UROLOGICAL SURVEY   ( Download pdf )

 

IMAGING

The Utility of Magnetic Resonance Imaging and Spectroscopy for Predicting Insignificant Prostate Cancer: an Initial Analysis
Shukla-Dave A, Hricak H, Kattan MW, Pucar D, Kuroiwa K, Chen HN, Spector J, Koutcher JA, Zakian KL, Scardino PT
Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
BJU Int. 2007; 99:786-93

  • Objective: To design new models that combine clinical variables and biopsy data with magnetic resonance imaging (MRI) and MR spectroscopic imaging (MRSI) data, and assess their value in predicting the probability of insignificant prostate cancer.
  • Patients and Methods: In all, 220 patients (cT stage T1c or T2a, prostate-specific antigen level < 20 ng/mL, biopsy Gleason score 6) had MRI/MRSI before surgery and met the inclusion criteria for the study. The probability of insignificant cancer was recorded retrospectively and separately for MRI and combined MRI/MRSI on a 0-3 scale (0, definitely insignificant; - 3, definitely significant). Insignificant cancer was defined from surgical pathology as organ-confined cancer of </= 0.5 cm (3) with no poorly differentiated elements. The accuracy of predicting insignificant prostate cancer was assessed using areas under receiver operating characteristic curves (AUCs), for previously reported clinical models and for newly generated MR models combining clinical variables, and biopsy data with MRI data (MRI model) and MRI/MRSI data (MRI/MRSI model).
  • Results: At pathology, 41% of patients had insignificant cancer; both MRI (AUC 0.803) and MRI/MRSI (AUC 0.854) models incorporating clinical, biopsy and MR data performed significantly better than the basic (AUC 0.574) and more comprehensive medium (AUC 0.726) clinical models. The P values for the differences between the models were: base vs. medium model, < 0.001; base vs. MRI model, < 0.001; base vs. MRI/MRSI model, < 0.001; medium vs. MRI model, < 0.018; medium vs. MRI/MRSI model, < 0.001.
  • Conclusions: The new MRI and MRI/MRSI models performed better than the clinical models for predicting the probability of insignificant prostate cancer. After appropriate validation, the new MRI and MRI/MRSI models might help in counseling patients who are considering choosing deferred therapy.

  • Editorial Comment
    Insignificant prostate cancer defined as pathologically organ-confined cancer with a total volume of ≤ 0.5 cm 3 and no poorly differentiated component (Gleason grade 4 or 5) on histology is not infrequent but patients with this cancer are very difficult to identify clinically. The authors presented their pioneering work emphasizing that after appropriate validation this new magnetic resonance imaging (MRI) and MRI / magnetic resonance spectroscopic imaging (MRSI) models, might improve the overall accuracy of clinical models in predicting the likelihood of insignificant prostate cancer .Information obtained with conventional MRI and with magnetic resonance spectroscopic imaging were combined with clinical variables and biopsy results in order to build this new clinical nomogram. Both MRI models and the MRI/MRSI model were more accurate than the clinical models for discriminating insignificant prostate cancer from significant prostate cancer. Since MRSI is more specific than conventional MRI for identification of prostate cancer, one could expect that the MRI/MRSI model was the most discriminating (area under the curve 0.854) and performed significantly better than MRI model alone and other clinical models. As pointed out by the authors the major limitation of the model is that they are vulnerable to upgrading of the biopsy Gleason grade after radical prostatectomy; 26% of the patients of this series had their Gleason scores upgraded. This was particularly important in 7% of the patients of this series. The authors emphasizes that their goal was not produce MRI models ready for clinical use, but rather to test the feasibility of creating such models. In our institution, we already started a prospective clinical study in order to validate this MRI/MRSI model.


Dr. Adilson Prando
Chief, Department of Radiology
Vera Cruz Hospital
Campinas, São Paulo, Brazil
E-mail: aprando@mpc.com.br