULTS

80% of patients were diagnosed with PCa, with 70% classified as clinically significant (csPCa). SB detected more csPCa cases than PET-TB, but the differences were not statistically significant. Discordant results were observed in 25% of cases, where SB outperformed PET-TB. Spatial analysis revealed that tumor-bearing cores from SB were often located in close proximity to the PET-highlighted region. Reporting schemes showed potential for csPCa detection with significantly increased SUVmax in csPCA patients. Subsequent follow-up data underscored the importance of SB in precise PCa grading and staging.

CONCLUSIONS

While PET-TB can simplify prostate biopsy and reduce invasiveness by core number, SB cannot be omitted yet due to potential PET-TB targeting errors. Factors such as limited spatial resolution and fusion inaccuracies contribute to the need for SB. Standardization in reporting schemes currently cannot compensate for targeting errors highlighting the need for refinement.

Prognostic Value of PSMA PET/CT in Prostate Cancer

Prostate-specific membrane antigen (PSMA) is a transmembrane glycoprotein expressed in the majority of prostate cancer (PCa). PSMA has an enzymatic function that makes metabolic substrates such as folate available for utilization by PCa cells. Intracellular folate availability drives aggressive tumor phenotype. PSMA expression is, therefore, a marker of aggressive tumor biology. The large extracellular domain of PSMA is available for targeting by diagnostic and therapeutic radionuclides, making it a suitable cellular epitope for theranostics. PET imaging of radiolabeled PSMA ligands has several prognostic utilities. In the prebiopsy setting, intense PSMA avidity in a prostate lesion correlate well with clinically significant PCa (csPCa) on histology. When used for staging, PSMA PET imaging outperforms conventional imaging for the accurate staging of primary PCa, and findings on imaging predict post-treatment outcomes. The biggest contribution of PSMA PET imaging to PCa management is in the biochemical recurrence setting, where it has emerged as the most sensitive imaging modality for the localization of PCa recurrence by helping to guide salvage therapy. PSMA PET obtained for localizing the site of recurrence is prognostic, such that a higher lesion number predicts a less favorable outcome to salvage radiotherapy or surgical intervention. Systemic therapy is given to patients with advanced PCa with distant metastasis. PSMA PET is useful for predicting response to treatments with chemotherapy, first- and second-line androgen deprivation therapies, and PSMA-targeted radioligand therapy. Artificial intelligence using machine learning algorithms allows for the mining of information from clinical images not visible to the human eyes. Artificial intelligence applied to PSMA PET images, therefore, holds great promise for prognostication in PCa management.

A Clinical Role of PET-MRI in Prostate Cancer?

PET/MRI is a relevant application field for prostate cancer management, offering advantages in early diagnosis, staging, and therapy planning. Despite drawbacks such as higher costs, longer acquisition time, and the need for skilled personnel, the technical integration of PET and MRI provides valuable information for detecting primary tumors, identifying metastases, and characterizing the disease, leading to more accurate staging and personalized treatment strategies. However, PET/MRI adoption has been slow, but ongoing technological advancements and AI integration might overcome challenges and improve clinical utility. As precision medicine gains importance in oncology, PET/MRI's multiparametric data can tailor treatment plans to individual patients, providing a comprehensive assessment of tumor biology and aggressiveness for more effective therapeutic strategies.

[Prostate cancer-multiparametric MRI and alternative approaches in intervention and therapy planning]

BACKGROUND

In recent years, multiparametric magnetic resonance imaging (mpMRI) of the prostate has gained importance and plays a crucial role in both personalized diagnostics and increasingly in the treatment planning for patients with prostate cancer.

OBJECTIVE

The aim of this study is to present established and innovative applications of MRI in the diagnosis and treatment of localized prostate cancer, evaluating their strengths and weaknesses. Furthermore, it will explore alternative approaches and compare them in a comprehensive manner.

MATERIALS AND METHODS

A systematic literature review on the application of mpMRI for biopsy and therapy planning was conducted.

RESULTS

The integration of modern imaging techniques, especially mpMRI, into the diagnostic algorithm has revolutionized prostate cancer diagnosis. MRI and MRI-guided biopsy detect more significant prostate cancer, with the potential to reduce unnecessary biopsies and the diagnosis of clinically insignificant carcinomas. In addition, MRI provides crucial information for risk stratification and treatment planning in prostate cancer patients, both before radical prostatectomy and during active surveillance.

