Search for: All records

Abstract Background Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Main text Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. Conclusions The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. Graphical Abstract 

Osteosarcomas are immune-resistant and metastatic as a result of elevated nonsense-mediated RNA decay (NMD), reactive oxygen species (ROS), and epithelial-to-mesenchymal transition (EMT). Although vitamin D has anti-cancer effects, its effectiveness and mechanism of action against osteosarcomas are poorly understood. In this study, we assessed the impact of vitamin D and its receptor (VDR) on NMD-ROS-EMT signaling in in vitro and in vivo osteosarcoma animal models. Initiation of VDR signaling facilitated the enrichment of EMT pathway genes, after which 1,25(OH) 2 D, the active vitamin D derivative, inhibited the EMT pathway in osteosarcoma subtypes. The ligand-bound VDR directly downregulated the EMT inducer SNAI2 , differentiating highly metastatic from low metastatic subtypes and 1,25(OH) 2 D sensitivity. Moreover, epigenome-wide motif and putative target gene analysis revealed the VDR’s integration with NMD tumorigenic and immunogenic pathways. In an autoregulatory manner, 1,25(OH) 2 D inhibited NMD machinery genes and upregulated NMD target genes implicated in anti-oncogenic activity, immunorecognition, and cell-to-cell adhesion. Dicer substrate siRNA knockdown of SNAI2 revealed superoxide dismutase 2 (SOD2)-mediated antioxidative responses and 1,25(OH) 2 D sensitization via non-canonical SOD2 nuclear-to-mitochondrial translocalization leading to overall ROS suppression. In a mouse xenograft metastasis model, the therapeutically relevant vitamin D derivative calcipotriol inhibited osteosarcoma metastasis and tumor growth shown for the first time. Our results uncover novel osteosarcoma-inhibiting mechanisms for vitamin D and calcipotriol that may be translated to human patients. 

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. 

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.