3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

Liu, Guixue; Xu, Zhihan; Ge, Yubin; Jiang, Beibei; Groen, Harry J.M.; Vliegenthart, Rozemarijn; Xie, Xueqian

doi:10.21037/tlcr-20-122

Cited by 34 publications

(40 citation statements)

References 46 publications

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“…This approach can be used to determine the molecular type of lung tumors based on the phenotypic appearance in computed tomography (CT) [ 18 ]. Several studies have reported encouraging results in discriminating EGFR mutation using radiomics [ 19 , 20 ]. For example, Jia et al built a random forest classifier to identify EGFR mutation and reached an area under the receiver operating characteristics curve (AUC) of 0.802 [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics

Zhang

Liu

et al. 2021

Cancers

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Purpose: To develop a machine learning-derived radiomics approach to simultaneously discriminate epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene (KRAS), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and tumor protein 53 (TP53) genetic mutations in patients with non-small cell lung cancer (NSCLC). Methods: This study included consecutive patients from April 2018 to June 2020 who had histologically confirmed NSCLC, and underwent pre-surgical contrast-enhanced CT and post-surgical next-generation sequencing (NGS) tests to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations. A dedicated radiomics analysis package extracted 1672 radiomic features in three dimensions. Discriminative models were established using the least absolute shrinkage and selection operator to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations, based on radiomic features and relevant clinical factors. Results: In 134 patients (63.6 ± 8.9 years), the 20 most relevant radiomic features (13 for KRAS) to mutations were selected to construct models. The areas under the curve (AUCs) of the combined model (radiomic features and relevant clinical factors) for discriminating EGFR, KRAS, ERBB2, and TP53 mutations were 0.78 (95% CI: 0.70–0.86), 0.81 (0.69–0.93), 0.87 (0.78–0.95), and 0.84 (0.78–0.91), respectively. In particular, the specificity to exclude EGFR mutations was 0.96 (0.87–0.99). The sensitivity to determine KRAS, ERBB2, and TP53 mutations ranged from 0.82 (0.69–90) to 0.92 (0.62–0.99). Conclusions: Machine learning-derived 3D radiomics can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations in patients with NSCLC. This noninvasive and low-cost approach may be helpful in screening patients before invasive sampling and NGS testing.

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Section: Introductionmentioning

confidence: 99%

Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics

Zhang

Liu

et al. 2021

Cancers

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“…There are different treatment methods in mutated EGFR group and wild type EGFR group (18), so generally, their prognosis was studied separately. However, many previous studies have shown that radiomic has a high accuracy in distinguishing wild and mutated EGFR NSCLC patients (19,20), therefore, we combined two groups to increase the universality of the models. We also performed subgroup analysis which indicated that the radiomic signature own higher discrimination capacity for wild type EGFR group than mutated EGFR subgroup for all cut-off time points.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients

Hong

Zhang

et al. 2021

Front. Oncol.

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PurposeThe purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.MethodsThis study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.ResultsThirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.ConclusionsRadiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.

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“…Machine-learning-based radiomics are a powerful tool in thoracic oncology, using an exploratory analysis to detect complex patterns that cannot be recognized by traditional analyses. In particular, some publications reported that quantifiable radiomics features extracted from the VOI within CT images can provide more information not only for subtype but also genetic information and immune infiltration of tumors 18 – 20 . Most cancer cases are subjected to multiple rounds of CT, MRI, and PET-CT prior to thoracotomy, accumulating a large amount of data; however, modern CT, MRI and combined PET-CT units are not standardized for image acquisition and reconstruction protocols.…”

Section: Discussionmentioning

confidence: 99%

“…With the introduction of radiomics-based machine-learning analyses, several reports have provided new insight into aspects such as the diagnosis of cancer and prognosis prediction 18 – 20 . Recently, two groups reported that radiomics-based analyses of CT findings could predict the existence of STAS in lung adenocarcinoma 21 , 22 .…”

Section: Introductionmentioning

confidence: 99%

Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

Onozato

Nakajima

Yokota

et al. 2021

Sci Rep

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Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.

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3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

Cited by 34 publications

References 46 publications

Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics

Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics

Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients

Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

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