CONCLUSION

Multiparametric MRI offers significant added value for the diagnosis and treatment of localized prostate cancer. The advancement of MRI analysis, such as the implementation of artificial intelligence algorithms, holds the potential for further enhancing imaging diagnostics.

DEPROMP Trial: the additive value of PSMA-PET/CT-guided biopsy for prostate cancer management in biopsy naïve men-study protocol for a randomized trial

BACKGROUND

The primary objective is to determine the proportion of men with suspected prostate cancer (PCA) in whom the management plans are changed by additive gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) guided prostate biopsy (PET-TB) in combination with standard of care (SOC) using systematic (SB) and multiparametric magnetic resonance imaging-guided biopsy (MR-TB) compared with SOC alone. The major secondary objectives are to determine the additive value of the combined approach of SB + MR-TB + PET-TB (PET/MR-TB) for detecting clinically significant PCA (csPCA) compared to SOC; to determine sensitivity, specificity, positive and negative predictive value and diagnostic accuracy of imaging techniques, respective imaging classification systems, and each biopsy method; and to compare preoperatively defined tumor burden and biomarker expression and pathological tumor extent in prostate specimens.

METHODS

The DEPROMP study is a prospective, open-label, interventional investigator-initiated trial. Risk stratification and management plans after PET/MR-TB are conducted randomized and blinded by different evaluation teams of experienced urologists based on histopathological analysis and imaging information: one including all results of the PET/MR-TB and one excluding the additional information gained by PSMA-PET/CT guided biopsy. The power calculation was centered on pilot data, and we will recruit up to 230 biopsy-naïve men who will undergo PET/MR-TB for suspected PCA. Conduct and reporting of MRI and PSMA-PET/CT will be performed in a blinded fashion.

DISCUSSION

The DEPROMP Trial will be the first to evaluate the clinically relevant effects of the use of PSMA-PET/CT in patients with suspected PCA compared to current SOC. The study will provide prospective data to determine the diagnostic yields of additional PET-TB in men with suspected PCA and the impact on treatment plans in terms of intra- and intermodal changes. The results will allow a comparative analysis of risk stratification by each biopsy method, including a performance analysis of the corresponding rating systems. This will reveal potential intermethod and pre- and postoperative discordances of tumor stage and grading, providing the opportunity to critically assess the need for multiple biopsies.

TRIAL REGISTRATION

German Clinical Study Register DRKS 00024134. Registered on 26 January 2021.

18 F-DCFPyL positron emission tomography/magnetic resonance imaging-guided ultrasound fusion biopsy is an identical pathway in prostate cancer diagnosis

BACKGROUND

Prostate biopsy is still unavoidable in patients with a rising prostate-specific antigen even though multiparametric magnetic resonance imaging (MRI) is widely used. ¹⁸ F-DCFPyL positron emission tomography (PET)/MRI was proved to be promising both in sensitivity and specificity. But its guiding fusion biopsy and the advantages in the diagnosis of prostate disease is seldom reported. This study aimed to verify the feasibility and advantage of ¹⁸ F-DCFPyL PET/MRI-guided fusion targeted biopsy (TB) over whole-mount histopathology (WMH) for prostate cancer diagnosis.

METHODS

A prospective study of 94 biopsy-naïve patients were conducted using ¹⁸ F-DCFPyL PET/MRI scans and scored on a scale of 1-4. Systematic biopsy was performed for all patients. Patients with suspicious lesions also underwent PET/MRI/transrectal ultrasound-guided fusion biopsy. Patients with pathologically confirmed cancer underwent surgery and WMH sections. Systematic biopsy was compared with TB for the detection of index tumors (ITs). Significant cancer was defined as Grade group (GG) 2 or higher no matter the length of the cancer core.

RESULTS

¹⁸ F-DCFPyL PET/MRI detected 30/94 (32%) patients with a score of 4, all of whom were verified to have prostate cancer. While it detected 10 patients with a score of 1 (10.6%), they were shown to have no cancer. The sensitivity and specificity of ¹⁸ F-DCFPyL PET/MRI were 94.4% and 75%, respectively, if images with a score of 3 are defined as positive. Systematic biopsy detected 18% (203/1128) samples as prostate cancer; conversely, TB detected 113 samples out of 259 scores (43.6%). A statistically significant difference was seen between the PCa detection rates by TB and SB (p < 0.001). All targeted lesions were pathologically proven to be the IT on WMH.

CONCLUSIONS

In biopsy-naïve patients, the ultrasound fusion biopsy targeted by ¹⁸ F-DCFPyL PET/MRI is an identical pathway for the detection of prostate cancer.

