Article

Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer

Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.
BJU International (Impact Factor: 3.13). 09/2011; 109(9):1315-22. DOI: 10.1111/j.1464-410X.2011.10612.x
Source: PubMed

ABSTRACT OBJECTIVES To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results. We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data. Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low-risk disease who are choosing between active surveillance and definitive therapy. PATIENTS AND METHODS In total, 181 low-risk PCa patients (clinical stage T1c-T2a, prostate-specific antigen level <10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery. For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa). Insignificant PCa was defined on surgical pathology. There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+. Prediction accuracy was assessed using areas under receiver operator characteristic curves. RESULTS At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients. For both readers, all magnetic resonance models performed significantly better than the base clinical model (P <= 0.05 for all) and similarly to the more comprehensive clinical model. CONCLUSIONS Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated. All MR-inclusive models performed significantly better than the base clinical model.

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