Hypoalbuminaemia as a Prognostic Biomarker of First-Line Treatment Resistance in Metastatic Non-small Cell Lung Cancer.

Introduction: Despite significant advances in systemic anticancer therapy (SACT) for non-small cell lung cancer (NSCLC), many patients still fail to respond to treatment or develop treatment resistance. Albumin, a biomarker of systemic inflammation and malnutrition, predicts survival in many cancers. We evaluated the prognostic significance of albumin in patients receiving first-line targeted therapy or immunotherapy-based SACT for metastatic NSCLC. Methods: All patients treated with first-line targeted therapy or immunotherapy-based SACT for metastatic NSCLC at a regional Scottish cancer centre were identified. Serum albumin at pre-treatment, after 12-weeks of treatment, and at the time of progressive disease were recorded. The relationship between albumin (≥ 35g/L v <35g/L) and overall survival (OS) was examined. Results: Data were available for 389 patients of both targeted therapy cohort (n = 159) and immunotherapy-based therapy cohort (n = 230). Pre-treatment albumin was predictive of OS in each cohort at HR1.82 (95%CI 1.23-2.7) (p =0.003) and HR2.55 (95%CI 1.78-3.65) (p < 0.001), respectively. Pre-treatment albumin <35 g/L was associated with a significantly higher relative risk of death within 12 weeks in each cohort at RR9.58 (95%CI 2.20-41.72, p = 0.003) and RR3.60 (95%CI 1.74-6.57, p < 0.001), respectively. The 12-week albumin was predictive of OS in each cohort at HR1.88 (95%CI 1.86-4.46) (p < 0.001) and HR2.67 (95%CI 1.74-4.08) (p < 0.001), respectively. 46 out of 133 (35%) evaluable patients treated with targeted therapy and 43 out of 169 (25%) treated with immunotherapy-based therapy crossed over albumin prognostic groups between pre-treatment and 12-week. The prognostic value of 12-week albumin was independent of pre-treatment albumin status. A majority of patients had albumin <35g/L at the time of progressive disease when it was also predictive of survival following progressive disease at HR2.48 (95%CI 1.61-3.82) (p < 0.001) and HR2.87 (95%CI 1.91-4.31) (p < 0.001) respectively). Conclusions: Albumin is a reliable prognostic factor in patients with metastatic NSCLC, predicting survival independent of the class of drug treatment at various time points during the patient journey. Tracking albumin concentrations during systemic therapy may indicate disease activity or treatment response over time.

Patient perceptions of their decision to undergo palliative chemotherapy in the Edinburgh Cancer Centre.

BACKGROUND: The decision to undergo chemotherapy for incurable cancer demands informed discussions about the risks and benefits of proposed treatments. Research has shown that many patients have a poor grasp of these factors. METHODS: An evaluation of the patient experience of palliative chemotherapy decision-making was undertaken. Patients with lung or gynaecological cancers were surveyed about their decision, what they understood about its risks and benefits, and how supported they felt. RESULTS: A total of 29 people with lung cancer (n = 21) or gynaecological cancer (n = 8) completed questionnaires. The majority felt sure about their decision, though many were less sure of the risks and benefits of treatment. Unprompted comments revealed significant nuance, including that the decision to undergo chemotherapy may not necessarily have felt like a choice. CONCLUSIONS: Our positive findings may reflect participant selection bias, or could represent genuine comfort in decision-making in Scottish oncology clinics. Further research is needed.

A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread.

Gliomas are primary brain tumours arising from the glial cells of the nervous system. The diffuse nature of spread, coupled with proximity to critical brain structures, makes treatment a challenge. Pathological analysis confirms that the extent of glioma spread exceeds the extent of the grossly visible mass, seen on conventional magnetic resonance imaging (MRI) scans. Gliomas show faster spread along white matter tracts than in grey matter, leading to irregular patterns of spread. We propose a mathematical model based on Diffusion Tensor Imaging, a new MRI imaging technique that offers a methodology to delineate the major white matter tracts in the brain. We apply the anisotropic diffusion model of Painter and Hillen (J Thoer Biol 323:25-39, 2013) to data from 10 patients with gliomas. Moreover, we compare the anisotropic model to the state-of-the-art Proliferation-Infiltration (PI) model of Swanson et al. (Cell Prolif 33:317-329, 2000). We find that the anisotropic model offers a slight improvement over the standard PI model. For tumours with low anisotropy, the predictions of the two models are virtually identical, but for patients whose tumours show higher anisotropy, the results differ. We also suggest using the data from the contralateral hemisphere to further improve the model fit. Finally, we discuss the potential use of this model in clinical treatment planning.

Moments of von Mises and Fisher distributions and applications.

The von Mises and Fisher distributions are spherical analogues to the Normal distribution on the unit circle and unit sphere, respectively. The computation of their moments, and in particular the second moment, usually involves solving tedious trigonometric integrals. Here we present a new method to compute the moments of spherical distributions, based on the divergence theorem. This method allows a clear derivation of the second moments and can be easily generalized to higher dimensions. In particular we note that, to our knowledge, the variance-covariance matrix of the three dimensional Fisher distribution has not previously been explicitly computed. While the emphasis of this paper lies in calculating the moments of spherical distributions, their usefulness is motivated by their relationship to population statistics in animal/cell movement models and demonstrated in applications to the modelling of sea turtle navigation, wolf movement and brain tumour growth.