Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Yip, Stephen; Kim, John; Coroller, Thibaud P.; Parmar, Chintan; Velazquez, Emmanuel Rios; Huynh, Elizabeth; Mak, Raymond H.; Aerts, Hugo J.W.L.

doi:10.2967/jnumed.116.181826

Cited by 140 publications

(114 citation statements)

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“…In addition to EGFR mutation prediction based on CT images, EGFR mutations were predicted based on Positron Emission Computed Tomography (PET)/CT. Notably, Yip et al performed radiomics analysis of PET images and identified significant features for distinguishing EGFR mutation status . However, these studies were designed to predict the overall mutation status of EGFR, and did not specifically describe the relevant radiomic features associated with EGFR mutation subtypes.…”

Section: Discussionmentioning

confidence: 99%

Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer

Ding

Zhang

et al. 2019

Medical Physics

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Purpose This retrospective study was designed to investigate the ability of radiomics to predict the mutation status of epidermal growth factor receptor (EGFR) subtypes (19Del and L858R) in patients with non‐small cell lung cancer (NSCLC). Methods In total, 312 patients with NSCLC were included, and 580 radiomic features were extracted from the computed tomography images of each patient. In the training set, univariate analysis was performed on the clinical and radiomic features; logistic regression models were established using a 5‐fold cross validation strategy for the prediction of EGFR subtypes 19Del and L858R. Subsequently, the predictive ability of the joint models was evaluated using the test set. Results The results revealed that the radiomic features specific for EGFR 19Del and L858R were Gabor’s MTRVariance, Gabor’s PTREntropy, and sphericity. Additionally, the respective areas under the receiver operating characteristic curves of the EGFR 19Del and L858R joint models were 0.7925 and 0.7750 for the test set. Conclusions Our study demonstrated the potential for radiomics to predict EGFR 19Del and L858R. Epidermal growth factor receptor 19Del and L858R exhibited distinct imaging phenotypes, which may help to guide the selection of more accurate and personalized treatment programs for patients with NSCLC.

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Section: Discussionmentioning

confidence: 99%

Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer

Ding

Zhang

et al. 2019

Medical Physics

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“…Given that somatic mutations affect the ability of cells to grow in otherwise non-permissive conditions, we decided to test whether these conditions can be quantified by radiomics and if they reflect the underlying mutational landscape - and whether one could use radiomic phenotype to predict tumor genotype. Although, associations between diagnostic imaging features and mutational data have been explored (24–34), most studies suffer from small cohort sizes, do not include external validation, or have relied on observer-dependent semi-quantitative features that make replication difficult. We hypothesized that automated quantitative radiomic feature extraction, applied to a large, heterogeneous cohort, and rigorously validated, could establish the genotype-imaging phenotype linkage.…”

Section: Introductionmentioning

confidence: 99%

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

et al. 2017

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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.

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“…Radiomics, that encodes tumor phenotypes with innumerable quantitative features using predefined image analysis algorithms. In particular, previous studies have demonstrated that certain radiomic features are associated with EGFR mutations status, suggesting that those identified features may be driven by somatic mutations . Although these studies have achieved impressive performances, especially when combined with clinical information, conventional radiomics‐based methods are born with three main challenges.…”

Section: Introductionmentioning

confidence: 99%

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

Zhao

Yang

et al. 2019

Cancer Medicine

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To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR‐mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild‐type (WT) was retrospectively analyzed. A deep learning system, namely 3D DenseNets, was developed to process 3D patches of nodules from CT data, and learn strong representations with supervised end‐to‐end training. The 3D DenseNets were trained with a training subset of 348 nodules and tuned with a development subset of 116 nodules. A strong data augmentation technique, mixup , was used for better generalization. We evaluated our model on a holdout subset of 115 nodules. An independent public dataset of 37 nodules from the cancer imaging archive (TCIA) was also used to test the generalization of our method. Conventional radiomics analysis was also performed for comparison. Our method achieved promising performance on predicting EGFR mutation status, with AUCs of 75.8% and 75.0% for our holdout test set and public test set, respectively. Moreover, strong relations were found between deep learning feature and conventional radiomics, while deep learning worked through an enhanced radiomics manner, that is, deep learned radiomics (DLR), in terms of robustness, compactness and expressiveness. The proposed deep learning system predicts EGFR‐mutant of lung adenocarcinomas in CT images noninvasively and automatically, indicating its potential to help clinical decision‐making by identifying eligible patients of pulmonary adenocarcinoma for EGFR‐targeted therapy.

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Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Cited by 140 publications

References 50 publications

Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer

Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

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