仅用器官体积就能精准预测前列腺放疗中直肠和膀胱的剂量分布。
Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
- 基于器官体积与重叠区域，用机器学习预测剂量-体积曲线。
- 在4000–6420cGy范围内，误差低至1.7%–3.7%。
- 新提出的模糊规则模型在关键剂量点误差低于1.6%，适合临床快速规划。
剂量预测是放疗计划中的持续研究方向。目前多数商业模型依赖影像数据与大量计算资源。本研究旨在仅通过靶区、危及器官及其重叠区域的体积参数，利用机器学习预测直肠和膀胱的剂量-体积曲线。收集了94例前列腺癌患者（接受6000cGy/20次分次放疗）的治疗计划系统导出文本数据，构建训练集。对比多种统计建模与机器学习方法，并验证了一种新的模糊规则预测模型（FRBP）在39例独立患者数据上的表现。在4000–6420cGy范围内，膀胱与直肠的中位绝对误差分别为2.0%–3.7%和1.7%–2.4%；在5300、5600和6000cGy剂量水平下，直肠误差均低于2.5%，膀胱低于3.8%。FRBP模型在上述剂量点对直肠和膀胱的误差分别为1.2%、1.3%、0.9%和1.6%、1.2%、0.1%。结果表明，仅凭结构体积即可准确预测临床上关键的剂量-体积参数。
原文摘要 · Abstract (English)
Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using just the volumes of these structures.
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