Is Systematic Biopsy Mandatory in All MRI-Guided Fusion Prostate Biopsies? A Machine Learning Prediction Model
Omer Longo, Gil Raviv, Miki Haifler

TL;DR
A machine learning model can predict when systematic prostate biopsies are unnecessary, potentially reducing risks and costs while maintaining cancer detection rates.
Contribution
A novel machine learning model predicts when systematic biopsy is unnecessary in MRI-guided prostate biopsies.
Findings
The model achieved an area under the curve of 0.82 and a negative predictive value of 0.92.
Prostate volume, PSA density, age, and PSA were the most important predictors.
Omitting systematic biopsies based on the model would miss clinically significant cancer in 0.7% of cases.
Abstract
Prostate biopsies usually include targeted samples from magnetic resonance imaging lesions plus systematic samples across the prostate. Systematic sampling can detect extra cancers, but it adds needle cores and may increase bleeding, infection, discomfort, and costs, and often does not change treatment because decisions depend on the highest-risk cancer found. The authors analyzed 529 men who underwent both methods and built a machine-learning model from routine pre-biopsy information, including age, prostate size, prostate-specific antigen, and prostate-specific antigen density, to predict when systematic cores would contain a higher-risk cancer than targeted cores. In internal testing, the model showed good discrimination (area under the curve of 0.82) and a high negative predictive value (0.92); if systematic cores were omitted when the model predicted no benefit, clinically…
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Taxonomy
TopicsProstate Cancer Diagnosis and Treatment · Prostate Cancer Treatment and Research · Advanced Radiotherapy Techniques
