Likelihood of sampling prostate cancer at systematic biopsy as a function of gland volume and number of cores

BACKGROUND

Pre-biopsy multiparametric magnetic resonance imaging (mpMRI) of the prostate is used to conduct targeted prostate biopsy (TB), guided by ultrasound and registered (fused) to the MRI. Systematic biopsy (SB) continues to be used together with TB or in mpMRI-negative patients. There is insufficient evidence on how to use SB to inform clinical decision-making in the mpMRI era. The purpose of this study was to estimate the effect of prostate volume and number of SB cores on sampling clinically significant prostate cancer (csPCa) using a simulation method based on clinical data.

METHODS

SBs were simulated using data from 42 patients enrolled in a transrectal ultrasound robot-assisted biopsy trial. Linear mixed models were used to examine the relationship between the number of SB cores and prostate volume on 1) clinically significant cancer detection probability (csCDP) and 2) percent of mpMRI depicted regions of interest (ROIs) sampled with the SB.

RESULTS

Median values and interquartile range (IQR) were 47.16 cm3 (35.61-65.57) for prostate volume, 0.57 cm3 (0.39-0.83) for ROI volume, and 4.0 (2-4) for PI-RADS v2.1 scores on MRI. csCDP increased with the increasing number of simulated SB cores and decreased substantially with larger prostate volume. Similarly, the percent of ROIs sampled increased with the increasing number of simulated SB cores and was lower for prostate volumes ≥60 cm3 compared to glands <60 cm3.

CONCLUSIONS

The effect of the number of SBs performed on detecting csPCa varies largely with gland volume. The common 12-core SB can achieve adequate cancer detection and sampling of ROIs in smaller glands, but not in larger glands. In addition to TB or in mpMRI-negative patients, the number of SB cores can be adjusted to prostate volume. Performing 12-core SB alone in ≥60 cm3 glands results in inadequate sampling and potential PCa underdiagnosis.

Machine learning-based prediction of upgrading on magnetic resonance imaging targeted biopsy in patients eligible for active surveillance

OBJECTIVE

To examine the ability of machine learning methods to predict upgrading of Gleason score on confirmatory magnetic resonance imaging-guided targeted biopsy (MRI-TB) of the prostate in candidates for active surveillance.

SUBJECTS AND METHODS

Our database included 592 patients who received prostate multiparametric magnetic resonance imaging in the evaluation for active surveillance. Upgrading to significant prostate cancer on MRI-TB was defined as upgrading to G 3+4 (definition 1 - DF1) and 4+3 (DF2). Machine learning classifiers were applied on both classification problems DF1 and DF2.

RESULTS

Univariate analysis showed that older age and the number of positive cores on pre-MRI-TB were positively correlated with upgrading by DF1 (P-value ≤ 0.05). Upgrading by DF2 was positively correlated with age and the number of positive cores and negatively correlated with body mass index. For upgrading prediction, the AdaBoost model was highly predictive of upgrading by DF1 (AUC 0.952), while for prediction of upgrading by DF2, the Random Forest model had a lower but excellent prediction performance (AUC 0.947).

CONCLUSION

We show that machine learning has the potential to be integrated in future diagnostic assessments for patients eligible for AS. Training our models on larger multi-institutional databases is needed to confirm our results and improve the accuracy of these models' prediction.

Rethinking prostate cancer screening: could MRI be an alternative screening test?

In the past decade rigorous debate has taken place about population-based screening for prostate cancer. Although screening by serum PSA levels can reduce prostate cancer-specific mortality, it is unclear whether the benefits outweigh the risks of false-positive results and overdiagnosis of insignificant prostate cancer, and it is not recommended for population-based screening. MRI screening for prostate cancer has the potential to be analogous to mammography for breast cancer or low-dose CT for lung cancer. A number of potential barriers and technical challenges need to be overcome in order to implement such a programme. We discuss different approaches to MRI screening that could address these challenges, including abbreviated MRI protocols, targeted MRI screening, longer rescreening intervals and a multi-modal screening pathway. These approaches need further investigation, and we propose a phased stepwise research framework to ensure proper evaluation of the use of a fast MRI examination as a screening test for prostate cancer.

The Institutional Learning Curve of Magnetic Resonance Imaging-Ultrasound Fusion Targeted Prostate Biopsy: Temporal Improvements in Cancer Detection in 4 Years

PURPOSE

While magnetic resonance imaging-ultrasound fusion targeted biopsy allows for improved detection of clinically significant prostate cancer, a concerning amount of clinically significant disease is still missed. We hypothesized that a number of these misses are due to the learning curve associated with magnetic resonance imaging-ultrasound fusion targeted biopsy. We report the results of repeat magnetic resonance imaging-ultrasound fusion targeted biopsy in men with continued suspicion for cancer and the institutional learning curve in the detection of clinically significant prostate cancer with time.

