Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.

目的。4D-CT 是接受立体定向放射治疗的肺癌患者的常规成像。没有研究调查放射组学的最佳 4D 相位选择。我们的目标是确定应如何使用相位数据来识别远处失败的预后生物标志物，并测试是否需要进行稳定性评估。将开发一种相位选择方法来帮助使用不同 4D 协议的研究并考虑患者差异。方法. 从 258 名患者的所有阶段的肿瘤和肿瘤周围提取了 186 个特征。使用四种方法从相位特征中选择特征值：(A) 跨相位平均值，(B) 跨相位中值，(C) 50% 相位，以及 (D) 最稳定的相位（值最接近两个邻居），创造个性化选择。还分析了四个级别的稳定性评估，包括：（1）所有特征，（2）跨所有阶段的稳定特征，（3）跨阶段和相邻阶段的稳定特征，以及（4）对相邻阶段进行平均的特征。针对特征类型和评估方法的十二种组合建立了临床放射组学模型。模型性能通过一致性指数 (c-index) 和来自放射组学特征的新信息的分数进行评估。结果. 最稳定的阶段跨越整个范围，但最常靠近呼气。所有的放射学特征都为远程故障预测提供了新信息。个性化模型的 c 指数最高 (0.77)，并且在未进行稳定性评估时，58% 的新信息由放射组学特征提供。结论。最稳定的阶段因患者而异，与标准方法相比，选择该阶段可提高模型性能。我们建议应该通过最小化与相邻阶段的特征差异来确定单个最稳定的阶段。对所有阶段的稳定性评估会因过度移除特征而降低性能。相反，当关注稳定性时，应该使用相邻相位的平均。这些模型表明，较高的瘤周强度预示着远处的失败。