Can 18F-FDG PET/CT predict EGFR status in patients with non-small cell lung cancer? A systematic review and meta-analysis

Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma

BACKGROUND

To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC).

METHODS

Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training (n = 404) and test sets (n = 112). Several deep learning models were developed utilizing transfer learning, involving CT-only and PET-only models. A dual-stream model fusing PET and CT and a three-stream transfer learning model (TS_TL) integrating clinical data were also developed. Image preprocessing includes semi-automatic segmentation, resampling, and image cropping. Considering the impact of class imbalance, the performance of the model was evaluated using ROC curves and AUC values.

RESULTS

TS_TL model demonstrated promising performance in predicting the EGFR mutation status, with an AUC of 0.883 (95%CI = 0.849-0.917) in the training set and 0.730 (95%CI = 0.629-0.830) in the independent test set. Particularly in advanced LADC, the model achieved an AUC of 0.871 (95%CI = 0.823-0.919) in the training set and 0.760 (95%CI = 0.638-0.881) in the test set. The model identified distinct activation areas in solid or subsolid lesions associated with wild and mutant types. Additionally, the patterns captured by the model were significantly altered by effective tyrosine kinase inhibitors treatment, leading to notable changes in predicted mutation probabilities.

CONCLUSION

PET/CT deep learning model can act as a tool for predicting EGFR mutation in LADC. Additionally, it offers clinicians insights for treatment decisions through evaluations both before and after treatment.

Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis

Objective

This study aimed to evaluate the value of 18F-FDG PET/CT radiomics in predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis.

Methods

The PubMed, Embase, Cochrane Library, Web of Science, and CNKI databases were searched from the earliest available date to June 30, 2023. The meta-analysis was performed using the Stata 15.0 software. The methodological quality and risk of bias of included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score criteria. The possible causes of heterogeneity were analyzed by meta-regression.

Results

A total of 17 studies involving 3763 non-small cell lung cancer patients were finally included. We analyzed 17 training cohorts and 10 validation cohorts independently. Within the training cohort, the application of 18F-FDG PET/CT radiomics in predicting EGFR mutations in NSCLC demonstrated a sensitivity of 0.76 (95% CI: 0.70-0.81) and a specificity of 0.78 (95% CI: 0.74-0.82), accompanied by a positive likelihood ratio of 3.5 (95% CI:3.0-4.2), a negative likelihood ratio of 0.31 (95% CI: 0.24-0.39), a diagnostic odds ratio of 11.0 (95% CI: 8.0-16.0), and an area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87). In the validation cohort, the values included a sensitivity of 0.76 (95% CI: 0.67-0.83), a specificity of 0.75 (95% CI: 0.68-0.80), a positive likelihood ratio of 3.0 (95% CI:2.4-3.8), a negative likelihood ratio of 0.32 (95% CI: 0.24-0.44), a diagnostic odds ratio of 9 (95% CI: 6-15), and an AUC of 0.82 (95% CI: 0.78-0.85). The average Radiomics Quality Score (RQS) across studies was 10.47 ± 4.72. Meta-regression analysis identifies the application of deep learning and regions as sources of heterogeneity.

Conclusion

18F-FDG PET/CT radiomics may be useful in predicting mutation status of the EGFR gene in non-small cell lung cancer.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42022385364.

A Novel Dual-labeled Peptide for Multimodal Imaging of EGFR with L858R Mutation

BACKGROUND

The development of molecular imaging agents targeting epidermal growth factor receptor (EGFR) with L858R mutation may help with the selection of non-small cell lung carcinoma (NSCLCL) patients who may benefit from EFGR tyrosine kinase inhibitor (TKI) therapy.

OBJECTIVE

In this study, we developed 99mTc STHHYYP-GHEG-ECGK-tetramethylrhodamine (STHHYYP-ECGK-TAMRA) to target EGFR with L858R mutation in NSCLC tumors and verified its probability as a molecular imaging agent.

METHODS

Fmoc solid-phase peptide synthesis was used to synthesize STHHYYP-ECGKTAMRA. 99mTc labelled STHHYYP-ECGK-TAMRA was prepared. Gamma imaging, fluorescent imaging and biodistribution were performed in murine models bearing NCI-H1975 and NCI-H1650 tumors.

RESULTS

The binding affinity value (Kd) of 99mTc STHHYYP-ECGK-TAMRA was estimated to be 130.6 ± 29.2 nM in NCI-H1975 cells. The gamma camera images showed a substantial uptake of 99mTc STHHYYP-ECGK-TAMRA in the NCI-H1975 tumor. The % injected dose/gram of the NCI-H1975 tumor tissue was 2.77 ± 0.70 and 3.48 ± 1.01 at 1 and 3 h, respectively.

CONCLUSION

Specific binding of 99mTc STHHYYP-ECGK-TAMRA to L858R-mutated EGFRpositive NCI-H1975 cells and tumors was demonstrated in in vivo and in vitro studies. The results suggest that 99mTc STHHYYP-ECGK-TAMRA is a good candidate agent for dualmodality imaging targeting EGFR with L858R mutation.

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

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.

Predictive value of intratumor metabolic and heterogeneity parameters on [18F]FDG PET/CT for EGFR mutations in patients with lung adenocarcinoma

PURPOSE

This study aimed to investigate the value of metabolic and heterogeneity parameters of 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) in predicting epidermal growth factor receptor (EGFR) mutations in patients with lung adenocarcinoma (ADC).

