Advances in radiation oncology · 2025
Computed Tomography-Based Radiomics Prediction of Biochemical Failure and Distant Metastasis in Patients With High- and Very High-Risk Localized Prostate Cancer.
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Adachi Takanori, Hirata Kota, Aizawa Rihito, Hirashima Hideaki, Ogata Takashi, Nakamura Mitsuhiro, Mizowaki Takashi
Аннотация
PURPOSE: This study aims to assess the utility of radiomics features extracted from planning computed tomography (pCT) images in predicting biochemical failure (BF) and distant metastasis (DM) in patients with localized prostate cancer (PCa).
METHODS AND MATERIALS: This retrospective study included 608 patients with high-risk and very high-risk localized PCa who received intensity modulated radiation therapy. Five clinical variables-age, clinical T-stage, Gleason score, prostate-specific antigen level, and prescribed dose-were collected. Additionally, 1316 radiomics features (shape, first-order, and texture) were extracted from pCT images. Patients were randomly classified into training-validation (70%) and test (30%) cohorts, with stratification by BF and DM incidence. Predictive models were developed to estimate BF and DM risk using 3 strategies: a clinical model based on the 5 clinical variables; a radiomics model using 5 selected radiomics features; and a hybrid model combining both. All models were constructed using random survival forest with undersampling, considering death as a competing risk. Models were then applied to the test cohort, stratifying patients into high- and low-score groups by the median risk score. Model performance was evaluated using the concordance index (C-index), where values of 0.5 and 1.0 indicate random and perfect predictions, respectively, with survival differences between the 2 groups assessed using Gray's test.
RESULTS: During a median follow-up of 8.2 years, BF and DM occurred in 178 (29.3%) and 52 (8.6%) patients, respectively. In the test cohort, the C-indices for BF prediction were 0.637, 0.640, and 0.687 for the clinical, radiomics, and hybrid models, respectively (P < .05). For DM prediction, the C-indices were 0.586 (P = .203), 0.588 (P = .153), and 0.599 (P = .099), respectively.
CONCLUSIONS: Integrating clinical and pCT-based radiomics features enhanced BF prediction in patients with high-risk and very high-risk localized PCa when accounting for competing risks using random survival forest. However, no significant improvement was observed for DM prediction.
METHODS AND MATERIALS: This retrospective study included 608 patients with high-risk and very high-risk localized PCa who received intensity modulated radiation therapy. Five clinical variables-age, clinical T-stage, Gleason score, prostate-specific antigen level, and prescribed dose-were collected. Additionally, 1316 radiomics features (shape, first-order, and texture) were extracted from pCT images. Patients were randomly classified into training-validation (70%) and test (30%) cohorts, with stratification by BF and DM incidence. Predictive models were developed to estimate BF and DM risk using 3 strategies: a clinical model based on the 5 clinical variables; a radiomics model using 5 selected radiomics features; and a hybrid model combining both. All models were constructed using random survival forest with undersampling, considering death as a competing risk. Models were then applied to the test cohort, stratifying patients into high- and low-score groups by the median risk score. Model performance was evaluated using the concordance index (C-index), where values of 0.5 and 1.0 indicate random and perfect predictions, respectively, with survival differences between the 2 groups assessed using Gray's test.
RESULTS: During a median follow-up of 8.2 years, BF and DM occurred in 178 (29.3%) and 52 (8.6%) patients, respectively. In the test cohort, the C-indices for BF prediction were 0.637, 0.640, and 0.687 for the clinical, radiomics, and hybrid models, respectively (P < .05). For DM prediction, the C-indices were 0.586 (P = .203), 0.588 (P = .153), and 0.599 (P = .099), respectively.
CONCLUSIONS: Integrating clinical and pCT-based radiomics features enhanced BF prediction in patients with high-risk and very high-risk localized PCa when accounting for competing risks using random survival forest. However, no significant improvement was observed for DM prediction.