Landmarks in the evolution of prostate biopsy

Approaches and techniques used for diagnostic prostate biopsy have undergone considerable evolution over the past few decades: from the original finger-guided techniques to the latest MRI-directed strategies, from aspiration cytology to tissue core sampling, and from transrectal to transperineal approaches. In particular, increased adoption of transperineal biopsy approaches have led to reduced infectious complications and improved antibiotic stewardship. Furthermore, as image fusion has become integral, these novel techniques could be incorporated into prostate biopsy methods in the future, enabling 3D-ultrasonography fusion reconstruction, molecular targeting based on PET imaging and autonomous robotic-assisted biopsy.

Using PSMA imaging for prognostication in localized and advanced prostate cancer

The use of prostate-specific membrane antigen (PSMA)-directed applications in modern prostate cancer management has evolved rapidly over the past few years, helping to establish new treatment pathways and provide further insights into prostate cancer biology. However, the prognostic implications of PSMA-PET have not been studied systematically, owing to rapid clinical implementation without long follow-up periods to determine intermediate-term and long-term oncological outcomes. Currently available data suggest that traditional prognostic factors and survival outcomes are associated with high PSMA expression (both according to immunohistochemistry and PET uptake) in men with localized and biochemically recurrent disease. Treatment with curative intent (primary and/or salvage) often fails when PSMA-positive metastases are present; however, the sensitivity of PSMA-PET in detecting all metastases is poor. Low PSMA-PET uptake in recurrent disease is a favourable prognostic factor; however, it can be associated with poor prognosis in conjunction with high ¹⁸F-fluorodeoxyglucose uptake in metastatic castration-resistant prostate cancer. Clinical trials embedding PSMA-PET for guiding management with reliable oncological outcomes are needed to support ongoing clinical use.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Simple Summary

Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices.

Abstract

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Overview of radiomics in prostate imaging and future directions

Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.

Radiogenomics in prostate cancer evaluation

PURPOSE OF REVIEW

Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management.

RECENT FINDINGS

Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice.

SUMMARY

In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making.

Background, applications and challenges of radiogenomics in genitourinary tumor

Genitourinary tumors are groups of tumors with high complexity and heterogeneity. For long-term monitoring, biomarkers that can be used in detection, grading and treatment response assessment are needed. With rapid development in imaging technology and cancer genomics, radiogenomics, the combination of "radiology" and "genomics", has emerged as a powerful tool in oncology practice in recent years because imaging can provide some information that genomic test cannot as gene expression and mutation status are usually evaluated on a small portion of the tumor and are usually not powerful enough to reflect tumor heterogeneity. Radiogenomics investigates the correlations between imaging features and gene expression of a disease, especially in oncologic diseases. It aims to detect the disease's mutation status and supplement genomic analysis based on imaging analysis, providing additional findings for diagnosis, treatment decisions, evaluation of treatment response and prognosis prediction of the disease. Recent years have seen an increase in the number of studies investigating the application of radiogenomics in genitourinary tumors. Many studies have shown promising results. However, there still exist limitations and challenges. In this review, we will summarize recent applications of radiogenomics in genitourinary tumors and discuss limitiations, challenges and future directions of radiogenomics.

[Machine learning and multiparametric MRI for early diagnosis of prostate cancer]

In the last few years, the early diagnosis of prostate cancer has continued to shift from systematic biopsies to multiparametric MRI (mpMRI)-guided/MRI-transrectal ultrasound (TRUS) fusion biopsies and guidelines are already reflecting these changes. While MRI-TRUS fusion biopsies have already resulted in significant improvements in diagnostic sensitivity and, thus, correct diagnosis of clinically significant prostate cancer (sPC), its use to avoid biopsies in certain men is still controversial. Optimal use of mpMRI requires a high degree of reader expertise due to the difficulty of image interpretation and poses the problem of training sufficient numbers of radiologists while demand is increasing. Recently, artificial intelligence (AI) has been utilized to create fully automatic analysis tools for interpretation of mpMRI of the prostate, rivaling the performance of clinical radiologist interpretation in retrospective research studies, demonstrating the promising potential of AI for diagnostic prostate MRI in the future. This article will provide an overview of machine and deep learning and its application in mpMRI of the prostate for early diagnosis of prostate cancer.

Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

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.

