Predicting the outcome of radiotherapy for prostate carcinoma: a model-building strategy
E Ben-Josef1, F Shamsa, J D Forman
1Department of Radiation Oncology, Wayne State University, The Barbara Ann Karmanos Cancer Institute, Detroit, Michigan, USA.
Background:
Clinical research of prostate carcinoma could be enhanced by models that allow early and reliable prediction of outcome. In this study, the authors describe a model-building strategy and compare different models.
Methods:
The sample population was comprised of 158 patients treated definitively with radiotherapy. Univariate and multivariate logistic regression analyses were conducted to identify prognostic factors and select the best predictive model. Variables included age, race, method of diagnosis (needle biopsy vs. transurethral resection of the prostate), stage, grade, pretreatment prostate specific antigen (PSA), in-treatment PSA (PSA(tx)), posttreatment PSA (PSA(post)), and nadir PSA. The following indices were used to compare discriminatory power: log-likelihood function, Akaike information criterion, the generalized coefficient of determination, and the area under the receiver operating characteristic curve.
Results:
At last follow-up, 49 patients (31%) had recurrence of carcinoma. By univariate analysis, the failure rate was significantly higher in patients with advanced stage, higher grade, higher pretherapy PSA, and nadir PSA > 1 ng/mL (P < 0.0001). Pretherapy PSA was associated significantly with stage, age, and nadir PSA (P = 0.001, P = 0.001, and P = 0.001, respectively). All PSA measurements were significantly interrelated. Nadir PSA was the most predictive variable. Significant gains (P = 0.01) in predictive power were derived from inclusion of PSA(tx), but not PSA (post). Age, race, stage, grade, and method of diagnosis contributed predictive power in addition to that derived from PSA levels (P = 0.01, log-likelihood test). The authors' model of choice predicts outcome with an overall correctness, sensitivity, specificity, and false-negative rate of 81.8%, 87.2%, 79.6%, and 12.8%, respectively.
Conclusions:
Applying the strategy described, a model was selected that allowed accurate prediction of failure shortly after the completion of therapy.
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