Recent and current advances in PET/CT imaging in the field of predicting epidermal growth factor receptor mutations in non-small cell lung cancer

Hu, Na; Yan, Gang; Wu, Yuhui; Wang, Li; Wang, Yan; Xiang, Ye; Lei, Pinggui; Luo, Peng

doi:10.3389/fonc.2022.879341

Cited by 2 publications

(2 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent studies have shown that non-invasive approaches based on PET/CT, for example, analysis of 18 F-FDG uptake (SUVmax, metabolic volume, tumor lesion glucose) and 18 F-FDG PET/CT-derived radiomics ( 46 , 47 ) could help quantify EGFR mutation status and predict prognosis in lung cancer patients for precision therapy. However, in some previous studies, the results of 18 F-FDG uptake were conflicting ( 9 , 10 ).…”

Section: Discussionmentioning

confidence: 99%

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Zuo

Liu

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

PurposeTo develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric 18F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome.MethodsThe 18F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models’ interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis. ResultsAmong the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863.ConclusionsThe integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric 18F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Zuo

Liu

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Over the past decade, 18F-FDG PET/CT radiomics has been applied to multiple aspects of cancer, such as the identification of benign and malignant solitary pulmonary nodules (20), lung cancer pathological typing and staging (21), prediction of gene mutation and molecular phenotypes (22), and evaluation of efficacy and prognosis (23). Amassed evidence substantiates the practicality and promising benefits of 18F-FDG PET/CT radiomics for prognosticating EGFR mutation classifications and subtypes (24). Radiomics methodology significantly mitigates patient discomfort and potential complications while delivering expedited diagnostic feedback and a comprehensive evaluation of tumor heterogeneity (25).…”

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.

View full text Add to dashboard Cite

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.

show abstract

Recent and current advances in PET/CT imaging in the field of predicting epidermal growth factor receptor mutations in non-small cell lung cancer

Cited by 2 publications

References 72 publications

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

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

Contact Info

Product

Resources

About