A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
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DOI：
10.1038/s41598-018-37741-x
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发表时间：
2019-01-31
影响因子：
4.6
通讯作者：
Jiang, Steve
中科院分区：
文献类型：
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作者：
Dan Nguyen;Long, Troy;Jiang, Steve
随着癌症患者放射治疗方式的进步，结果有所改善，但代价是治疗计划的复杂性和计划时间的增加。准确的剂量分布预测将通过指导临床计划优化来节省时间并保持高质量的计划，从而缓解这一问题。我们已经修改了卷积深度网络模型U-Net(最初设计用于分割目的)，用于根据计划目标体积(PTV)和危险器官(OAR)的患者图像轮廓来预测剂量。作为一个例子，我们表明，我们能够准确地预测前列腺癌患者的调强放射治疗(IMRT)的剂量，当比较预测的与真实的等剂量体积在0%和100%的处方剂量之间时，Dice相似系数的平均值为0.91。[最大，平均]剂量绝对差值的平均值小于处方剂量的5%，具体为：[1.80%，1.03%](PTV)，[1.94%，4.22%](膀胱)，[1.80%，0.48%](身体)，[3.87%，1.79%](L股骨头)，[5.07%，2.55%](右侧股骨头)，[1.26%，1.62%](直肠)。因此，我们成功地根据患者的PTV和OAR等高线绘制了所需的辐射剂量分布。作为一个额外的优点，在本文描述的技术和模型中使用的数据相对较少。
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.