The history of radiotherapy is intertwined with research on hypoxia. There is level 1a evidence that giving hypoxia-targeting treatments with radiotherapy improves locoregional control and survival without compromising late side-effects. Despite coming in and out of vogue over decades, there is now an established role for hypoxia in driving molecular alterations promoting tumour progression and metastases. While tumour genomic complexity and immune profiling offer promise, there is a stronger evidence base for personalising radiotherapy based on hypoxia status. Despite this, there is only one phase III trial targeting hypoxia modification with full transcriptomic data available. There are no biomarkers in routine use for patients undergoing radiotherapy to aid management decisions, and a roadmap is needed to ensure consistency and provide a benchmark for progression to application. Gene expression signatures address past limitations of hypoxia biomarkers and could progress biologically optimised radiotherapy. Here, we review recent developments in generating hypoxia gene expression signatures and highlight progress addressing the challenges that must be overcome to pave the way for their clinical application.

Clinical trials report adverse events (AEs) in a dense table focusing on the frequency of ‘worst grade’ AEs experienced over the duration of treatment. There is usually no granular information provided on the timing and trajectory of AEs or whether they are likely to worsen, improve, or remain constant over time.

Non-hematologic (NH) AE data was extracted from the CALYPSO trial comparing carboplatin with pegylated liposomal doxorubicin (CD) to carboplatin with paclitaxel (CP) in recurrent ovarian cancer (ROC). Generalised estimating equations (GEE) were used to assess the risk and trajectory of combined Grade 2 or higher (G2+) AE and of each specific AE. The risk of G2+AE was also compared between treatment arms.

The study included 976 patients and AE were reported for the duration of treatment. Most patients experienced at least one G2+NHAE (CPCD, 96.0%80.6%). Risk of combined G2+AE increased with CP (4.1% per-cycle) but decreased with CD (0.8%, P <0.01). When alopecia and sensory neuropathy were excluded, risk of G2+ AE decreased by 2.7% per-cycle, with no significant difference between treatment arms. G2+ nausea improved (15.2% per-cycle, P <0.01). G2+ sensory neuropathy worsened (29.3% per-cycle, P <0.01). Fatigue was stable (17% per-cycle, P =0.06) whilst G2+ pain decreased over time (13.4% per-cycle, P <0.01), with no difference between treatment arms.

Existing trial data can be used to provide AE trajectories as illustrated here for ROC. These trajectories have utility in guiding treatment choice and potentially optimising AE management with novel therapies and treatment combinations.

Existing trial data can be used to provide AE trajectories as illustrated here for ROC. These trajectories have utility in guiding treatment choice and potentially optimising AE management with novel therapies and treatment combinations.

The present study aims to explore whether the association between previous displacement to mainland Portugal to perform cancer therapy and current psychological adaptation is mediated by cancer survivors’ unmet needs in terms of their emotional experience, financial concerns, access and continuity of care, and relations with others.

This cross-sectional study included a sample of 173 cancer survivors from the Azores archipelago (Portugal) recruited from a local oncological health unit. Participants completed a sociodemographic and clinical questionnaire and self-report measures assessing their unmet needs and psychological adaptation. Two parallel multiple mediation models were tested.

Azorean cancer survivors live with unmet needs, especially emotional needs (M=16.68, SD=10.78). Displacement was indirectly associated with both anxious (indirect effect=0.58, SE=0.27, 95% Bias Corrected and accelerated Confidence Interval=[0.05, 1.15]) and depressive symptomatology (indirect effect=0.36, SE=0.17, 95% Bias Corrected and accelerated Confidence Interval=[0.03, 0.84]) through unmet emotional needs.

Previous displacements seem to play an important role in the way cancer survivors adapt to survivorship by contributing to higher levels of unmet emotional needs. These findings can provide a scientific and clinical contribution to other isolated or island regions in the world where survivors face similar constraints.

Previous displacements seem to play an important role in the way cancer survivors adapt to survivorship by contributing to higher levels of unmet emotional needs. These findings can provide a scientific and clinical contribution to other isolated or island regions in the world where survivors face similar constraints.Logistic regression classification (LRC) is widely used to develop models to predict the risk of femoral fracture. LRC models based on areal bone mineral density (aBMD) alone are poor, with area under the receiver operator curve (AUROC) scores reported to be as low as 0.63. This has led to researchers investigating methods to extract further information from the image to increase performance. Recently, the use of active shape (ASM) and appearance models (AAM) have resulted in moderate improvements, but there is a risk that inclusion of too many modes will lead to overfitting. In addition, there are concerns that the effort required to extract the additional information does not justify the modest improvement in fracture risk prediction. Selleckchem Mycophenolic This raises the question, are we reaching the limits of the information that can be extracted from an image? Finite element analysis was used in combination with active shape and appearance modelling to select variables to develop LRC models of fracture risk. Active shape and oved the performance of the classifier (ΔAUROC = 0.123). Further addition of modes did not result in any further substantial improvements. Based on these findings, it is suggested that we are reaching the limits of the information that can be extracted from an image to predict fracture risk.