Radiomics and radiogenomics in lung cancer: A review for the clinician. (January 2018) - Thawani, Rajat; McLane, Michael; Beig, Niha; Ghose, Soumya; Prasanna, Prateek; Velcheti, Vamsidhar; Madabhushi, Anant
Highlights: Description of radiomics from the perspective of physicians working with lung cancer patients. Discussion on the potential and importance of integration of radiomics into clinical workflow. Limitations of the technology and potential ways to overcome these. Abstract: Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening of certain at-risk populations, the practical implementation of these screening efforts has not yet been successful and remains in high demand. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate using machine learning algorithms. The quantitative features analyzed express subvisual characteristics of images which correlate with pathogenesis of diseases. These features are broadly classified into four categories: intensity, structure, texture/gradient, and wavelet, based on the types of image attributes they capture. Many studies have been done to show correlation between these features and the malignant potential of a nodule on a chest CT. In cancer patients, these nodules also have features that can be correlated with prognosis and mutation status. The major limitations of radiomics are the lack of standardization of acquisition parameters, inconsistent radiomic methods, and lack of reproducibility. Researchers are working on overcoming these limitations, which would make radiomics more acceptable in the medical community.