Luo Yi, McShan Daniel L, Matuszak Martha M, Ray Dipankar, Lawrence Theodore S, Jolly Shruti, Kong Feng-Ming, Ten Haken Randall K, El Naqa Issam
Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, 48103, USA.
Department of Radiation Oncology, Indiana University, Indianapolis, IN, 46202, USA.
Med Phys. 2018 Jun 4. doi: 10.1002/mp.13029.
Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning.
A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance.
Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively.
MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.
非小细胞肺癌（NSCLC）放疗中治疗结果的个体化可能因影响肿瘤局部控制（LC）和放射性肺炎（RP）等副作用的生物物理因素缺乏适当平衡而受到影响，而这些因素可能相互交织。在此，我们比较了用于适应性个性化治疗计划的单独和联合结果预测的性能。
对118例接受前瞻性方案治疗的NSCLC患者进行研究，其中32例出现局部进展，20例发生2级或更高等级的RP（RP2）。68例放疗前和放疗期间具有297个特征的患者用于探索，50例患者留作独立测试。开发了一种多目标贝叶斯网络（MO-BN）方法，以扩展马尔可夫覆盖作为输入来识别用于联合LC/RP2预测的重要特征，从而构建BN预测结构。交叉验证（CV）用于指导MO-BN结构学习。自由反应接收者操作特征（AU-FROC）曲线下面积用于评估联合预测性能。
在治疗前的MO-BN中，选择了包括单核苷酸多态性（SNP）、微小RNA、治疗前细胞因子、治疗前PET影像组学以及肺和肿瘤广义等效均匀剂量（gEUD）等重要特征，并确定了它们与放疗结果（LC和RP2）之间的生物物理相互关系。内部交叉验证时，联合LC/RP2预测的AU-FROC为0.80（95%CI：0.70-0.86）。在治疗期间的MO-BN中，通过放疗过程中增加另外两个SNP、一种细胞因子的变化和两个PET影像组学图像特征，这一结果提高到了0.85（0.75-0.91）。该MO-BN模型在治疗期间优于组合的单目标贝叶斯网络（SO-BN）[0.78（0.67-0.84）]。在测试数据集上评估MO-BN和单个SO-BN时，治疗前的AU-FROC值分别为0.77和0.68，治疗期间分别为0.79和0.71。
MO-BN可以揭示竞争性放疗临床终点之间可能的生物物理相互作用。通过提供额外的治疗期间信息可改善预测。所开发的MO-BN可以成为适应性个性化放疗决策支持系统的重要组成部分。