MATERIALS AND METHODS

A retrospective analysis was performed on 157 patients with lung ADC between September 2015 and June 2021, who had undergone both EGFR mutation testing and [¹⁸F]FDG PET/CT examination. Metabolic and heterogeneity parameters were measured and calculated, including maximum diameter (Dmax), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity factor (HF). Relationships between PET/CT parameters and EGFR mutation status were evaluated and a multivariate logistic regression analysis was analyzed to establish a combined prediction model.

RESULTS

108 (68.8%) patients exhibited EGFR mutations. EGFR mutations were more likely to occur in females (51.9% vs. 48.1%, P = 0.007), non-smokers (83.3% vs. 16.7%, P < 0.001) and right lobes (55.6% vs. 44.4%, P = 0.017). High Dmax, MTV and HF and low SUVmean were significantly correlated with EGFR mutations, and the areas under the ROC curve (AUCs) measuring 0.647, 0.701, 0.757, and 0.661, respectively. Multivariate logistic regression analysis suggested that non-smokers (OR = 0.30, P = 0.034), low SUVmean (≤ 7.75, OR = 0.63, P < 0.001) and high HF (≥ 4.21, OR = 1.80, P = 0.027) were independent predictors of EGFR mutations. The AUC of the combined prediction model measured up to 0.863, significantly higher than that of a single parameter.

CONCLUSIONS

EGFR mutant in lung ADC patients showed more intratumor heterogeneity (HF) than EGFR wild type, which was combined clinical feature (non-smokers), and metabolic parameter (SUVmean) may be helpful in predicting EGFR mutation status, thus playing a guiding role in EGFR-tyrosine kinase inhibitors (EGFR-TKIs) targeted therapies.

Radiolabeled EGFR TKI as predictive imaging biomarkers in NSCLC patients – an overview

Non-small cell lung cancer (NSCLC) has one of the highest cancer-related mortality rates worldwide. In a subgroup of NSCLC, tumor growth is driven by epidermal growth factor receptors (EGFR) that harbor an activating mutation. These patients are best treated with EGFR tyrosine kinase inhibitors (EGFR TKI). Identifying the EGFR mutational status on a tumor biopsy or a liquid biopsy using tumor DNA sequencing techniques is the current approach to predict tumor response on EGFR TKI therapy. However, due to difficulty in reaching tumor sites, and varying inter- and intralesional tumor heterogeneity, biopsies are not always possible or representative of all tumor lesions, highlighting the need for alternative biomarkers that predict tumor response. Positron emission tomography (PET) studies using EGFR TKI-based tracers have shown that EGFR mutational status could be identified, and that tracer uptake could potentially be used as a biomarker for tumor response. However, despite their likely predictive and monitoring value, the EGFR TKI-PET biomarkers are not yet qualified to be used in the routine clinical practice. In this review, we will discuss the currently investigated EGFR-directed PET biomarkers, elaborate on the typical biomarker development process, and describe how the advances, challenges, and opportunities of EGFR PET biomarkers relate to this process on their way to qualification for routine clinical practice.

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

Tyrosine kinase inhibitors (TKIs) are a significant treatment strategy for the management of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation status. Currently, EGFR mutation status is established based on tumor tissue acquired by biopsy or resection, so there is a compelling need to develop non-invasive, rapid, and accurate gene mutation detection methods. Non-invasive molecular imaging, such as positron emission tomography/computed tomography (PET/CT), has been widely applied to obtain the tumor molecular and genomic features for NSCLC treatment. Recent studies have shown that PET/CT can precisely quantify EGFR mutation status in NSCLC patients for precision therapy. This review article discusses PET/CT advances in predicting EGFR mutation status in NSCLC and their clinical usefulness.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

A novel dual-labeled small peptide as a multimodal imaging agent for targeting wild-type EGFR in tumors

The epidermal growth factor receptor (EGFR) is over-expressed in various human cancer. The over-expression of EGFR in tumors is an excellent target for the development of cancer imaging agents. In the present study, we developed Tc-99m SYPIPDT-GHEG-ECG-K-tetramethylrhodamine (SYPIPDT-ECG-TAMRA) as a molecular imaging agent targeting wild-type EFGR (wtEGFR)-positive tumor cells, and verified its feasibility as molecular imaging agent. SYPIPDT-ECG-TAMRA was synthesized using Fmoc solid-phase peptide synthesis. The radiolabeling of SYPIPDT-ECG-TAMRA with Tc-99m was accomplished using ligand exchange via tartrate. Cellular uptake and binding affinity studies were performed. In vivo gamma camera imaging, ex vivo imaging and biodistribution studies were performed using NCI-H460 and SW620 tumor-bearing murine models. After radiolabeling procedures with Tc-99m, Tc-99m SYPIPDT-ECG-TAMRA complexes were prepared at high yield (> 95%). The binding affinity value (K_d) of Tc-99m SYPIPDT-ECG-TAMRA for NCI-H460 cells was estimated to be 76.5 ± 15.8 nM. In gamma camera imaging, the tumor to normal muscle uptake ratios of Tc-99m SYPIPDT-ECG-TAMRA increased with time (2.7 ± 0.6, 4.0 ± 0.9, and 6.2 ± 1.0 at 1, 2, and 3 h, respectively). The percentage injected dose per gram of wet tissue for the NCI-H460 tumor was 1.91 ± 0.11 and 1.70 ± 0.22 at 1 and 3 h, respectively. We developed Tc-99m SYPIPDT-ECG-TAMRA, which is dual-labeled with both radioisotope and fluorescence. In vivo and in vitro studies demonstrated specific uptake of Tc-99m SYPIPDT-ECG-TAMRA into wtEGFR-positive NCI-H460 cells and tumors. Thus, the results of the present study suggest that Tc-99m SYPIPDT-ECG-TAMRA is a potential dual-modality imaging agent targeting wtEGFR.