Focused ultrasound (FUS) is a non-invasive technique producing a variety of biological effects by either thermal or mechanical mechanisms of ultrasound interaction with the targeted tissue. FUS could bring benefits, e.g., tumour sensitisation, immune stimulation, and targeted drug delivery, but investigation of FUS effects at the cellular level is still missing. New techniques are commonly tested in vitro on two-dimensional (2D) monolayer cancer cell culture models. The 3D tumour model—spheroid—is mainly utilised to mimic solid tumours from an architectural standpoint. It is a promising method to simulate the characteristics of tumours in vitro and their various responses to therapeutic alternatives. This study aimed to evaluate the effects of FUS on human prostate and glioblastoma cancer tumour spheroids in vitro. The experimental follow-up enclosed the measurements of spheroid integrity and growth kinetics, DNA damage, and cellular metabolic activity by measuring intracellular ATP content in the spheroids. Our results showed that pulsed FUS treatment induced molecular effects in 3D tumour models. With the disruption of the spheroid integrity, we observed an increase in DNA double-strand breaks, leading to damage in the cancer cells depending on the cancer cell type.

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Personalized treatment strategies for oncological patient management can improve outcomes of patient populations with heterogeneous treatment response. The implementation of such a concept requires the identification of biomarkers that can precisely predict treatment outcome. In the context of this thesis, we develop and validate biomarkers from multimodal imaging data for the outcome prediction after treatment in patients with locally advanced rectal cancer (LARC) and in patients with newly diagnosed glioblastoma multiforme (GBM), using conventional feature-based radiomics and deep-learning (DL) based radiomics. For LARC patients, we identify promising radiomics signatures combining computed tomography (CT) and T2-weighted (T2-w) magnetic resonance imaging (MRI) with clinical parameters to predict tumour response to neoadjuvant chemoradiotherapy (nCRT). Further, the analyses of externally available radiomics models for LARC reveal a lack of reproducibility and the need for standardization of the radiomics process. For patients with GBM, we use postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w MRI for the detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS). We show that DL models built on MET-PET have an improved diagnostic and prognostic value as compared to MRI.