Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

Background. Radiation-induced lung damage (RILD) is an important problem. Although physical parameters such as the mean lung dose are used in clinical practice, they are not suited for individualised radiotherapy. Objective, quantitative measurements of RILD on a continuous instead of on an ordinal, semi-quantitative, semi-subjective scale, are needed. Methods. Hounsfield unit (HU) changes before versus three months post-radiotherapy were correlated per voxel with the radiotherapy dose in 95 lung cancer patients. Deformable registration was used to register pre- and post-CT scans and the density increase was quantified for various dose bins. The dose-response curve for increased HU was quantified using the slope of a linear regression (HU/Gy). The end-point for the toxicity analysis was dyspnoea ≥ grade 2. Results. Radiation dose was linearly correlated with the change in HU (mean R² = 0.74 ± 0.28). No differences in HU/Gy between groups treated with stereotactic radiotherapy, conventional radiotherapy alone, sequential or concurrent chemo- radiotherapy were observed. In the whole patient group, 33/95 (34.7%) had dyspnoea ≥ G2. Of the 48 patients with a HU/Gy below the median, 16 (33.3%) developed dyspnoea ≥ G2, while in the 47 patients with a HU/Gy above the median, 17 (36.1%) had dyspnoea ≥ G2 (not significant). Individual patients showed a nearly 21-fold difference in radiosensitivity, with HU/Gy ranging from 0 to 10 HU/Gy. Conclusions. HU changes identify objectively the whole range of individual radiosensitivity on a continuous, quantitative scale. CT density changes may allow more robust and accurate radiogenomics studies.