Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.
Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.
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DOI：
10.1200/cci.22.00100
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发表时间：
2023-01
影响因子：
4.2
通讯作者：
Abazeed, Mohamed E.
中科院分区：
文献类型：
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作者：
Randall, James;Teo, Troy;Lou, Bin;Shah, Jainil;Patel, Jyoti;Kamen, Ali;Abazeed, Mohamed E.
我们开发了一个深度神经网络，它查询肺部计算机断层扫描衍生的特征空间，以识别辐射敏感性参数，这些参数可以预测治疗失败，从而指导放射治疗剂量的个体化。在这篇文章中，我们将研究该模型在卫生系统中的可移植性。这项基于队列的多中心登记研究纳入了1,120例接受立体定向体部放疗的肺癌患者。将来自内部研究队列（n = 849）的治疗前肺部计算机断层扫描图像输入到多任务深度神经网络中，以生成预测局部失效时间的图像指纹评分。将深度学习（DL）评分输入回归模型以获得iGray，iGray是一种个性化的辐射剂量估计，预计24个月时治疗失败概率< 5%。我们在外部坚持队列（n = 271）中验证了我们的发现。两个研究人群的基线患者特征存在实质性差异，允许评估模型的可移植性。在外部队列中，高DL评分患者的放射治疗失败率显著更高，3年局部失败累积发生率为28.5%（95% CI，19.8至37.8）vs 10.2%（95% CI，5.9至16.2;风险比，3.3 [95% CI，1.74至6.49]; P <0.001）。仅包括DL评分的模型预测治疗失败，一致性指数为0.68（95% CI，0.59 - 0.77），其性能与内部队列中的嵌套模型相似（0.70 [0.64 - 0.75]）。iGray值超过输送剂量的外部队列患者的局部失败率相应较高（P <0.001）。我们的研究结果支持在影像丰富和高度标准化的放射肿瘤学学科中开发和实施新的DL引导治疗指导工具。
We developed a deep neural network that queries the lung computed tomography–derived feature space to identify radiation sensitivity parameters that can predict treatment failures and hence guide the individualization of radiotherapy dose. In this article, we examine the transportability of this model across health systems. This multicenter cohort-based registry included 1,120 patients with cancer in the lung treated with stereotactic body radiotherapy. Pretherapy lung computed tomography images from the internal study cohort (n = 849) were input into a multitask deep neural network to generate an image fingerprint score that predicts time to local failure. Deep learning (DL) scores were input into a regression model to derive iGray, an individualized radiation dose estimate that projects a treatment failure probability of < 5% at 24 months. We validated our findings in an external, holdout cohort (n = 271). There were substantive differences in the baseline patient characteristics of the two study populations, permitting an assessment of model transportability. In the external cohort, radiation treatments in patients with high DL scores failed at a significantly higher rate with 3-year cumulative incidences of local failure of 28.5% (95% CI, 19.8 to 37.8) versus 10.2% (95% CI, 5.9 to 16.2; hazard ratio, 3.3 [95% CI, 1.74 to 6.49]; P < .001). A model that included DL score alone predicted treatment failures with a concordance index of 0.68 (95% CI, 0.59 to 0.77), which had a similar performance to a nested model derived from within the internal cohort (0.70 [0.64 to 0.75]). External cohort patients with iGray values that exceeded the delivered doses had proportionately higher rates of local failure (P < .001). Our results support the development and implementation of new DL-guided treatment guidance tools in the image-replete and highly standardized discipline of radiation oncology.