Multiparametric magnetic resonance imaging for prostate cancer—a comparative study including radical prostatectomy specimens

Advancing Traditional Prostate-specific Antigen Kinetics in the Detection of Prostate Cancer: A Machine Learning Model

BACKGROUND

Prostate-specific antigen (PSA) kinetics, defined as the change in PSA over time, may be of use as a predictor of prostate cancer. PSA kinetics can be assessed as the PSA velocity, which is traditionally evaluated dichotomously and classified as abnormal if greater than either 0.35 or 0.75 ng/ml/yr. Machine learning models may provide additional benefit in assessing risk using PSA kinetics instead of PSA velocity.

OBJECTIVE

To improve the utility of PSA kinetics by constructing a generalizable, universal machine learning model.

DESIGN, SETTING, AND PARTICIPANTS

Data were obtained from the PLCO and PCPT trials and from a contemporary Australian cohort. PSA data were interpolated using a modified Gaussian process. A machine learning model based on a two-headed approach was designed, in which the multivariable input was fed into a one-dimensional ResNet18 model.

OUTCOME MEASURES AND STATISTICAL ANALYSIS

The model performance was assessed compared to PSA levels and PSA velocity in terms of area under the receiver operator characteristic curve (AUC).

RESULTS AND LIMITATIONS

A total of 10719 patients were included in the analysis. In tests on a validation set of the complete database to diagnose grade group ≥2, the AUC was 0.886 (95% confidence interval [CI] 0.870-0.902) for the machine learning model, compared to 0.807 (95% CI 0.796-0.819) for PSA and 0.627 (95% CI 0.607-0.648) for PSA velocity.

CONCLUSIONS

Machine learning models can be used to augment the diagnostic utility of PSA kinetics in the diagnosis of prostate cancer. We demonstrated significant improvements in accuracy compared to the traditional approaches of PSA velocity and PSA thresholds.

PATIENT SUMMARY

Prostate cancer diagnosis is limited by the diagnostic accuracy of the prostate-specific antigen (PSA) blood test. Advances in techniques such as machine learning algorithms can greatly improve the diagnostic accuracy of prostate cancer screening without additional costs or tests.

Current Opinion on the Use of Magnetic Resonance Imaging in Staging Prostate Cancer: A Narrative Review

Accurate staging is critical for treatment planning and prognosis in men with prostate Cancer. Prostate magnetic imaging resonance (MRI) may aid in the staging evaluation by verifying organ-confined status, assessing the status of the pelvic lymph nodes, and establishing the local extent of the tumor in patients being considered for therapy. MRI has a high specificity for diagnosing extracapsular extension, and therefore may impact the decision to perform nerve sparing prostatectomy, along with seminal vesicle invasion and lymph node metastases; however, its sensitivity remains limited. Current guidelines vary significantly regarding endorsing the use of MRI for staging locoregional disease. For high-risk prostate cancer, most guidelines recommend cross sectional imaging, including MRI, to evaluate for more extensive disease that may merit change in radiation field, extended androgen deprivation therapy, or guiding surgical planning. Although MRI offers reasonable performance characteristics to evaluate bone metastases, guidelines continue to support the use of bone scintigraphy. Emerging imaging technologies, including coupling positron emission tomography (PET) with MRI, have the potential to improve the accuracy of prostate cancer staging with the use of novel radiotracers.

Patterns of primary staging for newly diagnosed prostate cancer in the era of prostate specific membrane antigen positron emission tomography: A population-based analysis

INTRODUCTION

There has been a growing body of evidence highlighting the improved sensitivity and specificity for prostate specific membrane antigen (PSMA) positron emission tomography (PET) in advanced prostate cancer imaging. We aimed to assess prostate cancer staging practice patterns in Australia using population-based data.

SUBJECT AND METHODS

We extracted data on men diagnosed with prostate cancer between October 2016 and December 2018 from the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic). We evaluated trends and comparisons between patients receiving PET/CT (with or without conventional imaging (CImg)), and CImg alone, and analysed imaging modality as predictor of clinical regional node positive disease (cN1 vs cN0/X), metastatic disease (cM1 vs cM0/X), and treatment received.

