Radiation oncology (London, England) · 2025
Dosiomic predictors of biochemical failure in patients with localized prostate cancer treated with Iodine-125 low-dose-rate brachytherapy.
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Nakano Masahiro, Kaji Shizuo, Kawakami Shogo, Tsumura Hideyasu, Imae Toshikazu, Tanaka Yuichi, Fujii Kyohei, Kainuma Takuro, Yamazaki Ryosuke, Uchida Ayaka, Kaneko Hijiri, Fujino Mako, Hata Chizu, Murakami Yu, Hashimoto Masatoshi, Ishiyama Hiromichi
Аннотация
BACKGROUND: This study aimed to identify dosiomic features that have a significant impact on biochemical failure (BCF) following low-dose rate (LDR) brachytherapy treatment using Iodine-125 seeds for prostate cancer and to provide insights into LDR brachytherapy treatment efficacy using a dosiomic approach.
METHODS: Between January 2005 and February 2015, 1,205 patients with localized prostate cancer underwent Iodine-125 seed implantation without combined external irradiation. A total of 96 patients were selected for this study, including 48 with BCF and 48 without BCF. The patients were divided into two cohorts: derivation and validation. Dose distribution images (DDs) were calculated from computed tomography (CT) images taken one month after implantation. A total of 1,130 dosiomic features, including shape-and-size, histogram, and texture features, were extracted from these DDs, their wavelet-transformed images, and Laplacian-of-Gaussian (LoG)-filtered images. The features obtained were categorized into three groups: shape-and-size (S), histogram (H), and texture (T). The Boruta algorithm was used to eliminate less important features. Two analyses were performed: Analysis A performed a multivariate logistic regression analysis using data from the validation cohort to identify significant features. Analysis B generated logistic regression models using derivation cohort data. The accuracy of BCF prediction was assessed using the validation cohort, with performance measured using the area under the receiver operating characteristic curve (AUC).
RESULTS: After the feature reduction process, two, two, and four features remained in the S, H, and T feature groups, respectively. In analysis A, the multivariate logistic regression identified four dominant features, two from each of the S and T groups. In analysis B, the AUC of the logistic regression prediction models using S, H, and all four features were 0.81, 0.77, and 0.86, respectively.
CONCLUSIONS: Four significant dosiomic features were identified. Notably, three features-elongation, Maximum2DDiameterRow, and wavelet-HHL_Skewness-strongly distinguished patients with favorable prognoses from others. These findings suggest that dosiomic features from postimplant CT and dose distribution may serve as effective factors for evaluating LDR brachytherapy outcomes in patients with prostate cancer.
METHODS: Between January 2005 and February 2015, 1,205 patients with localized prostate cancer underwent Iodine-125 seed implantation without combined external irradiation. A total of 96 patients were selected for this study, including 48 with BCF and 48 without BCF. The patients were divided into two cohorts: derivation and validation. Dose distribution images (DDs) were calculated from computed tomography (CT) images taken one month after implantation. A total of 1,130 dosiomic features, including shape-and-size, histogram, and texture features, were extracted from these DDs, their wavelet-transformed images, and Laplacian-of-Gaussian (LoG)-filtered images. The features obtained were categorized into three groups: shape-and-size (S), histogram (H), and texture (T). The Boruta algorithm was used to eliminate less important features. Two analyses were performed: Analysis A performed a multivariate logistic regression analysis using data from the validation cohort to identify significant features. Analysis B generated logistic regression models using derivation cohort data. The accuracy of BCF prediction was assessed using the validation cohort, with performance measured using the area under the receiver operating characteristic curve (AUC).
RESULTS: After the feature reduction process, two, two, and four features remained in the S, H, and T feature groups, respectively. In analysis A, the multivariate logistic regression identified four dominant features, two from each of the S and T groups. In analysis B, the AUC of the logistic regression prediction models using S, H, and all four features were 0.81, 0.77, and 0.86, respectively.
CONCLUSIONS: Four significant dosiomic features were identified. Notably, three features-elongation, Maximum2DDiameterRow, and wavelet-HHL_Skewness-strongly distinguished patients with favorable prognoses from others. These findings suggest that dosiomic features from postimplant CT and dose distribution may serve as effective factors for evaluating LDR brachytherapy outcomes in patients with prostate cancer.