Radiomics

Cancer is a heterogeneous disease with respect to etiology, pathogenesis, therapy response and prognosis. Tumor response to therapy varies not only among patients but also within the tumor itself. Today, increasing number of cancer treatment options are available due to rapid technical developments. Therefore, decision support systems are needed to offer the right treatment to the right patient.

One possibility to optimize treatment strategies is the identification of biomarkers. In recent years, imaging has become increasingly important due to its non-invasive nature for the identification of new prognostic biomarkers. Imaging datasets are expected to hold more information than visible to the human eye. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography, positron emission tomography or magnetic resonance imaging. Using our in-house developed radiomics software (Z-Rad) we can extract more than 1000 radiomic features describing tumor shape, tumor intensity, tumor texture from medical images (figure). Based on mathematical definitions we investigate tumor morphology as well as the prominent perceptual texture characteristics such as regularity (or periodicity), directionality and complexity. These radiomic features are potential biomarkers of the cancer phenotype, and hence can be used for patient outcome prognosis or for correlation to the tumor biology using advanced statistical methods.

For an introduction to our work, you may enjoy this video!

Our group is located in the radiation therapy department at the University Hospital of Zurich (USZ) which allows us to work in a highly interdisciplinary setting with the medical doctors and radiation biologists to address clinical needs. Our research focuses on:

• the robustness of radiomic features against scanning and imaging uncertainties
• the correlation of radiomic features to clinical parameters or patient outcome modeling to stratify patients into different risk groups for different tumor types (such as brain, head and neck, melanoma and lung)
• the correlation of radiomic features with tumor biology (such Gleason score or Human Papillomavirus)
• the translation of preclinical radiomic signatures to clinical setting
• the repetitive tumor and organs art risk monitoring using the concept of delta radiomics (time variation in radiomic features) and recently installed MRI-Linac
• the implementation of deep learning concepts for medical image analysis and outcome modeling
• Privacy preserving distributed learning for outcome modelling

1. Vuong D, Tanadini-Lang S, Huellner MW, Veit-Haibach P, Unkelbach J, Andratschke N, Kraft J, Guckenberger M, Bogowicz M. Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR. Med Phys. 2019 Apr;46(4):1677-1685
2. Tanadini-Lang S, Bogowicz M, Veit-Haibach P, et al. Exploratory Radiomics in Computed Tomography Perfusion of Prostate Cancer. Anticancer research. 2018;38(2):685-690.
3. Pavic M, Bogowicz M, Würms X, et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncologica. 2018:1-5.
4. Bogowicz M, Riesterer O, Stark LS, et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncol. 2017;56(11):1531-1536.
5. Bogowicz M, Riesterer O, Ikenberg K, et al. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. International journal of radiation oncology, biology, physics. 2017;99(4):921-928.
6. Bogowicz M, Leijenaar RTH, Tanadini-Lang S, et al. Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models. Radiother Oncol. 2017;125(3):385-391.
7. Nesteruk M, Lang S, Veit-Haibach P, et al. Tumor stage, tumor site and HPV dependent correlation of perfusion CT parameters and [18F]-FDG uptake in head and neck squamous cell carcinoma. Radiotherapy and Oncology. 2015;117(1):125-131.