RESULTS

In total, 6139 patients in the registry had either a staging PET scan (n = 889, 14%), CImg without PET scan (n = 2464, 40%), or no recorded PET or CImg (n = 2786, 45%). The proportion of allimaged patients who received staging PET increased from 19% to 36% from the first to last three-month period, and in the high-risk category the increase was 23-43%. After adjustment for grade group, PET vs CImg-only patients were observed to have a higher proportion of cN1 disease (OR = 2.46, 95% CI: 1.90-3.20) but not cM1 disease (OR = 1.10, 95% CI: 0.84-1.44).

CONCLUSIONS

Our registry data highlights the rapid uptake of PET imaging, particularly in high-risk disease. Based on this data, we highlight the increased diagnosis of nodal disease, thus potentially optimizing patient selection prior to definitive treatment for prostate cancer.

Multiparametric MRI with in-bore targeted biopsy in the diagnostic pathway of prostate cancer: Data from a single institution experience

BACKGROUND

Accuracy of multiparametric MRI (mpMRI) for the detection of significant prostate cancer (CaP) varies in the literature as only few studies use radical prostatectomy specimens as their gold standard. On another hand, MRI-targeted prostate biopsy is emerging as an alternative to the traditional randomized biopsy, with a higher detection rate of high-grade cancers. However, data on MRI guided in bore biopsy is lacking.

MATERIAL AND METHODS

We reviewed every patient that had his mpMRI, MRI guided in bore biopsy and radical prostatectomy performed in our hospital between November 2015 and December 2020. The diagnostic performances of both mpMRI and MRI targeted biopsy in sampling PIRADS index lesions were studied, using radical prostatectomy specimens as the gold standard. Sensitivity, specificity, positive predictive value and negative predictive value of mpMRI for detecting T3 stage, extra-capsular extension, seminal vesicles involvement and lymph node disease were also evaluated.

RESULTS

Sixty-two met our inclusion criteria. For PIRADS≥3 lesions, sensitivity and positive predictive value for detecting clinically significant CaP were of 83.5% and 94.7%. A total of 32.2% prostate cancers on targeted biopsy were upgraded on final pathology, with an upgrading to ISUP≥2 in 3.2% and to ISUP≥3 in 14.5%. A total of 20.9% of cancers were downgraded but without any downgrading to ISUP 1. When final pathology is taken as a gold standard, sensitivity of mpMRI was 31.8% for T3 staging prediction, 30.0% for extra-capsular extension, 28.7% for seminal vesicles involvement and 66.7% for lymph node disease prediction. Specificity was 89.3%, 93.1%, 95.3%, and 92.7%, respectively.

CONCLUSION

mpMRI has an acceptable accuracy for the prediction of significant CaP and index lesion detection but is unreliable for CaP staging. Comparison between pathology and biopsy results revealed that the in-bore biopsy technique has an upgrading and downgrading rate comparable in the literature to fusion biopsy, but higher than the combined biopsy approach.

Predictive clinical features for negative histopathology of MRI/Ultrasound-fusion-guided prostate biopsy in patients with high likelihood of cancer at prostate MRI: Analysis from a urologic outpatient clinic1

OBJECTIVE

The aim of this study was to evaluate clinical features associated with benign histopathology of Prostate Imaging Reporting and Data System (PI-RADS) category 4 and 5 lesions.

MATERIALS AND METHODS

Between March 2015 and November 2020, 1161 patients underwent mpMRI/Ultrasound-fusion-guided prostate biopsy (FBx) and concurrent 12-core systematic prostate biopsy (SBx) at the Department of Urology of the Ludwig-Maximilians-University of Munich, Germany. 848/ 1161 (73%) patients presented with either PI-RADS 4 or 5 index lesion and were retrospectively evaluated. Multivariate analysis was performed to evaluate clinical parameters associated with a negative outcome of PI-RADS 4 or 5 category lesions after FBx. Area under the receiver operating characteristics (ROC) curve (AUC) was conducted using ROC-analysis.

RESULTS

676/848 (79.7%) patients with either PI-RADS 4 or 5 index lesion were diagnosed with prostate cancer (PCa) by FBx and 172/848 (20.3%) patients had a negative biopsy (including the concurrent systematic prostate biopsy), respectively. Prostate volume (P-Vol) (OR 0.99, 95% CI = 0.98-1.00, p = 0.038), pre-biopsy-status (OR 0.48, 95% CI = 0.29-0.79, p = 0.004) and localization of the lesion in the transitional zone (OR 0.28, 95% CI = 0.13-0.60, p = 0.001) were independent risk factors for a negative outcome of FBx. Age (OR 1.09, 95% CI = 1.05-1.13, p < 0.001) and PSA density (PSAD) (OR 75.92, 95% CI = 1.03-5584.61, p = 0.048) increased the risk for PCa diagnosis after FBx. The multivariate logistic regression model combining all clinical characteristics achieved an AUC of 0.802 (95% CI = 0.765-0.835; p < 0.001) with a sensitivity and specificity of 66% and 85%.

