The main objective of this work is to explore the possibility of incorporating radiomic information from multiple lesions into survival models. We hypothesise that when more lesions are present, their inclusion can improve model performance, and we aim to find an optimal strategy for using multiple distinct regions in modelling. The idea of using multiple regions of interest (ROIs) to extract radiomic features for predictive models has been implemented in many recent works. However, in almost all studies, analogous regions were segmented according to particular criteria for all patients -- for example, the primary tumour and peritumoral area, or subregions of the primary tumour. They can be included in a model in a straightforward way as additional features. A more interesting scenario occurs when multiple distinct ROIs are present, such as multiple lesions in a regionally disseminated cancer. Since the number of such regions may differ between patients, their inclusion in a model is non-trivial and requires additional processing steps. We proposed several methods of handling multiple ROIs representing either ROI or risk aggregation strategy, compared them to a published one, and evaluated their performance in different classes of survival models in a Monte Carlo Cross-Validation scheme. We demonstrated the effectiveness of the methods using a cohort of 115 non-small cell lung cancer patients, for whom we predicted the metastasis risk based on features extracted from PET images in original resolution or interpolated to CT image resolution. For both feature sets, incorporating all available lesions, as opposed to a singular ROI representing the primary tumour, allowed for considerable improvement of predictive ability regardless of the model.

翻译：本研究的主要目标是探索将来自多个病灶的影像组学信息纳入生存模型的可能性。我们假设当存在更多病灶时，纳入这些信息能够提升模型性能，并旨在寻找在建模中使用多个不同区域的最佳策略。利用多个兴趣区域提取影像组学特征用于预测模型的想法已在近期许多工作中得到实现。然而，在几乎所有研究中，都是根据特定标准为所有患者分割出类似的区域——例如，原发肿瘤及瘤周区域，或原发肿瘤的子区域。这些区域可以作为附加特征以直接的方式纳入模型。当存在多个不同的兴趣区域时，例如区域播散性癌症中的多个病灶，情况则更为复杂。由于此类区域的数量可能因患者而异，将其纳入模型并非易事，需要额外的处理步骤。我们提出了几种处理多兴趣区域的方法，分别代表区域兴趣聚合或风险聚合策略，将其与已发表的一种方法进行比较，并在蒙特卡洛交叉验证方案中评估了它们在不同类别生存模型中的性能。我们利用一个包含115名非小细胞肺癌患者的队列验证了这些方法的有效性，基于从原始分辨率或插值至CT图像分辨率的PET图像中提取的特征，我们预测了患者的转移风险。对于两组特征集，与仅使用代表原发肿瘤的单一兴趣区域相比，无论采用何种模型，纳入所有可用病灶信息均能显著提升预测能力。