PURPOSE: To assess the incremental value of endorectal magnetic resonance (MR) imaging findings in addition to clinical variables for prediction of extracapsular extension (ECE) in patients with prostate cancer. MATERIALS AND METHODS: In this cohort study, 344 consecutive patients with biopsy-proved prostate cancer underwent endorectal MR imaging prior to surgery; 216 of these patients also underwent MR spectroscopic imaging. MR images were interpreted by 10 attending radiologists. The likelihood of ECE was scored retrospectively on the basis of MR imaging reports. Clinical variables included serum prostate-specific antigen (PSA) level, Gleason score, clinical stage of tumor, greatest percentage of cancer in all core biopsy specimens, percentage of cancer-positive core specimens in all core biopsy specimens, and presence of perineural invasion. For data analysis, receiver operating characteristic (ROC) curves and univariate and multivariate logistic regression analyses were used. Jackknife analysis was used for prediction of probability from a model that included clinical variables as tested comparatively with a model that included the clinical variables plus endorectal MR imaging findings. A difference with P <.05 was considered significant. RESULTS: At univariate analysis, all variables were associated with ECE. At ROC univariate analysis, endorectal MR imaging findings had the largest area under the ROC curve. At multivariate analysis, serum PSA level, percentage of cancer in all core biopsy specimens, and endorectal MR imaging findings (P =.001, P =.001, and P <.001, respectively) were predictors of ECE. Areas under ROC curve for two models, with and without endorectal MR imaging findings, were 0.838 and 0.772, respectively (P =.022). CONCLUSION: A model containing endorectal MR imaging findings has a significantly larger area under the ROC curve than a model containing only clinical variables; thus, endorectal MR imaging findings add incremental value in the prediction of ECE. Copyright RSNA, 2004
PURPOSE: To assess the incremental value of endorectal magnetic resonance (MR) imaging findings in addition to clinical variables for prediction of extracapsular extension (ECE) in patients with prostate cancer. MATERIALS AND METHODS: In this cohort study, 344 consecutive patients with biopsy-proved prostate cancer underwent endorectal MR imaging prior to surgery; 216 of these patients also underwent MR spectroscopic imaging. MR images were interpreted by 10 attending radiologists. The likelihood of ECE was scored retrospectively on the basis of MR imaging reports. Clinical variables included serum prostate-specific antigen (PSA) level, Gleason score, clinical stage of tumor, greatest percentage of cancer in all core biopsy specimens, percentage of cancer-positive core specimens in all core biopsy specimens, and presence of perineural invasion. For data analysis, receiver operating characteristic (ROC) curves and univariate and multivariate logistic regression analyses were used. Jackknife analysis was used for prediction of probability from a model that included clinical variables as tested comparatively with a model that included the clinical variables plus endorectal MR imaging findings. A difference with P <.05 was considered significant. RESULTS: At univariate analysis, all variables were associated with ECE. At ROC univariate analysis, endorectal MR imaging findings had the largest area under the ROC curve. At multivariate analysis, serum PSA level, percentage of cancer in all core biopsy specimens, and endorectal MR imaging findings (P =.001, P =.001, and P <.001, respectively) were predictors of ECE. Areas under ROC curve for two models, with and without endorectal MR imaging findings, were 0.838 and 0.772, respectively (P =.022). CONCLUSION: A model containing endorectal MR imaging findings has a significantly larger area under the ROC curve than a model containing only clinical variables; thus, endorectal MR imaging findings add incremental value in the prediction of ECE. Copyright RSNA, 2004
Authors: Julie C Bulman; Robert Toth; Amish D Patel; B Nicolas Bloch; Colm J McMahon; Long Ngo; Anant Madabhushi; Neil M Rofsky Journal: Radiology Date: 2012-01 Impact factor: 11.105