Conspicuity of prostate cancer on multiparametric magnetic resonance imaging: A cross-disciplinary translational hypothesis

Pre-biopsy multiparametric magnetic resonance imaging (mpMRI) has transformed the risk stratification and diagnostic approach for suspected prostate cancer. The majority of clinically significant prostate cancers are visible on pre-biopsy mpMRI, however, there are a subset of significant tumors that are not detected by mpMRI. The radiobiological mechanisms underpinning mpMRI-visibility and invisibility of these cancers remain uncertain. Emerging evidence suggests that mpMRI-visible tumors are enriched with molecular features associated with increased disease aggressivity and poor clinical prognosis, which is supported by short-term endpoints, such as biochemical recurrence following surgery. Furthermore, at the histopathological level, mpMRI-visible tumors appear to exhibit increased architectural and vascular density compared to mpMRI-invisible disease. It seems probable that the genomic, pathological, radiological, and clinical features of mpMRI-visible and mpMRI-invisible prostate cancers are interrelated. Here, we propose a novel cross-disciplinary theory that links genomic and molecular evidence with cellular and histopathological appearances, elucidating both the mpMRI visibility and clinical status of significant prostate cancer.

Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer

PURPOSE

Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [¹⁸F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features.

METHODS

In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [¹⁸F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC.

RESULTS

The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance.

CONCLUSION

Machine learning-based analysis of quantitative [¹⁸F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

68Ga-PSMA PET/CT tumour intensity pre-operatively predicts adverse pathological outcomes and progression-free survival in localised prostate cancer

PURPOSE

Prostate-specific membrane antigen (PSMA) positron emission tomography (PSMA-PET) improves prostate cancer staging. Intraprostatic PSMA intensity may predict clinically relevant oncological outcomes. The aim of this study was to investigate the relationship between intraprostatic PSMA intensity and adverse pathology outcomes, including biochemical progression-free survival (PFS) after radical prostatectomy.

METHODS

This is a cohort study of 71 patients with MRI-guided, biopsy-proven prostate cancer and pre-operative ⁶⁸Ga-PSMA-11 PET/CT prior to radical prostatectomy (RP). Intraprostatic PSMA intensity was correlated to adverse pathology outcomes (Gleason score and upgrading from biopsy, pathological stage) and PFS using multivariate statistical analysis.

RESULTS

⁶⁸Ga-PSMA-11 PET/CT intensity in vivo predicted all of Gleason score on RP, upgrading from biopsy to RP histopathology, pathological stage, positive surgical margins and PFS. 74.6% (53/71) of patients were free from progression at a median follow-up of 19.5 months (0.4-48 months). Predictive accuracy was particularly enhanced by PSMA among patients with biopsy Gleason score ≤ 3 + 4 (n = 39) as the most significant predictor of PFS according to Cox-proportional hazards regression. Cox-regression adjusted survival analysis predicted a 5.48-fold increase in hazard for Gleason score ≤ 3 + 4 patients with high (SUVmax > 8) compared with low (SUVmax < 8) PSMA intensity.

CONCLUSION

Intraprostatic ⁶⁸Ga-PSMA-11 intensity is prognostic and may be a valuable new biomarker in localised prostate cancer, especially in men with biopsy-proven Gleason 3 + 4 disease considering an initial approach of active surveillance or focal therapy.

Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis

Context

Multiparametric magnetic resonance imaging (mpMRI) detects most, but not all, clinically significant prostate cancer. The genetic basis of prostate cancer visibility and invisibility on mpMRI remains uncertain.

Objective

To systematically review the literature on differential gene expression between mpMRI-visible and mpMRI-invisible prostate cancer, and to use bioinformatic analysis to identify enriched processes or cellular components in genes validated in more than one study.

Evidence acquisition

We performed a systematic literature search of the Medline, EMBASE, PubMed, and Cochrane databases up to January 2020 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The primary endpoint was differential genetic features between mpMRI-visible and mpMRI-invisible tumours. Secondary endpoints were explanatory links between gene function and mpMRI conspicuity, and the prognostic value of differential gene enrichment.

Evidence synthesis

We retrieved 445 articles, of which 32 met the criteria for inclusion. Thematic synthesis from the included studies showed that mpMRI-visible cancer tended towards enrichment of molecular features associated with increased disease aggressivity, including phosphatase and tensin homologue (PTEN) loss and higher genomic classifier scores, such as Oncotype and Decipher. Three of the included studies had accompanying publicly available data suitable for further bioinformatic analysis. An over-representation analysis of these datasets revealed increased expression of genes associated with extracellular matrix components in mpMRI-visible tumours.

