Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics – the high-throughput extraction of large amounts of image features from radiographic images – addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

The easy-to-use nomograms can predict LR, DM, and OS over a 5-year period after surgery. They may be used as decision support tools in future trials by using the three defined risk groups to select patients for postoperative chemotherapy and close follow-up (http://www.predictcancer.org).

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome—including survival, recurrence patterns and toxicity—in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.

Purpose: Conventional anticancer treatments are often impaired by the presence of hypoxia. TH-302 selectively targets hypoxic tumor regions, where it is converted into a cytotoxic agent. This study assessed the efficacy of the combination treatment of TH-302 and radiotherapy in two preclinical tumor models. The effect of oxygen modification on the combination treatment was evaluated and the effect of TH-302 on the hypoxic fraction (HF) was monitored using [ 18 F]HX4-PET imaging and pimonidazole IHC stainings. Experimental Design: Rhabdomyosarcoma R1 and H460 NSCLC tumor-bearing animals were treated with TH-302 and radiotherapy (8 Gy, single dose). The tumor oxygenation status was altered by exposing animals to carbogen (95% oxygen) and nicotinamide, 21% or 7% oxygen breathing during the course of the treatment. Tumor growth and treatment toxicity were monitored until the tumor reached four times its start volume (T4ÂSV).Results: Both tumor models showed a growth delay after TH-302 treatment, which further increased when combined with radiotherapy (enhancement ratio rhabdomyosarcoma 1.23; H460 1.49). TH-302 decreases the HF in both models, consistent with its hypoxia-targeting mechanism of action. Treatment efficacy was dependent on tumor oxygenation; increasing the tumor oxygen status abolished the effect of TH-302, whereas enhancing the HF enlarged TH-302 0 s therapeutic effect. An association was observed in rhabdomyosarcoma tumors between the pretreatment HF as measured by [ 18 F]HX4-PET imaging and the T4ÂSV.Conclusions: The combination of TH-302 and radiotherapy is promising and warrants clinical testing, preferably guided by the companion biomarker [18 F]HX4 hypoxia PET imaging for patient selection.

The SUV(max)-based RI calculated after the first 2 weeks of RCT provided the best predictor of pathological treatment response, reaching AUCs of 0.87 and 0.84 for the TRG and the ypT stage, respectively. However, a few patients presented with peritumoral inflammatory reactions, which led to mispredictions. Exclusion of these patients further enhanced the predictive accuracy of PET imaging to AUCs of 0.97 and 0.89 for TRG and ypT, respectively.

BackgroundAltered metabolism is a hallmark of cancer. However, the role of genomic changes in metabolic genes driving the tumour metabolic shift remains to be elucidated. Here, we have investigated the genomic and transcriptomic changes underlying this shift across ten different cancer types.ResultsA systematic pan-cancer analysis of 6538 tumour/normal samples covering ten major cancer types identified a core metabolic signature of 44 genes that exhibit high frequency somatic copy number gains/amplifications (>20 % cases) associated with increased mRNA expression (ρ > 0.3, q < 10−3). Prognostic classifiers using these genes were confirmed in independent datasets for breast and kidney cancers. Interestingly, this signature is strongly associated with hypoxia, with nine out of ten cancer types showing increased expression and five out of ten cancer types showing increased gain/amplification of these genes in hypoxic tumours (P ≤ 0.01). Further validation in breast and colorectal cancer cell lines highlighted squalene epoxidase, an oxygen-requiring enzyme in cholesterol biosynthesis, as a driver of dysregulated metabolism and a key player in maintaining cell survival under hypoxia.ConclusionsThis study reveals somatic genomic alterations underlying a pan-cancer metabolic shift and suggests genomic adaptation of these genes as a survival mechanism in hypoxic tumours.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0999-8) contains supplementary material, which is available to authorized users.

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.