A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management

Sun, Xilin; Xiao, Zunyu; Chen, Gongyan; Han, Zhaoguo; Liu, Yang; Zhang, Chongqing; Sun, Yingying; Song, Yan; Wang, Kai; Fang, Fang; Wang, Xiance; Lin, Yunzhi; Xu, Lili; Shao, Liming; Li, Jin; Cheng, Zhen; Gambhir, Sanjiv S.; Shen, Baogen

doi:10.1126/scitranslmed.aan8840

Cited by 80 publications

(60 citation statements)

References 33 publications

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“…Although KRAS and EGFR mutation profiling is critical in NSCLC management, some studies have raised issues with this approach (6,7). First, such mutation status captures only a small degree of tumor heterogeneity and does not provide a complete picture for the assessment of tumor characteristics.…”

Section: Introductionmentioning

confidence: 99%

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Shiri¹,

Maleki²,

Hajianfar³

et al. 2020

Mol Imaging Biol

102

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Conflicts of Interest:The authors declare no potential conflicts of interest. Abstract PurposeConsiderable progress has been made in assessment and management of NSCLC patients based on mutation status in the EGFR and KRAS. At the same time, NSCLC management through KRAS and EGFR mutation profiling faces some challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on low dose CT, diagnostic CT, PET modalities radiomic features and machine learning (ML) algorithms. MethodsOur study involved NSCLC cancer patient with 186 PET, and 175 low dose CT and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted. Conventional clinically used standard uptake value (SUV) parameters were also obtained for PET images. Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed and q-value were reported for univariate analysis. Six feature selection methods and twelve classifiers were used to predict gene status in patient. We performed 10-fold cross-validation for model tuning to improve robustness and all model evaluation was reported on independent validation sets (68 patients). The mean area under the receiver operator characteristic (ROC) curve (AUC) was obtained for performance evaluation. ResultsThe best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore, the machine learning algorithm improved the perdition power up to AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with sigma 3.5 with SM feature selector and SGD classifier). ConclusionOur findings demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.This study was conducted on 211 NSCLC cancer patients with available imaging and genomic data. Imaging modalities, including diagnostic CT (CTD) and PET/CT (i.e., low-dose CT (CT) used for PET attenuation correction and the PET image) for all patients were obtained (29-32) . All images we...

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Section: Introductionmentioning

confidence: 99%

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Shiri¹,

Maleki²,

Hajianfar³

et al. 2020

Mol Imaging Biol

102

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“…is one of such novel PET tracers with high specificity to activating EGFR mutant kinase. In NSCLC mice models, 18 F-MPG could reveal EGFR-activating mutations with high sensitivity and specificity (100). In 75 patients with NSCLC, patients with EGFR mutations had significantly higher 18 F-MPG uptake than did those with wild-type EGFR (100).…”

Section: Molecular and Functional Images For Precision Therapy In Nsclcmentioning

confidence: 99%

Imaging of Precision Therapy for Lung Cancer: Current State of the Art

et al. 2019

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Advances in characterization of molecular and genomic abnormalities specific to lung cancer have made precision therapy the current standard of care for lung cancer treatment. This article will provide a cutting-edge review of imaging of lung cancer in the current era of precision medicine. The focus of the article includes (a) an update on the recent advances in precision therapy for non-small cell lung cancer and their implications on imaging; (b) molecular and genomic biomarkers and pitfalls of image interpretations for lung cancer precision therapy; and (c) review of the current approaches and future directions of precision imaging for lung cancer, emphasizing emerging observations in longitudinal tumor kinetics, radiomics, and molecular and functional imaging. The article is designed to help radiologists to remain up to date in the rapidly evolving world of lung cancer therapy and serve as key members of multidisciplinary teams caring for these patients.

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“…Epidermal growth factor (EGFR) -Tyrosine kinase inhibitor (TKI) treatment strategies for NSCLC depend on the mutation status of EGFR (72). A recent study by Sun et al demonstrated N-(3-chloro-4-fluorophenyl)-7-(2-(2-(2-(2-18F-fluoroethoxy) ethoxy) ethoxy) ethoxy)-6-methoxyquinazolin-4amine ( 18 F-MPG) PET/CT is a powerful method for precise quantification of EGFR-activating mutation status in NSCLC patients, and it is a promising strategy for noninvasively identifying patients sensitive to EGFR-TKIs and for monitoring the efficacy of EGFR-TKI therapy (72).…”

Section: Angiogenesis In Lung Cancermentioning

confidence: 99%

The role of new PET tracers for lung cancer

et al. 2019

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18F-Fluorodeoxyglucose ( 18 F-FDG) positron emission tomography-computed tomography (PET/CT) is integral to the investigation and management of suspected or proven lung cancer (1) despite the nonspecific nature of the 18 F-FDG, which is a glucose analogue. An improved understanding in tumour biology and advances in therapeutic options has driven the need to better characterise tumours, and predict and monitor treatment response to new targeted cancer therapies. Some of the cancer-related cellular processes being investigated include tumour proliferation, amino acid metabolism, tumour hypoxia and angiogenesis. The majority of tracers being used to evaluate these processes remain restricted to preclinical and clinical research. There are also certain receptors which are expressed by specific cancers which can then be targeted by peptides, e.g. neuroendocrine tumours express somatostatin receptors which have an affinity to somatostatin analogues such as DOTA-peptides. 68 Ga-DOTA-peptides have an established role in PET imaging, including imaging of carcinoid tumours, but these are not specific to lung lesions.

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A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management

Cited by 80 publications

References 33 publications

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Imaging of Precision Therapy for Lung Cancer: Current State of the Art

The role of new PET tracers for lung cancer

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