碘-125低剂量率近距离放射治疗的局限性前列腺癌患者生化失败的剂量组预测因子

IF 3.3 2区 医学 Q2 ONCOLOGY
Masahiro Nakano, Shizuo Kaji, Shogo Kawakami, Hideyasu Tsumura, Toshikazu Imae, Yuichi Tanaka, Kyohei Fujii, Takuro Kainuma, Ryosuke Yamazaki, Ayaka Uchida, Hijiri Kaneko, Mako Fujino, Chizu Hata, Yu Murakami, Masatoshi Hashimoto, Hiromichi Ishiyama
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引用次数: 0

摘要

背景:本研究旨在确定对低剂量率(LDR)近距离放射治疗前列腺癌后生化衰竭(BCF)有显著影响的剂量组学特征,并利用剂量组学方法深入了解LDR近距离放射治疗的疗效。方法:2005年1月至2015年2月,1205例局限性前列腺癌患者行碘125粒子植入术,无联合外照射。本研究共选择96例患者,其中48例合并BCF, 48例未合并BCF。患者被分为两组:衍生组和验证组。剂量分布图像(DDs)由植入后1个月的CT图像计算。从这些dd、它们的小波变换图像和拉普拉斯高斯(LoG)滤波图像中,共提取了1130个剂量组特征,包括形状和大小、直方图和纹理特征。将得到的特征分为三组:形状和大小(S)、直方图(H)和纹理(T)。采用Boruta算法剔除不太重要的特征。进行了两项分析:分析A使用验证队列的数据进行了多变量逻辑回归分析,以确定显著特征。分析B使用衍生队列数据生成逻辑回归模型。使用验证队列评估BCF预测的准确性,并使用受试者工作特征曲线下面积(AUC)来衡量效果。结果:特征约简后,S、H、T特征组分别保留2个、2个、4个特征。在分析A中,多元逻辑回归确定了四个主要特征,S组和T组各有两个。在分析B中,使用S、H和所有四个特征的logistic回归预测模型的AUC分别为0.81、0.77和0.86。结论:确定了四个显著的剂量学特征。值得注意的是,三个特征-伸长,Maximum2DDiameterRow和wavelet- hhl_skewness -强烈区分预后良好的患者。这些发现表明,移植后CT的剂量组特征和剂量分布可能是评估前列腺癌患者LDR近距离放射治疗效果的有效因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Dosiomic predictors of biochemical failure in patients with localized prostate cancer treated with Iodine-125 low-dose-rate brachytherapy.

Dosiomic predictors of biochemical failure in patients with localized prostate cancer treated with Iodine-125 low-dose-rate brachytherapy.

Dosiomic predictors of biochemical failure in patients with localized prostate cancer treated with Iodine-125 low-dose-rate brachytherapy.

Dosiomic predictors of biochemical failure in patients with localized prostate cancer treated with Iodine-125 low-dose-rate brachytherapy.

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.

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来源期刊
Radiation Oncology
Radiation Oncology ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
6.50
自引率
2.80%
发文量
181
审稿时长
3-6 weeks
期刊介绍: Radiation Oncology encompasses all aspects of research that impacts on the treatment of cancer using radiation. It publishes findings in molecular and cellular radiation biology, radiation physics, radiation technology, and clinical oncology.
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