Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling

Jia, Tian-Ying; Xiong, Junfeng; Li, Xiaoyang; Wen, Yu; Xu, Zhiyong; Cai, Xu-Wei; Ma, Jingchen; Ren, Yi; Larsson, Rasmus; Zhang, Jie; Zhao, Jun; Fu, Xiaolong

doi:10.1007/s00330-019-06024-y

Cited by 128 publications

(149 citation statements)

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“…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%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The mechanism of serum albumin with respect to immunotherapy response is yet to be established however, it was used in The Gustave Roussy Immune Score as a prognostic marker in immunotherapy phase I trials 40 . Emerging evidence demonstrates the utility of radiomics as a non-invasive approach to quantify and predict lung cancer treatment response of tyrosine kinase inhibitors 41,42 , platinum-based chemotherapy 43 , neo-adjuvant chemo-radiation 44,45 , stereotactic body radiation therapy 46,47 , and immunotherapy 8,48,49 . With respect to immunotherapy treatment response, our group previously demonstrated that pre-treatment clinical covariates and radiomic features predicted rapid disease progression phenotypes, including hyperprogression (AUROCs ranging 0.804-0.865) among 228 NSCLC patients treated with single agent or double agent immunotherapy 8 .…”

Section: Discussionmentioning

confidence: 99%

Hypoxia-related radiomics predict immunotherapy response: A multi-cohort study of NSCLC

Tunali

Tan

Gray

et al. 2020

Preprint

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word count: 398 Word count: 4245 Abstract Checkpoint blockade immunotherapy provides improved long-term survival in a subset of advanced stage nonsmall cell lung cancer (NSCLC) patients. However, highly predictive biomarkers of immunotherapy response are unmet clinical need. In this study, we utilized pre-treatment clinical factors and quantitative image-based biomarkers (radiomics) to identify a parsimonious model that predicts survival outcomes among NSCLC patients treated with immunotherapy. The NSCLC patients treated with single or double agent immunotherapy were included in three different cohorts: Training (N = 180), test (N = 90) and validation (N = 62) cohorts. The models were created based on overall survival (OS) and were additionally assessed for progression-free survival (PFS). Including most predictive radiomic features and clinical covariates, Classification and Regression Tree analysis was applied to stratify patients into survival risk-groups in the training cohort. The risk groups were later generated in the test and validation cohorts. Four independent NSCLC cohorts (total N = 446) were utilized for further validation of the radiomic signature. The biological underpinnings of the most informative radiomics were assessed using gene expression data from a radiogenomics dataset and validated by immunohistochemistry data (IHC). A parsimonious clinical-radiomics model was found to be significantly associated with OS and PFS after stratifying patients into groups of low-, moderate-, high-, and very-high risk of death and progression. This trained model was further tested and validated in two independent cohorts. When the extreme phenotypes were compared, the very-high risk group was found to be associated with extremely poor OS in both the test (hazard ratio [HR] = 5.35, 95% confidence interval [CI]: 2.14 -13.36; 1-year OS = 11.1%) and validation (HR = 13.81, 95% CI: 2.58 -73.93; 1year OS = %47.6) cohorts when compared to the low risk group (HRs = 1.00; 1-year OS = 85.0% & 80.2%).Similar findings were observed for PFS. The final radiomic feature (GLCM inverse difference) was associated with OS in four independent NSCLC cohorts and was found to be positively associated with the hypoxia-related carbonic anhydrase, CAIX, by gene expression profiling and immunohistochemistry. We validated a novel clinical-radiomics model that is associated with OS and PFS among NSCLC patients treated with immunotherapy and identified a highly vulnerable subset of patients that are unlikely to respond to immunotherapy. The most informative radiomic feature was associated with CAIX, a marker of tumor hypoxia, tumor acidosis, and treatment resistance.

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“…Radiomic features extracted from treatment planning computed tomography (CT) images may be useful for the prognostic prediction of SBRT for lung cancer [6][7][8][9][10]. Radiomics in lung cancer has also been performed using diagnostic CT images for feature extraction [11][12][13][14][15]. In diagnostic CT scanning, contrast-enhanced scans are sometimes conducted.…”

Section: Introductionmentioning

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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Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling

Cited by 128 publications

References 12 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

Hypoxia-related radiomics predict immunotherapy response: A multi-cohort study of NSCLC

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

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