Dataset Information

Comparison of plasma ctDNA and tissue/cytology-based techniques for the detection of EGFR mutation status in advanced NSCLC: Spanish data subset from ASSESS.

ABSTRACT: PURPOSE:The analysis of epidermal growth factor receptor (EGFR) mutations in many patients with advanced non-small-cell lung cancer (aNSCLC) has provided the opportunity for successful treatment with specific, targeted EGFR tyrosine kinase inhibitors. However, this therapeutic decision may be challenging when insufficient tumor tissue is available for EGFR mutation testing. Therefore, blood surrogate samples for EGFR mutation analysis have been suggested. METHODS:Data were collected from the Spanish cohort of patients in the large, non-interventional, diagnostic ASSESS study (NCT01785888) evaluating the utility of circulating free tumor-derived DNA from plasma for EGFR mutation testing. The incidence of EGFR mutation in Spain and the level of concordance between matched tissue/cytology and plasma samples were evaluated. RESULTS:In a cohort of 154 eligible patients, EGFR mutations were identified in 15.1 and 11.0% of tumor and plasma samples, respectively. The most commonly used EGFR mutation testing method for the tumor tissue samples was the QIAGEN Therascreen® EGFR RGQ PCR kit (52.1%). Fragment Length Analysis?+?PNA LNA Clamp was used for the plasma samples. The concordance rate for EGFR mutation status between the tissue/cytology and plasma samples was 88.8%; the sensitivity was 45.5%, and the specificity was 96.7%. CONCLUSIONS:The high concordance between the different DNA sources for EGFR mutation testing supports the use of plasma samples when tumor tissue is unavailable.

SUBMITTER: Arriola E

PROVIDER: S-EPMC6153859 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

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Comparison of plasma ctDNA and tissue/cytology-based techniques for the detection of EGFR mutation status in advanced NSCLC: Spanish data subset from ASSESS.

Arriola E E Paredes-Lario A A García-Gomez R R Diz-Tain P P Constenla M M García-Girón C C Márquez G G Reck M M López-Vivanco G G

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico 20180405 10

<h4>Purpose</h4>The analysis of epidermal growth factor receptor (EGFR) mutations in many patients with advanced non-small-cell lung cancer (aNSCLC) has provided the opportunity for successful treatment with specific, targeted EGFR tyrosine kinase inhibitors. However, this therapeutic decision may be challenging when insufficient tumor tissue is available for EGFR mutation testing. Therefore, blood surrogate samples for EGFR mutation analysis have been suggested.<h4>Methods</h4>Data were collect ...[more]

PMID: 29623586

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Project description:To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

Project description:Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

Dataset Information

Comparison of plasma ctDNA and tissue/cytology-based techniques for the detection of EGFR mutation status in advanced NSCLC: Spanish data subset from ASSESS.

Publications

Comparison of plasma ctDNA and tissue/cytology-based techniques for the detection of EGFR mutation status in advanced NSCLC: Spanish data subset from ASSESS.

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