Background and purpose: Radiomics allows extraction of quantifiable features from imaging. This study performs a systematic review and meta-analysis of the performance of radiomics based prognostic models in non-small cell lung cancer (NSCLC).Materials and methods: A literature review was performed following PRISMA guidelines. Medline, EMBASE and Cochrane databases were searched for articles investigating radiomics features predictive of overall survival (OS) in NSCLC treated with curative intent radiotherapy. A random-effects metaanalysis of Harrell's Concordance Index (C-index) was performed on the performance of radiomics models.Results: Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review. There was significant heterogeneity in the methodology for feature selection and model development. Twelve datasets reported the C-index of radiomics based models in predicting OS and were included in the meta-analysis. The C-index random effects estimate was 0.57 (95% CI 0.53-0.62). There was significant heterogeneity (I 2 = 70.3%).Conclusions: Based on this review, radiomics based models for lung cancer have to date demonstrated modest prognostic capabilities. Future research should consider using standardised radiomics features, robust feature selection and model development, and deep learning techniques, absolving the need for pre-defined features, to improve imaging-based models.

Since the early days of megavoltage Radiation Therapy (RT), the potential of delivering treatment to a sub group of patients in an upright position has been recognized. Compared to lying horizontally, treating patients in an upright position offers potential benefits in terms of patient comfort especially for patients experiencing dyspnoea and saliva accumulation when lying down. Dosimetric benefits can also be gained from changes in the volume and location of lungs and heart in an upright position, which are potentially advantageous for clinical situations including Hodgkin's disease, lung and breast malignancies. Since the 1950's, upright stabilization mechanisms have ranged from standalone chair based apparatus to couch-top attachments with increasingly customizable solutions. The introduction of Computed-Tomography (CT) based three-dimensional (3D) dosimetry in the 1980's−90's necessitated image acquisition in a horizontal position (supine or prone), significantly reducing options for alternative patient positioning and upright techniques. Despite this, upright techniques have still been utilized where clinically indicated for palliative and novel approaches often involving non-standard treatment scenarios. More recently, a small number of centers have reported on specialized equipment capable of acquiring planning data with the patient in a vertical position. The possibility of acquiring planning quality Cone Beam CT (CBCT) on linear accelerators has recently reinvigorated the potential to deliver highly accurate and targeted treatments to patients in an upright position. This paper reflects on the historical applications of upright RT and explores new possibilities for this technology in modern RT departments.

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.