Dataset Information

Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.

ABSTRACT: Identifying an epidermal growth factor receptor (EGFR) mutation is important because EGFR tyrosine kinase inhibitors are the first-line treatment of choice for patients with EGFR mutation-positive lung adenocarcinomas (LUAC). This study is aimed at developing and validating a radiomics-based machine learning (ML) approach to identify EGFR mutations in patients with LUAC. We retrospectively collected data from 201 patients with positive EGFR mutation LUAC (140 in the training cohort and 61 in the validation cohort). We extracted 1316 radiomics features from preprocessed CT images and selected 14 radiomics features and 1 clinical feature which were most relevant to mutations through filter method. Subsequently, we built models using 7 ML approaches and established the receiver operating characteristic (ROC) curve to assess the discriminating performance of these models. In terms of predicting EGFR mutation, the model derived from radiomics features and combined models (radiomics features and relevant clinical factors) had an AUC of 0.79 (95% confidence interval (CI): 0.77-0.82), 0.86 (0.87-0.88), respectively. Our study offers a radiomics-based ML model using filter methods to detect the EGFR mutation in patients with LUAC. This convenient and low-cost method may be of help to noninvasively identify patients before obtaining tumor sample for molecule testing.

SUBMITTER: Lu J

PROVIDER: S-EPMC9107363 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.

Lu Jiameng J Ji Xiaoqing X Wang Lixia L Jiang Yunxiu Y Liu Xinyi X Ma Zhenshen Z Ning Yafei Y Dong Jie J Peng Haiying H Sun Fei F Guo Zihan Z Ji Yanbo Y Xing Jianping J Lu Yue Y Lu Degan D

Disease markers 20220507

Identifying an epidermal growth factor receptor (EGFR) mutation is important because EGFR tyrosine kinase inhibitors are the first-line treatment of choice for patients with EGFR mutation-positive lung adenocarcinomas (LUAC). This study is aimed at developing and validating a radiomics-based machine learning (ML) approach to identify EGFR mutations in patients with LUAC. We retrospectively collected data from 201 patients with positive EGFR mutation LUAC (140 in the training cohort and 61 in the ...[more]

PMID: 35578691

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Project description:Object To evaluate the difference between multiple primary lung adenocarcinoma (MPLA) and solitary primary lung adenocarcinoma (SPLA) by delta-radiomics based machine learning algorithms in CT images. Methods A total of 1094 patients containing 268 MPLAs and 826 SPLAs were recruited for this retrospective study between 2014 to 2020. After the segmentation of volume of interest, the radiomic features were automatically calculated. The patients were categorized into the training set and testing set by a random proportion of 7:3. After feature selection, the relevant classifiers were constructed by the machine learning algorithms of Bayes, forest, k-nearest neighbor, logistic regression, support vector machine, and decision tree. The relative standard deviation (RSD) was calculated and the classification model with minimal RSD was chosen for delta-radiomics analysis to explore the variation of tumor during follow-up surveillance in the cohort of 225 MPLAs and 320 SPLAs. According to the different follow-up duration, it was divided into group A (3–12 months), group B (13–24 months), and group C (25–48 months). Then the corresponding delta-radiomics classifiers were developed to predict MPLAs. The area under the receiver operator characteristic curve (AUC) with 95% confidence interval (CI) was quantified to evaluate the efficiency of the model. Results To radiomics analysis, the forest classifier (FC-radio) with the minimal RSD showed the better stability with AUCs of 0.840 (95%CI, 0.810–0.867) and 0.670 (95%CI, 0.611–0.724) in the training and testing set. The AUCs of the forest classifier based on delta-radiomics (FC-delta) were higher than those of FC-radio. In addition, with the extension of follow-up duration, the performance of FC-delta in Group C were the best with AUCs of 0.998 (95%CI, 0.993–1.000) in the training set and 0.853 (95%CI, 0.752–0.940) in the testing set. Conclusions The machine-learning approach based on radiomics and delta-radiomics helped to differentiate SPLAs from MPLAs. The FC-delta with a longer follow-up duration could better distinguish between SPLAs and MPLAs. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10036-1.

Project description:BackgroundThe nuclear translocation of epidermal growth factor receptor (EGFR) has been considered to play a role in carcinogenesis. However, the relevance of differentially located EGFR proteins in lung cancer remains unclear.MethodsWe examined 161 patients with primary lung adenocarcinoma to detect EGFR expression in lung cancer cells using immunohistochemistry and determined the correlations of EGFR expression with clinical characteristics, EGFR mutations, and survival time. Moreover, we graded complete membranous staining with strong intensity as high membranous EGFR (mEGFR) expression, and nuclear EGFR staining with strong intensity as high nuclear (nEGFR) expression.ResultsThe prevalence of high mEGFR and nEGFR expression in lung adenocarcinoma was 42.86 and 39.13%, respectively. After multivariate analyses, high mEGFR expression was associated with a significantly reduced mortality risk in older patients, those with a history of smoking, and those without brain metastasis (hazard ratio[95% confidential interval], HR[95% CI] = 0.55[0.32~ 0.92]; 0.51[0.26~ 0.98] and 0.56[0.33~ 0.94], in overall survival, respectively). An association between high nEGFR expression and early recurrence was observed in patients with metastasis (HR[95% CI] =1.68[1.05~ 2.68], in progression-free survival). Notably, patients with low mEGFR and low nEGFR expression had the lowest survival rate in cases without brain metastasis (p = 0.018) and with a history of smoking (p = 0.062) and total EGFR (any high mEGFR or nEGFR) expression indicated a more favorable response to platinum-based chemotherapy regardless of EGFR mutations (HR[95% CI] =0.33[0.12-0.92]; adjusted HR[95% CI] = 0.36[0.13~ 1.02] with the use of tyrosine kinase inhibitor).ConclusionsEGFR proteins at different cellular locations in lung adenocarcinoma might influence the biology of cancer cells and are an independent indicator of more favorable prognosis and treatment response.

Project description:Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.

Dataset Information

Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.

Publications

Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.

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