Applying the PRECISION approach in biopsy naïve and previously negative prostate biopsy patients

Magnetic Resonance Imaging Guided Prostate Biopsy in Patients with ≥ One Negative Systematic Transrectal Ultrasound-Guided Biopsy: A Systemic Review and Meta-Analysis

The present study aimed to compare prostate cancer (PCa) and clinically significant PCa (csPCa) detection sensitivity between magnetic resonance imaging guided-biopsy (MRI-GB) and transrectal ultrasound-guided biopsy (TRUS-GB) in patients with ≥ 1 negative TRUS-GB, and to explore the additive value of TRUS-GB to MRI-GB. The meta-analysis of 18 studies demonstrated that MRI-GB had a similar sensitivity for PCa detection but a higher sensitivity for csPCa than TRUS-GB. In conclusion, there was limited value in combining TRUS-GB with MRI-GB compared with MRI-GB alone for csPCa detection in patients with one or more negative TRUS-GBs that were suspicious of having PCa.

Transrectal Ultrasound in Prostate Cancer: Current Utilization, Integration with mpMRI, HIFU and Other Emerging Applications

Transrectal ultrasound (TRUS) has been an invaluable tool in the assessment of prostate size, anatomy and aiding in prostate cancer (PCa) diagnosis for decades. Emerging techniques warrant an investigation into the efficacy of TRUS, how it compares to new techniques, and options to increase the accuracy of prostate cancer diagnosis. Currently, TRUS is used to guide both transrectal and transperineal biopsy approaches with similar cancer detection rates, but lower rates of infection have been reported with the transperineal approach, while lower rates of urinary retention are often reported with the transrectal approach. Multiparametric MRI has substantial benefits for prostate cancer diagnosis and triage such as lesion location, grading, and can be combined with TRUS to perform fusion biopsies targeting specific lesions. Micro-ultrasound generates higher resolution images that traditional ultrasound and has been shown effective at diagnosing PCa, giving it the potential to become a future standard of care. Finally, high-intensity focused ultrasound focal therapy administered via TRUS has been shown to offer safe and effective short-term oncological control for localized disease with low morbidity, and the precise nature makes it a viable option for salvage and repeat therapy.

Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients

BACKGROUND

Due to the low cancer-detection rate in patients with PIRADS category 3 lesions, we created machine learning (ML) models to facilitate decision-making about whether to perform prostate biopsies or monitor clinical information without biopsy results.

METHODS

In our retrospective, single-center study, 101 eligible patients with at least one PIRADS category 3 lesion but no higher PIRADS lesions underwent MRI/US fusion biopsies between September 2017 and June 2020. Thirty additional patients were included as the validation cohort from the next chronological period from June 2020 to October 2020. Our ML research was a supervised classification problem, with a binary output based on pathological reports of cancerous or benign tissue. The clinical inputs were age, prostate-specific antigen (PSA), prostate volume, prostate-specific antigen density (PSAD), and the number of previous biopsies. The radiology-report inputs were the number of lesions, maximum lesion diameter, lesion location, and lesion zone. We subsequently removed the inputs with low importance. Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting Tree (XGBoost) were employed. From receiver operating characteristic (ROC) curves, we determined Area Under the ROC Curve (AUC), the cut-off point, and sensitivity score (recall score) to evaluate the ML-model performance.

RESULTS

Twenty-four adenocarcinoma patients had a mean age of 70 ± 5.79 years, a mean PSA of 12.42 ± 6.67 ng/ml, a mean prostate volume of 46.49 ± 23.13 ml, and a mean PSAD of 0.31 ± 0.22 ng/ml² . Seventy-seven patients with benign tissue reports had a mean age of 66.39 ± 6.66 years, a mean PSA of 11.31 ± 7.50 ng/ml, a mean prostate volume of 65.25 ± 35.88 ml, and a mean PSAD of 0.19 ± 0.13 ng/ml² . On the validation cohort, XGBoost had the best AUC of 0.76, which considered 80% sensitivity and 72% specificity at a probability cutoff of 57%. The remaining possible ML models performed worse with lesser AUC. The worst was Naïve Bayes, with AUC of 0.50.

CONCLUSIONS

ML models facilitate PIRADS 3 patient selection for MRI/US fusion biopsies. ML could optimize how we use previously known clinical risk factors to their full potential.