Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Velazquez, Emmanuel Rios; Parmar, Chintan; Liu, Ying; Coroller, Thibaud P.; Cruz, Gisele; Stringfield, Olya; Ye, Zhaoxiang; Makrigiorgos, G. Mike; Fennessy, Fiona M.; Mak, Raymond H.; Gillies, Robert J.; Quackenbush, John; Aerts, Hugo J.W.L.

doi:10.1158/0008-5472.can-17-0122

Cited by 304 publications

(301 citation statements)

References 55 publications

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“…Because early stages are often curable, this could drastically improve patient outcomes, minimize overtreatment, and even save lives. Furthermore, AI also can enhance lung cancer staging and characterization for treatment selection, as well as monitoring treatment response (Table ) …”

Section: Lung Cancer Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

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1,037

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Section: Lung Cancer Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

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1,037

763

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“…Research studies have demonstrated that radiomics can be used to distinguish between EGFR mutations and wild‐type EGFR . However, these studies did not identify imaging phenotypes that can be used to distinguish subtypes (19Del and L858R) of mutant EGFR and wild‐type EGFR (EGFR non‐mutant).…”

Section: Introductionmentioning

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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“…Within oncology, multiple efforts have successfully explored radiomics tools for assisting clinical decision making related to the diagnosis and risk stratification of different cancers 15,16 . For example, studies in non-small-cell lung cancer (NSCLC) used radiomics to predict distant metastasis in lung adenocarcinoma 17 and tumour histological subtypes 18 as well as disease recurrence 19 , somatic mutations 20 , gene-expression profiles 21 and overall survival 22 . Such findings have motivated an exploration of the clinical utility of AI-generated biomarkers based on standard-of-care radiographic images 23 — with the ultimate hope of better supporting radiologists in disease diagnosis, imaging quality optimization, data visualization, response assessment and report generation.…”

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confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Cited by 304 publications

References 55 publications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Artificial intelligence in cancer imaging: Clinical challenges and applications

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

Artificial intelligence in radiology

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