CONCLUSION

Lesions with high or highly likelihood of PCa on multiparametric magnetic resonance imaging (mpMRI) but subsequent negative prostate biopsy occur in a small amount of patients. Localization of the lesion in the transitional zone, prostate volume and prebiopsy were shown to be predictors for benign histopathology of category 4 or 5 lesions on mpMRI. Integration of these features into daily clinical routine could be used for risk-stratification of these patients after negative biopsy of PI-RADS 4 or 5 index lesions.

The Impact of Histopathological Features of Prostate Cancerous Lesions on Multiparametric Magnetic Resonance Imaging Findings using PI-RADS Version 2

OBJECTIVES

To determine the square measure threshold of prostate cancer lesions in pathological specimens showing PI-RADS categories 3 to 5, and to identify the pathological characteristics of cancerous lesions over the threshold.

METHODS

Cancer foci detected in horizontal sections of specimens were defined as pathological cancerous lesions, in which square measure, lesion location (peripheral or transition zone), Gleason pattern (GP), GP4-5 component percentages, and GP 4 subtypes were assessed. A receiver operating characteristic curve was used to determine the threshold of the square measure of pathological specimens that distinguishes between lesions of PI-RADS categories 1 and 2 and those of 3 to 5. Univariable and multivariable analyses were performed to determine the histopathological features associated with PI-RADS categories 3 to 5.

RESULTS

A total of 100 consecutive patients underwent multiparametric magnetic resonance imaging before robotic-assisted laparoscopic prostatectomy. A total of 1366 pathological cancerous lesions were detected, 217 of which were classified as PI-RADS categories 3 to 5. A square measure of 40 mm² on pathological specimens was the threshold for PI-RADS categories 3 to 5. Of the 415 lesions that were over 40 mm², 211 lesions exhibited PI-RADS categories 1, 2 and 204 lesions exhibited PI-RADS categories 3 to 5. Multiple logistic regression analysis showed that square measure, fused glands, and cribriform glands were independently associated with PI-RADS categories 3 to 5.

CONCLUSION

Cancerous lesions over 40 mm² showing PI-RADS categories 3 to 5 are associated with square measure, fused glands, and cribriform glands.

PSA-based machine learning model improves prostate cancer risk stratification in a screening population

CONTEXT

The majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and biochemical adjuncts to PSA may improve prostate cancer risk stratification. We aimed to develop a contemporary, accurate and cost-effective model based on objective measures to improve the accuracy of prostate cancer risk stratification.

METHODS

Data were collated from a local institution and combined with patient data retrieved from the Prostate, Lung, Colorectal and Ovarian Cancer screening Trial (PLCO) database. Using a dataset of 4548 patients, a machine learning model was developed and trained using PSA, free-PSA, age and free-PSA to total PSA (FTR) ratio.

RESULTS

The model was trained on a dataset involving 3638 patients and was then tested on a separate set of 910 patients. The model improved prediction for prostate cancer (AUC 0.72) compared to PSA alone (AUC 0.63), age (AUC 0.52), free-PSA (AUC 0.50) and FTR alone (AUC 0.65). When an operating point is chosen such that the sensitivity of the model is 80% the specificity of the model is 45.3%. The benefit in AUC secondary to the model was related to sample size, with AUC of 0.64 observed when a subset of the cohort was assessed.

CONCLUSIONS

Development of a dense neural network model improved the diagnostic accuracy in screening for prostate cancer. These results demonstrate an additional utility of machine learning methods in prostate cancer risk stratification when using biochemical parameters.

Contrast-enhanced ultrasound with dispersion analysis for the localization of prostate cancer: correlation with radical prostatectomy specimens

PURPOSE

To determine the value of two-dimensional (2D) contrast-enhanced ultrasound (CEUS) imaging and the additional value of contrast ultrasound dispersion imaging (CUDI) for the localization of clinically significant prostate cancer (csPCa).

