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.

Cigarette smoking is significantly associated with colorectal cancer incidence and mortality.

Pancreatic cancer, although infrequent, has an exceptionally high mortality rate, making it one of the four or five most common causes of cancer mortality in developed countries. The incidence of pancreatic cancer varies greatly across regions, which suggests roles for lifestyle factors, such as diet, or environmental factors, such as vitamin D exposure. Smoking is the most common known risk factor, and is the cause of 20-25% of all pancreatic tumors. Alcohol does not seem to be a risk factor, unless it leads to chronic pancreatitis, which is a probable risk factor. Long-standing diabetes increases the risk of pancreatic cancer, but can also be an early manifestation of pancreatic tumors. 5-10% of patients with pancreatic cancer have an underlying germline disorder, while the remaining percentage of cancer cases is thought to be caused by somatic mutations. Some individual studies suggest that mutations in various polymorphic genes can lead to small increases in the risk of pancreatic cancer, but these findings need to be replicated. Rising prevalence of smoking in developing countries, improved diagnosis and increasing population longevity are all likely to increase the global burden of pancreatic cancer in the coming decades.

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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. '

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It was suggested that vitamin D levels influence cancer development. The vitamin D receptor (VDR) is a crucial mediator for the cellular effects of vitamin D. Results from previous studies on the association of VDR polymorphisms with different cancer types are somewhat contradictory, and the role of VDR in the etiology of cancer is still equivocal. We therefore performed a meta-analysis on the association between the two most studied VDR polymorphisms (FokI and BsmI) and any cancer site. Up to January 2009, we identified 67 independent studies. We used random-effects models to provide summary odds ratio (SOR) for VDR polymorphisms and cancer. We tested homogeneity of effects across studies and publication bias and explored between-study heterogeneity. When comparing FokI ff with FF carriers, we found a significant increase in skin cancer [SOR; 95% confidence intervals (CIs): 1.30; 1.04-1.61] and breast cancer (SOR; 95%CI: 1.14; 1.03-1.27) risk. For the same genotype comparison, we found a significantly higher risk of cancer when we pooled estimates from cancer sites possibly associated with vitamin D levels (prostate, breast, skin, ovary, nonHodgkin lymphoma and colorectal). A significant reduction in prostate cancer risk was observed for carriers of BsmI Bb compared with bb genotype (SOR; 95%CI: 0.83; 0.69-0.99). In Caucasian populations, both Bb and BB carriers had a significant reduced risk of cancer at any site. In conclusion, this meta-analysis showed that VDR FokI and BsmI polymorphisms might modulate the risk of cancer of breast, skin and prostate and possibly affect cancer risk at any site in Caucasians.

Air bronchogram, pleural retraction, small size relate to EGFR mutation in NSCLC. Pleural effusion and younger age relate to ALK mutation. Round lesion shape, nodules in non-tumour lobes relate to KRAS mutation.