Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology

Tu, Wenting; Sun, Guangyuan; Fan, Li; Wang, Yun; Yi, Xin; Guan, Yu; Li, Qiong; Zhang, Di; Liu, Shiyuan; Li, Zhaobin

doi:10.1016/j.lungcan.2019.03.025

Cited by 121 publications

(106 citation statements)

References 33 publications

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“…findings in other radiomic studies (15,16,20). Among the selected radiomic features, Original_Firstorder_90Percentile, Original_Firstorder_Maximum, and Wavelet-LHH_GLDM_LDHGLE were the most significant and robust features associated with ALK mutations, which reflect tumour's intensity and textural features surrounding and within the high-intensity CT voxels.…”

Section: Discussionmentioning

confidence: 57%

“…However, the evaluation of these conventional CT features depends heavily on the radiologist's experience and is time-consuming. Radiomics is a computer-based approach that has been widely applied Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; CT, computed tomography; DICOM, digital imaging and communications in medicine; GGO, ground-glass opacity; GLCM, gray level co-occurrence matrix; GLSZM, gray level size zone matrix; GLRLM, gray level run-length matrix; GLDM, gray level dependence matrix; AIS, adenocarcinoma in situ; MIA, minimally invasive adenocarcinoma; IAC, invasive adenocarcinoma; NCCN, National Comprehensive Cancer Network; NSCLC, non-small cell lung cancer; CEA, carcinoembryonic antigen; DBSCAN, density-based spatial clustering of applications with noise; RFE, recursive feature elimination; LR, logistic regression; DT, decision tree in the diagnosis of lung neoplasm as well as the prediction of survival and gene mutations in lung cancer (15)(16)(17)(18). It could help radiologists to identify additional information about tumor phenotype that is distinct from conventional findings of CT images (15,16,(19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

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Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Song

Zhu

Mao³

et al. 2020

Front. Oncol.

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Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features. Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test. Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83-0.88, P = 0.01), but not in the test cohort (AUC = 0.80-0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns Song et al. Radiomic-Based Model for ALK Mutations in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma. Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Song

Zhu

Mao³

et al. 2020

Front. Oncol.

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“…Reproducible features in terms of segmentation difference are usually identified with the criterion of ICC > 0.75-0.80 for each feature in most radiomics studies [12,28,[34][35][36]. One of the reasons for defining the ICC criterion > 0.75-0.80 may be that it is necessary for constructing a prognostic or classification model to significantly reduce the feature's dimensions.…”

Section: Discussionmentioning

confidence: 99%

Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients

et al. 2020

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“…The attempts to define the association between imaging textures (CT, for example) and cancer genomic phenotypes have accelerated the development of "radiogenomics". By extracting high throughout of CT series, radiogenomics allows the noninvasive diagnosis of tumor gene mutations in NSCLC EGFR mutation prediction, 20 prediction of pathological stage in NSCLC using machine learning 21 or deep learning 22 and NSCLC multi-subtype classifications. 23 Whilst several previous studies have demonstrated certain radiological characteristics associated with NSCLC ALK+ and built a diagnostic model 24 based on radiological characteristics and clinical factors, the role of CT based radiogenomics machine learning in ALK+ solid lung adenocarcinoma remains to be explored.…”

Section: Introductionmentioning

confidence: 99%

<p>Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers</p>

Ma¹,

Gao²,

Dan³

et al. 2020

OTT

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To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods: This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 precontrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPVof pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538-0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630-0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion: Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard postcontrast CT radiomics biomarkers produced superior performance compared with that of precontrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.

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Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology

Cited by 121 publications

References 33 publications

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients

<p>Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers</p>

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