Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma

Zhao, Hongyue; Su, Yexin; Zhang, Linhan; Fu, Peng

doi:10.4149/neo_2021_201222n1388

Cited by 10 publications

(9 citation statements)

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“…This prediction model for EGFR mutation had the diagnostic efficiency of 82.7%. Zhao et al [ 102 ] also demonstrated a model based on 18F-FDG PET/CT radiomics features and clinical factors in identifying EGFR mutations, with a concordance index (C-index) value of 0.841, indicating a good clinical utility.…”

Section: Combined Prediction Modelsmentioning

confidence: 99%

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Liu

Xiong

2021

Current Oncology

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Lung cancer ranks first in the incidence and mortality of cancer in the world, of which more than 80% are non-small cell lung cancer (NSCLC). The majority of NSCLC patients are in stage IIIB~IV when they are admitted to hospital and have no opportunity for surgery. Compared with traditional chemotherapy, specific targeted therapy has a higher selectivity and fewer adverse reactions, providing a new treatment direction for advanced NSCLC patients. Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are the widely used targeted therapy for NSCLC patients. Their efficacy and prognosis are closely related to the mutation status of the EGFR gene. Clinically, detecting EGFR gene mutation is often limited by difficulty obtaining tissue specimens, limited detecting technology, and economic conditions, so it is of great clinical significance to find indicators to predict EGFR gene mutation status. Clinicopathological characteristics, tumor markers, liquid biopsy, and other predictors are less invasive, economical, and easier to obtain. They can be monitored in real-time, which is supposed to predict EGFR mutation status and provide guidance for the accurate, individualized diagnosis and therapy of NSCLC patients. This article reviewed the correlation between the clinical indicators and EGFR gene mutation status in NSCLC patients.

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Section: Combined Prediction Modelsmentioning

confidence: 99%

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Liu

Xiong

2021

Current Oncology

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“…In recent years, using machine learning methods with high prediction efficiency and strong feasibility to assess radiomic features and predict EGFR mutation status has become a research “hot spot” [ 5 , 19 – 32 ]. However, most of these studies had small sample sizes, with a total sample number of no more than 200 cases [ 19 – 26 ], and the TNM stages of the enrolled patients varied greatly [ 21 , 27 , 31 ]. These factors significantly affected the stability of the results.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have shown that clinical parameters such as gender, smoking history, and the presence of ground-glass opacity (GGO) are closely related to the EGFR mutation status in lung adenocarcinoma [ 33 ]. Combining with clinical parameters can improve model performance in predicting EGFR mutation status [ 4 , 5 , 26 , 30 ], but some researchers suggest that adding clinical features to the radiomics model does not improve its predictive performance [ 34 , 35 ]. Therefore, whether incorporating clinical parameters can improve the performance of the radiomics model is still inconclusive.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to SUV max , radiomics features can better reflect the spatial distribution of tumors and more comprehensively evaluate tumor heterogeneity. In recent years, using machine learning methods with high prediction efficiency and strong feasibility to assess radiomic features and predict EGFR mutation status has become a research "hot spot" [5,[19][20][21][22][23][24][25][26][27][28][29][30][31][32]. However, most of these studies had small sample sizes, with a total sample number of no more than 200 cases [19][20][21][22][23][24][25][26], and the TNM stages of the enrolled patients varied greatly [21,27,31].…”

Section: Introductionmentioning

confidence: 99%

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The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

et al. 2023

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Background This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Methods A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. Results Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I–II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III–IV lesions (training and testing sets AUC: 0.722 vs. 0.723). Conclusions Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

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“…In recent years, it has been revealed that the diagnostic performance and goodness-of-fit of models utilizing clinical features integrated with TR features are more optimal, with greater clinical benefit in predicting EGFR mutations. Eight studies on TSR are listed in Table 1 , six of which used combined models of radiomics and clinical features ( 22 , 34 , 37 , 40 – 42 ) and achieved superior performance as compared to single radiomics models and single clinical feature models, with the area under the curve (AUC) values of the test set results ranging from 0.81 to 0.87. These data demonstrate the effectiveness of this integrated imaging tool in predicting EGFR mutations.…”

Section: Introductionmentioning

confidence: 99%

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

Ge,

Gao,

Niu

et al. 2023

Front. Oncol.

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Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice.

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Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma

Cited by 10 publications

References 0 publications

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

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