Towards the use of multiple ROIs for radiomics-based survival modelling: finding a strategy of aggregating lesions

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.