Imaging-based representation and stratification of intra-tumor heterogeneity via tree-edit distance.

ABSTRACT: Personalized medicine is the future of medical practice. In oncology, tumor heterogeneity assessment represents a pivotal step for effective treatment planning and prognosis prediction. Despite new procedures for DNA sequencing and analysis, non-invasive methods for tumor characterization are needed to impact on daily routine. On purpose, imaging texture analysis is rapidly scaling, holding the promise to surrogate histopathological assessment of tumor lesions. In this work, we propose a tree-based representation strategy for describing intra-tumor heterogeneity of patients affected by metastatic cancer. We leverage radiomics information extracted from PET/CT imaging and we provide an exhaustive and easily readable summary of the disease spreading. We exploit this novel patient representat

SUBMITTER: Cavinato L 

PROVIDER: S-EPMC9666477 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature