Dose-Based Radiomic Analysis (Dosiomics) for Intensity Modulated Radiation Therapy in Patients With Prostate Cancer: Correlation Between Planned Dose Distribution and Biochemical Failure

Purpose Although radiation therapy is one of the most significant treatment modalities for localized prostate cancer, the prognostic factors for biochemical recurrence (BCR) regarding the treatment plan are unclear. We aimed to develop a novel dosiomics-based prediction model for BCR in patients with prostate cancer and clarify the correlations between the dosimetric factors and BCR. Methods and Materials This study included 489 patients with localized prostate cancer (BCR: 96; no-BCR: 393) who received intensity modulated radiation therapy. A total of 2475 dosiomic features were extracted from the dose distributions on the prostate, clinical target volume (CTV), and planning target volume. A prediction model for BCR was trained on a training cohort of 342 patients. The performance of this model was validated using the concordance index (C-index) in a validation cohort of 147 patients. Another model was constructed using clinical variables, dosimetric parameters, and radiomic features for comparisons. Kaplan-Meier curves with log-rank analysis were used to assess the univariate discrimination based on the predictive dosiomic features. Results The dosiomic feature derived from the CTV was significantly associated with BCR (hazard ratio, 0.73; 95% CI, 0.57-0.93; P = .01). Although the dosiomics model outperformed the dosimetric and radiomics models, it did not outperform the clinical model. The performance significantly improved by combining the clinical variables and dosiomic features (C-index: 0.67; 95% CI, 0.65-0.68; P < .0001). The predictive dosiomic features were used to distinguish high-risk and low-risk patients (P < .05). Conclusions The dosiomic feature extracted from the CTV was significantly correlated with BCR in patients with prostate cancer, and the dosiomics model outperformed the model with conventional dose indices. Hence, new metrics for evaluating the quality of a treatment plan are warranted. Moreover, further research should be conducted to determine whether dosiomics can be incorporated in a clinical workflow or clinical trial. Although radiation therapy is one of the most significant treatment modalities for localized prostate cancer, the prognostic factors for biochemical recurrence (BCR) regarding the treatment plan are unclear. We aimed to develop a novel dosiomics-based prediction model for BCR in patients with prostate cancer and clarify the correlations between the dosimetric factors and BCR. This study included 489 patients with localized prostate cancer (BCR: 96; no-BCR: 393) who received intensity modulated radiation therapy. A total of 2475 dosiomic features were extracted from the dose distributions on the prostate, clinical target volume (CTV), and planning target volume. A prediction model for BCR was trained on a training cohort of 342 patients. The performance of this model was validated using the concordance index (C-index) in a validation cohort of 147 patients. Another model was constructed using clinical variables, dosimetric parameters, and radiomic features for comparisons. Kaplan-Meier curves with log-rank analysis were used to assess the univariate discrimination based on the predictive dosiomic features. The dosiomic feature derived from the CTV was significantly associated with BCR (hazard ratio, 0.73; 95% CI, 0.57-0.93; P = .01). Although the dosiomics model outperformed the dosimetric and radiomics models, it did not outperform the clinical model. The performance significantly improved by combining the clinical variables and dosiomic features (C-index: 0.67; 95% CI, 0.65-0.68; P < .0001). The predictive dosiomic features were used to distinguish high-risk and low-risk patients (P < .05). The dosiomic feature extracted from the CTV was significantly correlated with BCR in patients with prostate cancer, and the dosiomics model outperformed the model with conventional dose indices. Hence, new metrics for evaluating the quality of a treatment plan are warranted. Moreover, further research should be conducted to determine whether dosiomics can be incorporated in a clinical workflow or clinical trial.