Background/Objectives: Extracting spatial features (texture analysis) from dose distributions (dosiomics) for outcome prediction is a rapidly evolving field in radiotherapy. To account for fraction size differences, the biological effective dose (BED) is often calculated. We evaluated the impact and added value of the BED in the dosiomics prediction modelling of grade ≥ 2 late rectal bleeding (LRB) probability within 5 years after treatment in three parts. Methods: For N = 656 prostate cancer patients previously treated in a randomized trial with conventional (CF) or hypofractionated (HF) radiotherapy, 42 dosiomic features were extracted from the dose distributions of the delineated rectum in physical doses and from dose distributions converted to the BED. Part 1: To assess whether an HF BED dosiomics model is generalizable to CF and vice versa, multivariate logistic regression BED models were constructed for HF and CF separately and tested on the other fractionation scheme. Part 2: The BED models were fitted to combined HF and CF data together to test whether this resulted in better models. Part 3: Separate physical HF and CF models were constructed and compared to the BED models. Results: Part 1: Dosiomics related to large-zone and long-run high-dose levels were predictive for both HF and CF. Deviation from the mean gray level was only predictive for HF. The BED HF model calibrations with CF data and vice versa were generally poor. AUCs ranged from 0.55 to 0.65. Part 2: Compared to the separate models, the models fitted to the combined HF and CF data showed better discriminative ability in CF but not in HF. Part 3: The apparent performances of models for the BED and physical dose were similar. Conclusions: Using the BED in the predictive dosiomic modelling of late rectal bleeding after prostate cancer radiotherapy to account for differences in fraction doses was of limited value.
背景/目的：从剂量分布中提取空间特征（纹理分析）用于预后预测（剂量组学）是放射治疗领域一个快速发展的方向。为考虑分次剂量差异，常计算生物有效剂量（BED）。本研究通过三个部分，评估了BED在预测前列腺癌放疗后5年内≥2级晚期直肠出血（LRB）概率的剂量组学建模中的影响与附加价值。方法：对先前一项随机试验中接受常规分割（CF）或大分割（HF）放疗的656例前列腺癌患者，从勾画直肠的物理剂量分布及转换为BED的剂量分布中分别提取42个剂量组学特征。第一部分：为评估HF的BED剂量组学模型能否推广至CF（反之亦然），分别构建HF和CF的多元逻辑回归BED模型，并在另一种分割方案数据中进行验证。第二部分：将BED模型同时拟合至合并的HF与CF数据，检验是否可获得更优模型。第三部分：分别构建物理剂量的HF与CF模型，并与BED模型进行比较。结果：第一部分：与大范围及长程高剂量水平相关的剂量组学特征对HF和CF均具预测价值；而灰度均值偏差仅对HF有预测作用。BED-HF模型在CF数据中的校准效果普遍较差（反之亦然），AUC值范围为0.55-0.65。第二部分：相较于独立模型，合并HF与CF数据构建的模型在CF中显示出更好的判别能力，但在HF中未体现优势。第三部分：BED模型与物理剂量模型的表观性能相近。结论：在前列腺癌放疗后晚期直肠出血的预测性剂量组学建模中，采用BED以考虑分次剂量差异的价值有限。
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