  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.
