Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning

Wang, Shuo; Shi, Jingyun; Ye, Zhaoxiang; Dong, Di; Yu, Dongdong; Zhou, Mu; Liu, Ying; Gevaert, Olivier; Wang, Kun; Zhu, Yongbei; Zhou, Hongyu; Liu, Zhenyu; Tian, Jie

doi:10.1183/13993003.00986-2018

Cited by 342 publications

(317 citation statements)

References 39 publications

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“…Radiomics, which involves extracting quantitative features from medical images, is capable of generating imaging biomarkers as decision support tools for clinical practice [18][19][20][21][22][23][24][25][26]. The traditional radiomics method utilizes single-phase medical images for evaluation or prediction, which neglects the tumor change during treatment or following up.…”

Section: Introductionmentioning

confidence: 99%

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

et al. 2020

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Background: The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination. Methods: A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA). Results:The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model. Conclusion:The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.

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Section: Introductionmentioning

confidence: 99%

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

et al. 2020

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“…into mineable data (16,17), which has been widely applied in the prediction of preoperative distant metastasis, histologic subtype classification, and so on (18)(19)(20). Prognosis based on radiomics is gaining popularity as associations between radiomic features and the underlying genomic patterns emerge in various cancers (21)(22)(23)(24). On one hand, a latest study showed that a subset of radiomic features were able to consistently capture texture information about the underlying tissue histology, but some of them were incapable to be observed at the purely human level (25,26).…”

Section: Introductionmentioning

confidence: 99%

CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study

Zhang

Wang

et al. 2020

Front. Oncol.

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Objectives: To identify a computed tomography (CT)-based radiomic signature for predicting progression-free survival (PFS) in stage IV anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer (NSCLC) patients treated with tyrosine kinase inhibitor (TKI) crizotinib. Materials and Methods:This retrospective proof-of-concept study included a cohort of 63 stage IV ALK-positive NSCLC patients who had received TKI crizotinib therapy for model construction and validation. Another independent cohort including 105 stage IV EGFR-positive NSCLC patients was also used for external validation in EGFR-TKI treatment. We initially extracted 481 quantitative three-dimensional features derived from manually segmented tumor volumes of interest. Pearson's correlation analysis along with the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression was successively performed to select critical radiomic features. A CT-based radiomic signature for PFS prediction was obtained using multivariate Cox regression. The performance evaluation of the radiomic signature was conducted using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and Kaplan-Meier survival analysis.Results: A radiomic signature containing three features showed significant prognostic performance for ALK-positive NSCLC patients in both the training cohort (C-index, 0.744; time-dependent AUC, 0.895) and the validation cohort (C-index, 0.717; time-dependent AUC, 0.824). The radiomic signature could significantly risk-stratify ALK-positive NSCLC patients (hazard ratio, 2.181; P < 0.001) and outperformed other prognostic factors. However, no significant association with PFS was captured for the radiomic signature in the EGFR-positive NSCLC cohort (log-rank tests, P = 0.41).Li et al. Prognostic Factor for ALK-Positive NSCLC Conclusions:The CT-based radiomic features can capture valuable information regarding the tumor phenotype. The proposed radiomic signature was found to be an effective prognostic factor in stage IV ALK mutated nonsynchronous nodules in NSCLC patients treated with a TKI.

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“…The most recent review and meta-analysis of CT and clinical characteristics to predict the risk of EGFR mutation confirms that CT features with the highest correlation with EGFR mutation are from the nodule and other structures of the lung 30 . Also, another work based on deep learning techniques with an interpretable visual output, identified that the regions surrounding the nodule were the most relevant for the classification decision 18,31 . In our opinion, it is crucial to emphasise this characteristic, as it might change the direction and broaden the analysis spectrum of future radiogenomics studies, which until now have been mainly focusing on the nodule or in a region of interest (ROI) around it [32][33][34] .…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Studies in lung cancer have presented the association between EGFR mutation status and quantitative features extracted from computed tomography (CT) scans [14][15][16][17] . The most recent methods are based on convolutional neural networks, which are end-to-end approaches that allow to automatically learn the whole process, reducing the subjectivity and human effort 14,18 . Also, regarding qualitative features, recent works have shown that semantic human annotations of CT scans can be used to train a model to accurately predict EGFR mutation status, although the same was not verifiable for KRAS 19,20 .…”

Section: Introductionmentioning

confidence: 99%

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Pinheiro

Pereira

Dias

et al. 2019

Preprint

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EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. Biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes, however, the same does not seem to hold true for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures. 2/103/10 7/10 is balanced individually for each fold using SMOTE-NC, avoiding data leakage. After parameter optimisation, probabilistic outputs of each model with optimal parameters were analysed using the AUC of Receiver Operating Characteristic (ROC). ROC is a probability curve and AUC represents degree or measure of separability, telling how much model is capable of distinguishing between classes. The ROC curve is plotted with True Positive Rate (TPR) against the False Positive Rate (FPR), usually with TPR on the y-axis and FPR on the x-axis. Experimental DesignWe designed four experiments in order to test and compare which type of input features allow to achieve better performance in gene mutation status prediction. We first trained a model that took nodule-related radiomic features as input. Then, for direct comparison purposes and to allow a modular evaluation, we split the semantic data into three parts: nodule, non-nodule and hybrid. The first one contains only nodular information, the second one contains only information external to the nodule and the third one is the combination of both. The split can be seen in detail in Table 2 of the supplementary material. 10/10

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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning

Cited by 342 publications

References 39 publications

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

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