Conclusions

Prostate cancer that is visible on mpMRI is generally enriched with molecular features of tumour development and aggressivity, including activation of proliferative signalling, DNA damage, and inflammatory processes. Additionally, there appears to be concordant cellular components and biological processes associated with mpMRI conspicuity, as highlighted by bioinformatic analysis of large genetic datasets.

Patient summary

Prostate cancer that is detected by magnetic resonance imaging (MRI) tends to have genetic features that are associated with more aggressive disease. This suggests that MRI can be used to assess the likelihood of aggressive prostate cancer, based on tumour visibility.

Radiogenomics Based on PET Imaging

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.

Application of Radiomics for Personalized Treatment of Cancer Patients

Radiomics is a novel concept that relies on obtaining image data from examinations such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). With the appropriate algorithm, the extracted results have broad applicability and potential for a massive positive impact in radiology. For example, clinicians can verify treatment efficiency, predict the location of tumor metastasis, correlate results with a histopathological examination, or more accurately define the type of cancer. Combining radiomics with other testing techniques allows every patient to have a personalized treatment plan that is essential for advanced examination and treatment. This article explains the process of radiomics, including data collection mechanisms, combined use with genomics, and artificial intelligence and immunology techniques, which may solve many of the challenges faced by doctors in diagnosing and treating their patients.

Gallium-68-Labeled Prostate-Specific Membrane Antigen-11 PET/CT of Prostate and Nonprostate Cancers

OBJECTIVE. The purpose of this study is to provide a concise summary of the current experience with ⁶⁸Ga-labeled prostate-specific membrane antigen (PSMA)-11 imaging of prostate and nonprostate malignancies and benign conditions. CONCLUSION. PSMA is overexpressed in prostate cancer and in the neovasculature of many other malignancies. The relevance of PSMA as a biologic target, coupled with advances in the design, synthesis, and evaluation of PSMA-based radionuclides for imaging and therapy, is anticipated to play a major role in patient care.

Radiomic features from PSMA PET for non-invasive intraprostatic tumor discrimination and characterization in patients with intermediate- and high-risk prostate cancer - a comparison study with histology reference

Purpose: To evaluate the performance of radiomic features (RF) derived from PSMA PET for intraprostatic tumor discrimination and non-invasive characterization of Gleason score (GS) and pelvic lymph node status. Patients and methods: Patients with prostate cancer (PCa) who underwent [⁶⁸Ga]-PSMA-11 PET/CT followed by radical prostatectomy and pelvic lymph node dissection were prospectively enrolled (n=20). Coregistered histopathological gross tumor volume (GTV-Histo) in the prostate served as reference. 133 RF were derived from GTV-Histo and from manually created segmentations of the intraprostatic tumor volume (GTV-Exp). Spearman´s correlation coefficients (ρ) were assessed between RF derived from the different GTVs. We additionally analyzed the differences in RF values for PCa and non-PCa tissues. Furthermore, areas under receiver-operating characteristics curves (AUC) were calculated and uni- and multivariate analyses were performed to evaluate the RF based discrimination of GS 7 and ≥8 disease and of patients with nodal spread (pN1) and non-nodal spread (pN0) in surgical specimen. The results found in the latter analyses were validated by a retrospective cohort of 40 patients. Results: Most RF from GTV-Exp showed strong correlations with RF from GTV-Histo (86% with ρ>0.7). 81% and 76% of RF from GTV-Exp and GTV-Histo significantly discriminated between PCa and non-PCa tissue. The texture feature QSZHGE discriminated between GS 7 and ≥8 considering GTV-Histo (AUC=0.93) and GTV-Exp (prospective cohort: AUC=0.91 / validation cohort: AUC=0.84). QSZHGE also discriminated between pN1 and pN0 disease considering GTV-Histo (AUC=0.85) and GTV-Exp (prospective cohort: AUC=0.87 / validation cohort: AUC=0.85). In uni- and multivariate analyses including patients of both cohorts QSZHGE was a statistically significant (p<0.01) predictor for PCa patients with GS ≥8 tumors and pN1 status. Conclusion: RF derived from PSMA PET discriminated between PCa and non-PCa tissue within the prostate. Additionally, the texture feature QSZHGE discriminated between GS 7 and GS ≥8 tumors and between patients with pN1 and pN0 disease. Our results support the role of RF in PSMA PET as a new tool for non-invasive PCa discrimination and characterization of its biological properties.