CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses

Mei, Dongdong; Luo, Yan; Wang, Yan; Gong, Jingshan

doi:10.1186/s40644-018-0184-2

Cited by 71 publications

(71 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although interest in quantitative imaging biomarker is increasing, the application of radiomics in thoracic oncology has been limited to prediction of EGFR mutation or survival after treatment . Our study suggests that adding radiomic features to clinical variables could increase predictability for PD‐L1 expression in advanced lung adenocarcinomas, and to our knowledge, this was the first attempt to investigate the value of radiomic features for prediction of PD‐L1 expression.…”

Section: Discussionmentioning

confidence: 86%

“…“Radiomics,” an emerging tool that provides quantitative imaging parameters, has been applied in oncology for tumor assessment and evaluation of the patient's response to treatment (e.g. prediction of EGFR mutation and response to the targeted therapy in NSCLC) . Because a radiomics approach can provide objective and quantitative parameters of the tumor, we hypothesized that quantitative radiomic features can predict PD‐L1 expression in advanced stage lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

“…prediction of EGFR mutation and response to the targeted therapy in NSCLC). [14][15][16][17][18][19] Because a radiomics approach can provide objective and quantitative parameters of the tumor, we hypothesized that quantitative radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas

et al. 2020

View full text Add to dashboard Cite

Background We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD‐L1) expression in advanced stage lung adenocarcinoma. Methods This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD‐L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c‐statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability. Results Among 153 patients, 53 patients were classified as PD‐L1 positive and 100 patients as PD‐L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad‐score by radiomic analysis was higher in the PD‐L1 positive group than in the PD‐L1 negative group with a statistical significance (−0.378 ± 1.537 vs. −1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c‐statistic = 0.646 vs. 0.550, P = 0.0299). Conclusions Quantitative CT radiomic features can predict PD‐L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression. Key points Significant findings of the study Quantitative CT radiomic features can help predict PD‐L1 expression, whereas none of the qualitative imaging findings is associated with PD‐L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression.

show abstract

Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Radiogenomics, a specific field within radiomics, is defined by the correlation between quantitative features, directly extracted from radiological images (imaging phenotype), and genetic information (genotype) 14 . 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 .…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, predictive models for EGFR and KRAS mutation status in lung cancer were developed. Following the current direction in the literature, where the analysis only focuses on the nodule structure and texture 23,24 , we started by using objective radiomic features directly extracted from nodules in CT scans. Then, semantic features, annotated during radiologist evaluation, were used as input.…”

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

View full text Add to dashboard Cite

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

show abstract

Clinical and radiological predictors of epidermal growth factor receptor mutation in nonsmall cell lung cancer

Dang

Wang

Qian

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

PurposeTo determine the prognostic factors of epidermal growth factor receptor (EGFR) mutation status in a group of patients with nonsmall cell lung cancer (NSCLC) by analyzing their clinical and radiological features.Materials and methodsPatients with NSCLC who underwent EGFR mutation detection between 2014 and 2017 were included. Clinical features and general imaging features were collected, and radiomic features were extracted from CT data by 3D Slicer software. Prognostic factors of EGFR mutation status were selected by least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model of EGFR mutation.ResultsA total of 118 patients were enrolled in this study. The smoking index (P = 0.028), pleural retraction (P = 0.041), and three radiomic features were significantly associated with EGFR mutation status. The areas under the ROC curve (AUCs) for prediction models of clinical features, general imaging features, and radiomic features were 0.284, 0.703, and 0.815, respectively, and the AUC for the combined prediction model of the three models was 0.894. Finally, a nomogram was established for individualized EGFR mutation prediction.ConclusionsThe combination of radiomic features with clinical features and general imaging features can enable discrimination of EGFR mutation status better than the use of any group of features alone. Our study may help develop a noninvasive biomarker to identify EGFR mutation status by using a combination of the three group features.

show abstract

CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses

Cited by 71 publications

References 22 publications

Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas

Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas

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

Clinical and radiological predictors of epidermal growth factor receptor mutation in nonsmall cell lung cancer

Contact Info

Product

Resources

About