METHODS

In this multicentre study, subjects scheduled for a radical prostatectomy underwent 2D CEUS imaging preoperatively. CUDI maps were generated from the CEUS recordings. Both CEUS recordings and CUDI maps were scored on the likelihood of presenting csPCa (any Gleason ≥ 4 + 3 and Gleason 3 + 4 larger than 0.5 mL) by five observers and compared to radical prostatectomy histopathology. An automated three-dimensional (3D) fusion protocol was used to match imaging with histopathology. Receiver operator curve (ROC) analysis was performed per observer and imaging modality.

RESULTS

133 of 216 (62%) patients were included in the final analysis. Average area under the ROC for all five readers for CEUS, CUDI and the combination was 0.78, 0.79 and 0.78, respectively. This yields a sensitivity and specificity of 81 and 64% for CEUS, 83 and 56% for CUDI and 83 and 55% for the combination. Interobserver agreement for CEUS, CUDI and the combination showed kappa values of 0.20, 0.18 and 0.18 respectively.

CONCLUSION

The sensitivity and specificity of 2D CEUS and CUDI for csPCa localization are moderate. Despite compressing CEUS in one image, CUDI showed a similar performance to 2D CEUS. With a sensitivity of 83% at cutoff point 3, it could become a useful imaging procedure, especially with 4D acquisition, improved quantification and combination with other US imaging techniques such as elastography.

Accuracy of multiparametric magnetic resonance imaging for detecting extracapsular extension in prostate cancer: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of multiparametric MRI (mpMRI) for detecting extracapsular extension (ECE) in patients with prostate cancer (PCa).

METHODS AND MATERIALS

We searched MEDLINE, PubMed, Embase and the Cochrane library up to December 2018. We included studies that used mpMRI to differentiate ECE from organ-confined PCa with a combination of T₂ weighted imaging (T₂WI), diffusion-weighted imaging, and dynamic contrast-enhanced MRI. All studies included had pathological diagnosis with radical prostatectomy. Two reviewers independently assessed the methodological quality of included studies by using Quality Assessment of Diagnostic Accuracy Studies 2 tool. We calculated pooled sensitivity, specificity, positive and negative predictive values, diagnostic odds ratios and receiver operating characteristic curve for mpMRI from 2 × 2 tables.

RESULTS

A total of 17 studies that comprised 3374 participants were included. The pooled data showed a sensitivity of 0.55 (95% confidence interval 0.43, 0.66]) and specificity of 0.87 (95% confidence interval 0.82, 0.91) for extracapsular extension detection in PCa.

CONCLUSION

First, our meta-analysis shows moderate sensitivity and high specificity for mpMRI to differentiate ECE from organ-confined prostate cancer before surgery. Second, our meta-analysis shows that mpMRI had no significant differences in performance compared with the former meta-analysis with use of T₂WI alone or with additional functional MRI.

ADVANCES IN KNOWLEDGE

It is the first meta-analysis to evaluate the accuracy of mpMRI in combination of TWI, diffusion-weightedimaging and dynamiccontrast-enhanced-MRI for extracapsular extension detection.

Imaging as a Personalized Biomarker for Prostate Cancer Risk Stratification

Biomarkers provide objective data to guide clinicians in disease management. Prostate-specific antigen serves as a biomarker for screening of prostate cancer but has come under scrutiny for detection of clinically indolent disease. Multiple imaging techniques demonstrate promising results for diagnosing, staging, and determining definitive management of prostate cancer. One such modality, multiparametric magnetic resonance imaging (mpMRI), detects more clinically significant disease while missing lower volume and clinically insignificant disease. It also provides valuable information regarding tumor characteristics such as location and extraprostatic extension to guide surgical planning. Information from mpMRI may also help patients avoid unnecessary biopsies in the future. It can also be incorporated into targeted biopsies as well as following patients on active surveillance. Other novel techniques have also been developed to detect metastatic disease with advantages over traditional computer tomography and magnetic resonance imaging, which primarily rely on defined size criteria. These new techniques take advantage of underlying biological changes in prostate cancer tissue to identify metastatic disease. The purpose of this review is to present literature on imaging as a personalized biomarker for prostate cancer risk stratification.

