Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer

Zhang, Jianyuan; Zhao, Xiuhua; Zhao, Yan; Zhang, Jingmian; Zhang, Zhaoqi; Wang, Jianfang; Wang, Yingchen; Dai, Meng; Han, Jianmin

doi:10.1007/s00259-019-04592-1

Cited by 157 publications

(152 citation statements)

References 30 publications

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“…In our study, the EGFR+ group was more heterogeneous on both PET and CT images than the EGFR- group. Our findings were similar to previous studies ( 21 – 23 ). They found that those image texture feature measuring the variability of gray-level intensity or the asymmetry of the distribution of gray-level values were significant predictive of EGFR mutation status.…”

Section: Discussionsupporting

confidence: 94%

“…Gender as only clinical characteristic was selected in combine model of our study. The addition of clinical characteristics to PET/CT radiomics model, to varying degrees, increase the diagnostic performance of diagnostic model in previous studies ( 22 , 23 ) and our study, which finally reached 82.6% of diagnostic accuracy in Li X’s study ( 22 ), 80.0% in Zhang J’s study ( 23 ), and 75.3% in our study for predicting EGFR mutation status. It suggested that the combined model might be an alternative indicator of EGFR mutations when tissue samples are not available.…”

Section: Discussionsupporting

confidence: 52%

“…In summary, the NSCLCs with EGFR mutation had lower glucose metabolism and density, with more heterogeneity on both PET and CT images than those with wild-type EGFR. Owing to the bi-modal image features, PET/CT radiomics model in recent studies (0.79 in Zhang J’s study ( 23 ); 0.80 in Li X’s study ( 22 ); 0.77 in our study) has showed higher AUC than those generated by PET (0.67 in Yip, SS’s study ( 21 )) or CT (0.69 in Rios Velazquez, E’s study ( 19 ); 0.56-0.75 in Sacconi, B’s study ( 31 )) radiomics features alone for predicting EGFR mutation status. However, compared with larger sample size in CT radiomics research, the current sample size in PET/CT radiomics-related studies is generally limited, and thus the generalization ability of PET/CT radiomics-based model remains to be further tested.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics data obtained using mathematical algorithms can quantitatively describe the spatial relationship between voxels, and become an important tool to study tumor heterogeneity in vivo ( 18 ). To date, most studies using radiomics for the prediction of EGFR mutation status in NSCLC are based on chest CT images ( 19 , 20 ), whereas few studies about the relationship between PET or PET/CT radiomics features and EGFR mutation status in lung cancer ( 21 – 23 ) are conducted.…”

Section: Introductionmentioning

confidence: 99%

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Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Zhang

Bao

et al. 2020

Front. Oncol.

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Objective To assess the performance of pretreatment 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) radiomics features for predicting EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Patients and Methods We enrolled total 173 patients with histologically proven NSCLC who underwent preoperative 18 F-FDG PET/CT. Tumor tissues of all patients were tested for EGFR mutation status. A PET/CT radiomics prediction model was established through multi-step feature selection. The predictive performances of radiomics model, clinical features and conventional PET-derived semi-quantitative parameters were compared using receiver operating curves (ROCs) analysis. Results Four CT and two PET radiomics features were finally selected to build the PET/CT radiomics model. Compared with area under the ROC curve (AUC) equal to 0.664, 0.683 and 0.662 for clinical features, maximum standardized uptake values (SUV max ) and total lesion glycolysis (TLG), the PET/CT radiomics model showed better performance to discriminate between EGFR positive and negative mutations with the AUC of 0.769 and the accuracy of 67.06% after 10-fold cross-validation. The combined model, based on the PET/CT radiomics and clinical feature (gender) further improved the AUC to 0.827 and the accuracy to 75.29%. Only one PET radiomics feature demonstrated significant but low predictive ability (AUC = 0.661) for differentiating 19 Del from 21 L858R mutation subtypes. Conclusions EGFR mutations status in patients with NSCLC could be well predicted by the combined model based on 18 F-FDG PET/CT radiomics and clinical feature, providing an alternative useful method for the selection of targeted therapy.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Zhang

Bao

et al. 2020

Front. Oncol.

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“…Many studies have shown that radiomics features have great potential to be the maker for tumor phenotype (8)(9)(10)(11)(12)(13)(14)(15)(16)(17), and found Adc can be differentiated from Sqc by radiomics (17)(18)(19)(20)(21)(22)(23). However, The data sets of those studies only included Adc and Sqc, that is to say, the accuracy of those models will be affected by other histological subtypes of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Yan

Wang

2020

Front. Oncol.

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Purpose: To develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET. Methods: Machine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases (Mts), adenocarcinoma (Adc), and squamous cell carcinoma (Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership. The performance metrics include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics. Results: The combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR fourclass classification model showed 91% Acc and 0.89 Kappa. Conclusion: The proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.

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Safety, efficacy, and pharmacokinetics of SH‐1028 in EGFR T790M‐positive advanced non–small cell lung cancer patients: A dose‐escalation phase 1 study

Zhao

et al. 2023

Cancer

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Background: SH-1028 is a new third-generation EGFR tyrosine kinase inhibitors (TKI) to benefit patients with EGFR T790M-mutated NSCLC. Here, the authors report its clinical safety, preliminary efficacy, and pharmacokinetic (PK) profile for the first time.Methods: Patients with EGFR T790M mutation, locally advanced non-small cell lung cancer (NSCLC), or metastatic NSCLC who had progressed after previous EGFR TKI therapy were eligible. Patients received SH-1028 at five oral dose levels (60 mg, 100 mg, 200 mg, 300 mg, and 400 mg) once daily until disease progression, unacceptable toxicity, or patient withdrawal. The primary end points were the safety, dose-limiting toxicity (DLT), maximum-tolerated dose (MTD), and PK profile. Secondary end points included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), etc.Results: Data cut off on December 31, 2020, a total of 20 patients were enrolled during the trial, two of three patients in 300 mg cohort experienced a DLT, and no DLT was observed in 240 mg cohort, 240 mg was determined to be the MTD of SH-1028. A total of 95.0% (19 of 20) of patients reported treatment-related adverse events (TRAEs), and the incidence of serious adverse events was 20.0% (4 of 20).The ORR and DCR of the 200 mg cohort were 75% (95% confidence interval [CI],19.41-99.37) and 75.0% (95% CI, 19.41-99.37), respectively. The overall ORR was 40% (95% CI, 19.12-63.95), and DCR was 70.0% (95% CI,). According to the PK profile, the dosage regimen for future studies was determined as 200 mg once daily.Conclusions: SH-1028 showed a manageable safety and promising antitumor activity in patients with EGFR T790M mutation at the dose of 200 mg once daily.The first two authors contributed equally to this article. The trial registration is ClinicalTrials.gov ID: NCT03603262.

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Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer

Cited by 157 publications

References 30 publications

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Safety, efficacy, and pharmacokinetics of SH‐1028 in EGFR T790M‐positive advanced non–small cell lung cancer patients: A dose‐escalation phase 1 study

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