Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Nguyen, Hung Song; Ho, Dang Khanh Ngan; Nguyen, Nam Nhat; Tran, Huy; Tam, Ka‐Wai; Le, Nguyen Quoc Khanh

doi:10.1016/j.acra.2023.03.040

Cited by 26 publications

(14 citation statements)

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“…Other similar studies have shown a similar utility of DL models with improvements in the AUC when combined with clinical parameters [ 25 , 26 , 27 , 28 ]. Further, a study on the PET/CT fusion algorithm using a dataset of 150 patients showed a prediction accuracy of EGFR and non-EGFR mutations of 86.25% in the training dataset and 81.92% in the validation set [ 29 ].…”

Section: Discussionmentioning

confidence: 64%

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Mahajan,

Kania,

Agarwal

et al. 2024

Cancers

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Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model’s accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction.

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Section: Discussionmentioning

confidence: 64%

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Mahajan,

Kania,

Agarwal

et al. 2024

Cancers

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“…Furthermore, our evaluation framework using bootstrap-bias-corrected 10-times-repeated 20-fold crossvalidation accounts for the variance introduced by an arbitrary single train/test split in small sample sizes [18,27] and measures both calibration and discrimination [27]. Other authors applied radiomics to primary pulmonary neoplasms and metastases in CECT and PET/CT [28][29][30][31][32][33]. Predicted outcomes were composed of the severity of pulmonary lesions, epidermal growth factor receptor status, or survival outcomes [28][29][30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

“…Other authors applied radiomics to primary pulmonary neoplasms and metastases in CECT and PET/CT [28][29][30][31][32][33]. Predicted outcomes were composed of the severity of pulmonary lesions, epidermal growth factor receptor status, or survival outcomes [28][29][30][31][32][33]. In contrast, we focus on the per-node dignity of thoracic lymph nodes, where rather little research has been conducted [23,24].…”

Section: Discussionmentioning

confidence: 99%

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Laqua

Woźnicki

Bley

et al. 2023

Cancers

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Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional “hand-crafted” radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865–0.878), SBS 35.8 (34.2–37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.

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“…22 Features that would serve as predictors were obtained using a variety of supervised methods (ie, random forests, support vector machines, boosting, bagging, LASSO and Ridge regression). [25][26][27] After testing these methods, we determined that random forests provided relevant predictors at reduced mean-squared error compared with other approaches. Predictors for the algorithm were derived from random forests applied to randomly selected groups of 1000 observations at a time, and regressing 10 variables in each run.…”

Section: Model Development: Feature Selectionmentioning

confidence: 99%

Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study

Padula,

Armstrong,

Pronovost

et al. 2024

BMJ Open

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ObjectiveTo predict the risk of hospital-acquired pressure injury using machine learning compared with standard care.DesignWe obtained electronic health records (EHRs) to structure a multilevel cohort of hospitalised patients at risk for pressure injury and then calibrate a machine learning model to predict future pressure injury risk. Optimisation methods combined with multilevel logistic regression were used to develop a predictive algorithm of patient-specific shifts in risk over time. Machine learning methods were tested, including random forests, to identify predictive features for the algorithm. We reported the results of the regression approach as well as the area under the receiver operating characteristics (ROC) curve for predictive models.SettingHospitalised inpatients.ParticipantsEHRs of 35 001 hospitalisations over 5 years across 2 academic hospitals.Main outcome measureLongitudinal shifts in pressure injury risk.ResultsThe predictive algorithm with features generated by machine learning achieved significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.72; whereas standard care only achieved an area under the ROC curve of 0.52. At a specificity of 0.50, the predictive algorithm achieved a sensitivity of 0.75.ConclusionsThese data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.

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Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Cited by 26 publications

References 60 publications

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study

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