PSMA PET applications in the prostate cancer journey: from diagnosis to theranostics

The heterogeneity of prostate cancer has made imaging modalities of crucial importance in this disease. Accurate diagnosis and staging of the volume and extent of disease, especially in advanced and metastatic prostate cancer, can help to tailor the timing and modalities of treatment. While MRI has been effective in the detection of significant prostate cancer, its use in the identification and quantification of extraprostatic disease is limited. This gap is now being filled by PSMA PET. PSMA PET scans have now been shown to have a role in all stages in the prostate cancer journey. Emerging evidence has shown its promise in primary staging, restaging and theranostics. In this paper, we review the evidence for the use of PSMA PET in the various stages of prostate cancer, from initial diagnosis to advanced metastatic disease where other systemic treatments have failed.

Comparison of the accuracy of multiparametric magnetic resonance imaging (mpMRI) results with the final pathology findings for radical prostatectomy specimens in the detection of prostate cancer

AIMS

To assess the accuracy of multiparametric magnetic resonance imaging (mpMRI), used in conjunction with the Prostrate Imaging Reporting and Data System (PI-RADS), version 2, in the detection of prostate cancer (PCa), and to determine the extent of the efficacy of mpMRI as a screening test in biopsy-naïve patients.

METHODS

Retrospective analysis was conducted in 107 patients who underwent mpMRI prior to radical prostatectomy (RP) at a single institution. The mpMRI findings were reassessed using PI-RADS, version 2. A comparison was made between the histological findings for the RP specimens and the mpMRI results.

RESULTS

Unique histologically confirmed PCa foci (237) were identified in 107 patients. Overall, mpMRI sensitivity of 46% was found for PCa detection (110/237). The sensitivity, specificity and negative predictive value of mpMRI was 75.5%, 77.0% and 79.8%, respectively, for clinically significant cancer, and 75.7%, 77.7% and 79.5%, for pathological index tumors. A moderate and significant correlation was observed between a high PI-RADS score and a high pathological grade, tumor volume, index tumor status and clinically significant cancer status (all, P < 0.001, respectively). Pathological tumor volume was a significant predictor of PCa detection using mpMRI according to multivariate analysis. Using a cut-off value of 0.89 cc, the sensitivity and specificity of mpMRI for PCa detection were 0.87 and 0.65, respectively.

CONCLUSION

The mpMRI, used in conjunction with PI-RADS, was useful in detecting PCa and in predicting tumor aggressiveness. However, the detection of 20% of clinically significant cancer was missed using mpMRI. Thus, its inclusion in a triage test should be limited to selected biopsy-naïve patients.

68Ga-prostate-specific membrane antigen-positron emission tomography/computed tomography in advanced prostate cancer: Current state and future trends

The early and accurate detection of prostate cancer is important to ensure timely management and appropriate individualized treatment. Currently, conventional imaging has limitations particularly in the early detection of metastases and at prostate-specific antigen (PSA) levels < 2.0 ng/mL. Furthermore, disease management such as salvage radiotherapy is best at low PSA levels. Thus, it is critical to capture the disease in the oligometastatic stage as disease progression and commencement of systemic therapies can be delayed by metastasis-directed therapy. Prostate-specific membrane antigen (PSMA) is overexpressed in prostatic cancer cells. Novel imaging modalities using radiolabeled tracers with PSMA such as ⁶⁸Ga-PSMA-positron emission tomography (PET)/computed tomography (CT) have shown promising results. We review the literature regarding ⁶⁸Ga-PSMA-PET/CT in the setting of primary prostate cancer and biochemical recurrence. At present, the best utilization of ⁶⁸Ga-PSMA-PET/CT appears to be in biochemical recurrence. ⁶⁸Ga-PSMA-PET/CT has high diagnostic accuracy for lymph node metastases and has been shown to have superior detection rates to conventional imaging, especially at low PSA levels. The exact role of ⁶⁸Ga-PSMA-PET/CT in primary prostate cancer is not yet entirely clear. It has an improved detection rate for smaller lesions and may be able to identify nodal or distant metastatic disease at an earlier stage. While still experimental, there may also be value in combining ⁶⁸Ga-PSMA-PET to multiparametric magnetic resonance imaging for staging of intraprostatic disease. To date, ⁶⁸Ga-PSMA-PET/CT has been shown to have considerable clinical value and to impact treatment selection for patients with prostate cancer. Still in its infancy, the results of future clinical trials will be excitedly awaited.