Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases

Haim, Oz; Абрамов, С. И.; Shofty, Ben; Fanizzi, Claudia; DiMeco, Francesco; Avisdris, Netanell; Ram, Zvi; Artzi, Moran; Grossman, Rachel

doi:10.1007/s11060-022-03946-4

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…Early and non-invasive identification of EGFR mutation status and subtypes is of great importance to guide individual therapy [ 5 , 6 ]. To our knowledge, although radiological characterization for differentiation EGFR mutation status or subtypes has been explored [ 10 – 16 , 24 , 25 ], there was a lack of a classifier that could identify WT EGFR and the two common EGFR mutation subtypes (19Del and 21L858R) simultaneously. Hence, we extracted and fused radiomics features of BM from NSCLC from T1-CE, T2WI, DWI, and T2-FLAIR sequences and developed a DL Radio-GCN model to classify EGFR status at both lesion- and patient-wise.…”

Section: Discussionmentioning

confidence: 99%

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

Li,

Lv,

Chen

et al. 2024

Eur Radiol Exp

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Background To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. Methods A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. Results We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. Conclusions The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. Relevance statement The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. Key points • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

Li,

Lv,

Chen

et al. 2024

Eur Radiol Exp

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“…Haim et al. applied a deep-learning approach, using a ResNet-50 convolutional neural network, to predict EGFR mutation status in NSCLC BMs based on the EGFR testing results from resected BMs ( 20 ). However, they used data from a small cohort of 59 patients, of which only 16 patients were EGFR -positive.…”

Section: Discussionmentioning

confidence: 99%

“…using an independent testing data set, they also assumed that EGFR expression was consistent between the metastatic tumor and the primary tumor, which may not be accurate as discussed above. Haim et al applied a deep-learning approach, using a ResNet-50 convolutional neural network, to predict EGFR mutation status in NSCLC BMs based on the EGFR testing results from resected BMs (20). However, they used data from a small cohort of 59 patients, of which only 16 patients were EGFR-positive.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have evaluated the relationship of radiomics features with the isocitrate dehydrogenase gene status of gliomas (12) or the BRAF gene status of melanoma BMs (13). Although several studies have applied radiomics to identify EGFR mutations in either BMs or primary lung cancers using brain MRI, the study populations were relatively small, especially for patients with EGFR mutations, or the EGFR mutation status of the BMs was determined based on the primary tumor status, rather than samples obtained from the BMs (14)(15)(16)(17)(18)(19)(20)(21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

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Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases

et al. 2022

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BackgroundLung cancer is the most common primary tumor metastasizing to the brain. A significant proportion of lung cancer patients show epidermal growth factor receptor (EGFR) mutation status discordance between the primary cancer and the corresponding brain metastases, which can affect prognosis and therapeutic decision-making. However, it is not always feasible to obtain brain metastases samples. The aim of this study was to establish a radiomic model to predict the EGFR mutation status of lung cancer brain metastases.MethodsData from 162 patients with resected brain metastases originating from lung cancer (70 with mutant EGFR, 92 with wild-type EGFR) were retrospectively analyzed. Radiomic features were extracted using preoperative brain magnetic resonance (MR) images (contrast-enhanced T1-weighted imaging, T1CE; T2-weighted imaging, T2WI; T2 fluid-attenuated inversion recovery, T2 FLAIR; and combinations of these sequences), to establish machine learning-based models for predicting the EGFR status of excised brain metastases (108 metastases for training and 54 metastases for testing). The least absolute shrinkage selection operator was used to select informative features; radiomics models were built with logistic regression of the training cohort, and model performance was evaluated using an independent test set.ResultsThe best-performing model was a combination of 10 features selected from multiple sequences (two from T1CE, five from T2WI, and three from T2 FLAIR) in both the training and test sets, resulting in classification area under the curve, accuracy, sensitivity, and specificity values of 0.85 and 0.81, 77.8% and 75.9%, 83.7% and 73.1%, and 73.8% and 78.6%, respectively.ConclusionsRadiomic signatures integrating multi-sequence MR images have the potential to noninvasively predict the EGFR mutation status of lung cancer brain metastases.

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“…Compared with radiomics model, the end-to-end deep learning method can extract high-order image features by using a self-learning strategy and does not require a precise annotated tumour boundary (Gong et al 2020). For a data-driven algorithm, a relatively large dataset must be collected to train the deep learning model (Wang et al 2019, Haim et al 2022, Zhao et al 2022. Previous deep learning studies have demonstrated promising performance in the risk prediction of lung cancer (Dong et al 2021, Gong et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images

Gong,

Fu,

et al. 2023

Phys. Med. Biol.

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Objective: Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features.Approach: First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N = 654), an independent internal validation cohort (N = 164) and an external validation cohort (N = 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models.Main results: For the two validation cohorts, the feature fusion model achieved AUC values of 0.86±0.03 and 0.80±0.05, which were significantly higher than those of the single-task DNN and radiomics models (all P < 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P > 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P = 0.02) and overall survival (P < 0.005) for validation cohort 2.Significance: The results demonstrate that 1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, 2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and 3) high correlations exist between some deep image and radiomics features.

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Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases

Cited by 15 publications

References 39 publications

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images

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