Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.

Proton therapy (PT) is a treatment with high dose conformality that delivers a highly-focused radiation dose to solid tumors. Targeted radionuclide therapy (TRT), on the other hand, is a systemic radiation therapy, which makes use of intravenously-applied radioconjugates. In this project, it was aimed to perform an initial dose-searching study for the combination of these treatment modalities in a preclinical setting. Therapy studies were performed with xenograft mouse models of folate receptor (FR)-positive KB and prostate-specific membrane antigen (PSMA)-positive PC-3 PIP tumors, respectively. PT and TRT using ¹⁷⁷Lu-folate and ¹⁷⁷Lu-PSMA-617, respectively, were applied either as single treatments or in combination. Monitoring of the mice over nine weeks revealed a similar tumor growth delay after PT and TRT, respectively, when equal tumor doses were delivered either by protons or by β¯-particles, respectively. Combining the methodologies to provide half-dose by either therapy approach resulted in equal (PC-3 PIP tumor model) or even slightly better therapy outcomes (KB tumor model). In separate experiments, preclinical positron emission tomography (PET) was performed to investigate tissue activation after proton irradiation of the tumor. The high-precision radiation delivery of PT was confirmed by the resulting PET images that accurately visualized the irradiated tumor tissue. In this study, the combination of PT and TRT resulted in an additive effect or a trend of synergistic effects, depending on the type of tumor xenograft. This study laid the foundation for future research regarding therapy options in the situation of metastasized solid tumors, where surgery or PT alone are not a solution but may profit from combination with systemic radiation therapy.

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.