Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

ContextColorectal cancer is the third most common form of cancer and the fourth most frequent cause of cancer deaths worldwide. The association between cigarette smoking and colorectal cancer has been inconsistent among studies.ObjectiveTo clarify the association of cigarette smoking and colorectal cancer, we performed a comprehensive literature search and a meta-analysis of observational studies considering both incidence and mortality.Data SourcesWe performed a literature search using PubMed, ISI Web of Science (Science Citation Index Expanded), and EMBASE to May 2008, with no restrictions. We also reviewed references from all retrieved articles.Study SelectionAll articles that were independent and contained the minimum information necessary to estimate the colorectal cancer risk associated with cigarette smoking and a corresponding measure of uncertainty.Data ExtractionArticles were reviewed and data were extracted and cross-checked independently by 3 investigators, and any disagreement was resolved by consensus among all 3.ResultsOne hundred six observational studies were included in the analysis of incidence. Twenty-six studies provided adjusted risk estimates for ever smokers vs never smokers, leading to a pooled relative risk of 1.18 (95% confidence interval [CI], 1.11-1.25). Smoking was associated with an absolute risk increase of 10.8 cases per 100 000 person-years (95% CI, 7.9-13.6). We found a statistically significant dose-relationship with an increasing number of pack-years and cigarettes per day. However, the association was statistically significant only after 30 years of smoking. Seventeen cohort studies were included in the analysis of mortality. The pooled risk estimate for ever vs never smokers was 1.25 (95% CI, 1.14-1.37). Smoking was associated with an absolute risk increase of 6.0 deaths per 100 000 person-years (95% CI, 4.2-7.6). For both incidence and mortality, the association was stronger for cancer of the rectum than of the colon.ConclusionCigarette smoking is significantly associated with colorectal cancer incidence and mortality.

Melanocortin‐1‐receptor (MC1R) is one of the major genes that determine skin pigmentation. MC1R variants were suggested to be associated with red hair, fair skin, and an increased risk of melanoma. We performed a meta‐analysis on the association between the 9 most studied MC1R variants (p.V60L, p.D84E, p.V92M, p.R142H, p.R151C, p.I155T, p.R160W, p.R163Q and p.D294H) and melanoma and/or red hair, fair skin phenotype. Eleven studies on MC1R and melanoma, and 9 on MC1R and phenotype were included in the analysis. The 7 variants p.D84E, p.R142H, p.R151C, p.I155T, p.R160W, p. R163Q and p.D294H were significantly associated with melanoma development, with ORs (95%CI) ranging from 1.42 (1.09–1.85) for p.R163Q to 2.45 (1.32–4.55) for p.I155T. The MC1R variants p.R160W and p.D294H were associated both with red hair and fair skin, while p.D84E, p.R142H, and p.R151C were strongly associated with red hair only‐ ORs (95%CI) ranged from 2.99 (1.51–5.91) for p.D84E to 8.10 (5.82–11.28) for p.R151C. No association with melanoma or phenotype was found for p.V60L and p.V92M variants. In conclusion this meta‐analysis provided evidence that some MC1R variants are associated both with melanoma and phenotype, while other are only associated with melanoma development. These results suggest that MC1R variants could play a role in melanoma development both via pigmentary and non‐pigmentary pathways.