MATERIALS AND METHODS

We analyzed the records of 1,813 prostate biopsies in a prospectively acquired cohort of men who presented for prostate biopsy in a 4-year period. All men were offered prebiopsy magnetic resonance imaging and were assigned a maximum PI-RADS™ (Prostate Imaging Reporting and Data System version 2) score. Biopsy outcomes in men with a suspicious region of interest were compared. The relationship between time and clinically significant prostate cancer detection was analyzed.

RESULTS

The clinically significant prostate cancer detection rate increased 26% with time in men with a PI-RADS 4/5 region of interest. On repeat magnetic resonance imaging-ultrasound fusion targeted biopsy in men with continued suspicion for cancer 53% of those with a PI-RADS 4/5 region of interest demonstrated clinically significant discordance from the initial magnetic resonance imaging-ultrasound fusion targeted biopsy compared to only 23% with a PI-RADS 1/2 region of interest. Significantly less clinically significant prostate cancer was missed or under graded in the most recent biopsies compared to the earliest biopsies.

CONCLUSIONS

The high upgrade rate on repeat magnetic resonance imaging-ultrasound fusion targeted biopsy and the increasing cancer detection rate with time show the significant learning curve associated with magnetic resonance imaging-ultrasound fusion targeted biopsy. Men with low risk or negative biopsies with a persistent, concerning region of interest should be promptly rebiopsied. Improved targeting accuracy with operator experience can help decrease the number of missed cases of clinically significant prostate cancer.

Predicting Benign Prostate Pathology on Magnetic Resonance Imaging/Ultrasound Fusion Biopsy in Men with a Prior Negative 12-core Systematic Biopsy: External Validation of a Prognostic Nomogram

BACKGROUND

Magnetic resonance imaging (MRI) of the prostate after a prior negative biopsy may reduce the need for unnecessary repeat biopsies.

OBJECTIVE

To externally validate a previously developed nomogram predicting benign prostate pathology on MRI/ultrasound (US) fusion-targeted biopsy in men with a Prostate Imaging Reporting and Data System (PI-RADS) 3-5 region of interest and a prior negative 12-core systematic biopsy, and update this nomogram to improve its performance.

DESIGN, SETTING, AND PARTICIPANTS

A total of 2063 men underwent MRI/US fusion-targeted biopsy from April 2012 to September 2017; 104 men with a negative systematic biopsy followed by MRI-US fusion-targeted biopsy of a PI-RADS 3-5 region of interest (58%) met the study inclusion criteria.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

An MRI-based nomogram that had previously been developed in a multi-institutional clinical setting was externally validated. Predictive characteristics were age, prostate volume, MRI PI-RADS score, and prostate-specific antigen (PSA). Bayesian logistic regression was used to update the previous model.

RESULTS AND LIMITATIONS

Median age of the external validation cohort was 68 yr, PSA was 7.2ng/ml, and biopsy confirmed benign pathology in 30% (n=31), suggesting a lower baseline risk compared with the nomogram development cohort. Receiver operating characteristic curve analysis showed areas under curve (AUCs) from 0.77 to 0.80 for nomogram validation. An updated model was constructed with improved calibration and similar discrimination (AUC 0.79).

CONCLUSIONS

Age, prostate volume, PI-RADS, and PSA predict benign pathology on MRI/US fusion-targeted biopsy in men with a prior negative 12-core systematic biopsy. The validated and updated nomogram demonstrated high diagnostic accuracy and may further aid in the decision to avoid a biopsy in men with a prior negative biopsy.

PATIENT SUMMARY

We externally validated a clinically useful tool that predicts benign prostate pathology on magnetic resonance imaging/ultrasound fusion-targeted biopsy in men with a prior negative 12-core systematic biopsy and updated this predictive tool to improve its performance in patient counseling regarding the need for a repeat biopsy.

Prediction of Prostate Cancer Risk Among Men Undergoing Combined MRI-targeted and Systematic Biopsy Using Novel Pre-biopsy Nomograms That Incorporate MRI Findings

OBJECTIVE

To develop nomograms that predict the probability of overall prostate cancer (PCa) and clinically significant PCa (Gleason ≥7) on magnetic resonance imaging (MRI)-targeted, and combined MRI-targeted and systematic, prostate biopsy.

MATERIALS AND METHODS

From June 2012 to August 2014, magnetic resonance imaging to ultrasound fusion-targeted prostate biopsy was performed on 464 men with suspicious regions identified on pre-biopsy 3T MRI along with systematic 12 core biopsy. Logistic regression modeling was used to evaluate predictors of overall and clinically significant PCa, and corresponding nomograms were generated for men who were not previously biopsied or had 1 or more prior negative biopsies. Models were created with 70% of a randomly selected training sample and bias-corrected using bootstrap resampling. The models were then validated with the remaining 30% testing sample pool.

