Using spatial tracking with magnetic resonance imaging/ultrasound‐guided biopsy to identify unilateral prostate cancer

Abstract

Objectives To create reliable predictive metrics of unilateral disease using spatial tracking from a fusion device, thereby improving patient selection for hemi‐gland ablation of prostate cancer. Patients and Methods We identified patients who received magnetic resonance imaging (MRI)/ultrasound‐guided biopsy and radical prostatectomy at a single institution between 2011 and 2018. In addition to standard clinical features, we extracted quantitative features related to biopsy core and MRI target locations predictive of tumour unilaterality. Classification and Regression Tree (CART) analysis was used to create a decision tree (DT) for identifying cancer laterality. We evaluated concordance of model‐determined laterality with final surgical pathology. Results A total of 173 patients were identified with biopsy coordinates and surgical pathology available. Based on CART analysis, in addition to biopsy‐ and MRI‐confirmed disease unilaterality, patients should be further screened for cancer detected within 7 mm of midline in a 40 mL prostate, which equates to the central third of any‐sized prostate by radius. The area under the curve for this DT was 0.82. Standard diagnostics and the DT correctly identified disease laterality in 73% and 80% of patients, respectively (P = 0.13). Of the patients identified as unilateral by standard diagnostics, 47% had undetected contralateral disease or were otherwise incorrectly identified. This error rate was reduced to 17% (P = 0.01) with the DT. Conclusion Using spatial tracking from fusion devices, a DT was more reliable for identifying laterality of prostate cancer compared to standard diagnostics. Patients with cancer detected within the central third of the prostate by radius are poor hemi‐gland ablation candidates due to the risk of midline extension of tumour.

Can artificial intelligence optimize case selection for hemi‐gland ablation?

“…In this issue of BJUI , the study by Zhou et al . is the first to utilize AI to optimize case selection for hemi‐gland ablation. In this study, classification and regression tree (CART) analysis, which is a form of supervised machine‐learning algorithm, was used to identify laterality of prostate cancer.…”