Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n ¼ 353) and verified them in an independent validation cohort (n ¼ 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter).We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR þ and EGFR Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.

Background This study retrospectively evaluated the capability of computed-tomography (CT) based radiomic features to predict EGFR mutation status in surgically-resected peripheral lung adenocarcinomas in an Asian cohort of patients. Materials and Methods 298 patients with surgically resected peripheral lung adenocarcinomas were investigated in this institutional review board-approved retrospective study with waived consent. 219 quantitative 3D features were extracted from segmented volumes of each tumor, and 59 of these which were considered as independent features were included in the analysis. Clinical and pathological information were obtained from the institutional database. Results Mutant EGFR was significantly associated with female gender (p=0.0005); never smoker status (p<0.0001), lepidic predominant adenocarcinomas (p=0.017), and low or intermediate pathologic grade (p=0.0002). Statistically significant differences were found in 11 radiomic features between EGFR mutant and wild type groups on univariate analysis. Mutant EGFR status could be predicted by a set of five radiomic features that fall in three broad groups: CT attenuation energy, tumor main direction and texture defined by wavelets and Laws (AUC 0.647). Multiple logistic regression model showed that adding radiomic features to a clinical model resulted in a significant improvement of predicting power, as the AUC increased from 0.667 to 0.709 (p<0.0001). Conclusions CT based radiomic features of peripheral lung adenocarcinomas can capture useful information regarding tumor phenotype, and the model we built can be useful to predict the presence of EGFR mutations in peripheral lung adenocarcinoma in Asian patients when mutational profiling is not available or possible.

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.DOI: http://dx.doi.org/10.7554/eLife.23421.001

PURPOSE Determine if quantitative analyses (“radiomics”) of low dose CT lung cancer screening images at baseline can predict subsequent emergence of cancer. PATIENTS AND METHODS Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen detected lung cancer (SDLC), then matched to cohorts of 208 and 196 screening subjects with benign pulmonary nodules (bPN). Image features were extracted from each nodule and used to predict the subsequent emergence of cancer. RESULTS The best models used 23 stable features in a Random Forest classifier, and could predict nodules that will become cancerous 1 and 2 years hence with accuracies of 80% (AUC 0.83) and 79% (AUC 0.75), respectively. Radiomics outperformed Lung-RADS and volume. McWilliams’ risk assessment model was commensurate. CONCLUSION Radiomics of lung cancer screening CTs at baseline can be used to assess risk for development of cancer.

the national Lung Screening trial (nLSt) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDct screening is overdiagnosis of slow growing and indolent cancers. in this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. incident lung cancer patients from the nLSt were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression tree to stratify patients into three risk-groups based on two radiomics features (nGtDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. these patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes. The National Lung Screening Trial (NLST) demonstrated that annual screening with low-dose helical computed tomography (LDCT) compared to chest radiography is associated with a 20% relative reduction in lung cancer mortality among high-risk individuals 1. However, LDCT screening can lead to overdiagnosis and overtreatment of slow growing, indolent cancers that may pose no threat if left untreated 2,3. Prior post-hoc analyses of the NLST have estimated that 18-22.5% of screen-detected cancers would not become symptomatic in a patient's lifetime and would remain as indolent lung cancer 4. At present there is limited data regarding the potential harmful impact of overdiagnosis on lung cancer outcomes; however, studies have suggested overdiagnosis is associated with increased operative mortality, severe disability among survivors, and reduction in longer term disease-free survival attributed to loss of pulmonary reserve 5. Though clinical guidelines provide recommendations for the management of screen-detected nodules, there are limited tools to discriminate between indolent and aggressive lung cancers diagnosed in the lung cancer screening setting 6-9. As such, biomarkers that can classify behavior of screen-detected lung cancers is an unmet clinical need since prior studies have suggested that 10 to 27% of lung cancers are over-diagnosed in lung cancer screening 10-13. Quantitative image features, also known as radiomics 14 , are non-invasive biomarkers that are generated from medical imaging and reflect the underlying tumor pathophysiology and heterogeneity. Radiomics have many advantages over circulating and tissue-based bio...