RESULTS

A total of 459 patients were included for analysis (median age 66 years, prostate-specific antigen [PSA] 5.2 ng/mL, prostate volume 49 cc). Independent predictors of PCa on targeted and systematic prostate biopsy were PSA density, age, and MRI suspicion score. PCa probability nomograms were generated for each cohort using the predictors. Bias-corrected areas under the receiver-operating characteristic curves for overall and clinically significant PCa detection were 0.82 (0.78) and 0.91 (0.84) for men without prior biopsy and 0.76 (0.65) and 0.86 (0.87) for men with a prior negative biopsy in the training (testing) samples.

CONCLUSION

PSA density, age, and MRI suspicion score predict PCa on combined MRI-targeted and systematic biopsy. Our generated nomograms demonstrate high diagnostic accuracy and may further aid in the decision to perform biopsy in men with clinical suspicion of PCa.

MRI-fusion biopsy: the contemporary experience

Advancements in magnetic resonance imaging (MRI) and MRI-ultrasound (US)-fusion targeted biopsy have resulted in a paradigm shift in the diagnosis of prostate cancer by overcoming the limitations of systematic biopsy. Prebiopsy MRI and MRI-US-fusion biopsy results in an increased detection of clinically significant disease, reduction in the detection of indolent disease, and allows for tumor localization during targeted biopsy. With these advantages, we have adopted a prebiopsy MRI and MRI-US-fusion biopsy diagnostic care pathway for all men at risk for prostate cancer and have performed more than 1900 biopsies to date. Herein we present our institutional development of MRI-US-fusion biopsy and highlight our results in those men who have had a previous negative biopsy, no prior biopsy, and those with a prior cancer diagnosis who may be candidate for active surveillance. Risk stratification with biomarkers and nomograms may allow for further counseling on the need for biopsy and the risk of harboring clinically significant disease.

Proposed Adjustments to PI-RADS Version 2 Decision Rules: Impact on Prostate Cancer Detection

Purpose To test the impact of existing Prostate Imaging Reporting and Data System (PI-RADS) version 2 (V2) decision rules, as well as of proposed adjustments to these decision rules, on detection of Gleason score (GS) 7 or greater (GS ≥7) prostate cancer. Materials and Methods Two radiologists independently provided PI-RADS V2 scores for the dominant lesion on 343 prostate magnetic resonance (MR) examinations. Diagnostic performance for GS ≥7 tumor was assessed by using MR imaging-ultrasonography fusion-targeted biopsy as the reference. The impact of existing PI-RADS V2 decision rules, as well as a series of exploratory proposed adjustments, on the frequency of GS ≥7 tumor detection, was evaluated. Results A total of 210 lesions were benign, 43 were GS 6, and 90 were GS ≥7. Lesions were GS ≥7 in 0%-4.1% of PI-RADS categories 1 and 2, 11.4%-27.1% of PI-RADS category 3, 44.4%-49.3% of PI-RADS category 4, and 72.1%-73.7% of PI-RADS category 5 lesions. PI-RADS category 4 or greater had sensitivity of 78.9%-87.8% and specificity of 75.5%-79.1 for detecting GS ≥7 tumor. The frequency of GS ≥7 tumor for existing PI-RADS V2 decision rules was 30.0%-33.3% in peripheral zone (PZ) lesions upgraded from category 3 to 4 based on dynamic contrast enhancement (DCE) score of positive; 50.0%-66.7% in transition zone (TZ) lesions upgraded from category 3 to 4 based on diffusion-weighted imaging (DWI) score of 5; and 71.7%-72.7% of lesions in both zones upgraded from category 4 to 5 based on size of 15 mm or greater. The frequency of GS ≥7 tumor for proposed adjustments to the decision rules was 30.0%-60.0% for TZ lesions upgraded from category 3 to 4 based on DWI score of 4; 33.3%-57.1% for TZ lesions upgraded from category 3 to 4 based on DCE score of positive when incorporating new criteria (unencapsulated sheetlike enhancement) for DCE score of positive in TZ; and 56.4%-61.9% for lesions in both zones upgraded from category 4 to 5 based on size of 10-14 mm. Other proposed adjustments yielded GS ≥7 tumor in less than 15% of cases for one or more readers. Conclusion Existing PI-RADS V2 decision rules exhibited reasonable performance in detecting GS ≥7 tumor. Several proposed adjustments to the criteria (in TZ, upgrading category 3 to 4 based on DWI score of 4 or modified DCE score of positive; in PZ or TZ, upgrading category 4 to 5 based on size of 10-14 mm) may also have value for this purpose. ^© RSNA, 2016 Online supplemental material is available for this article.