Dataset Information

A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

ABSTRACT:

Background

This study aimed at predicting the survival status on non-small cell lung cancer patients with the phenotypic radiomics features obtained from the CT images.

Methods

A total of 186 patients' CT images were used for feature extraction via Pyradiomics. The minority group was balanced via SMOTE method. The final dataset was randomized into training set (n?=?223) and validation set (n?=?75) with the ratio of 3:1. Multiple random forest models were trained applying hyperparameters grid search with 10-fold cross-validation using precision or recall as evaluation standard. Then a decision threshold was searched on the selected model. The final model was evaluated through ROC curve and prediction accuracy.

Results

From those segmented images of 186 patients, 1218 features were obtained via feature extraction. The preferred model was selected with recall as evaluation standard and the optimal decision threshold was set 0.56. The model had a prediction accuracy of 89.33% and the AUC score was 0.9296.

Conclusion

A hyperparameters tuning random forest classifier had greater performance in predicting the survival status of non-small cell lung cancer patients, which could be taken for an automated classifier promising to stratify patients.

SUBMITTER: He B

PROVIDER: S-EPMC6180390 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

He Bo B Zhao Wei W Pi Jiang-Yuan JY Han Dan D Jiang Yuan-Ming YM Zhang Zhen-Guang ZG Zhao Wei W

Respiratory research 20181010 1

<h4>Background</h4>This study aimed at predicting the survival status on non-small cell lung cancer patients with the phenotypic radiomics features obtained from the CT images.<h4>Methods</h4>A total of 186 patients' CT images were used for feature extraction via Pyradiomics. The minority group was balanced via SMOTE method. The final dataset was randomized into training set (n = 223) and validation set (n = 75) with the ratio of 3:1. Multiple random forest models were trained applying hyperpara ...[more]

PMID: 30305102

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Project description:This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan-Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27-3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635-0.758), 0.705 (95% CI, 0.649-0.762), 0.657 (95% CI, 0.589-0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499-0.64), 0.552 (95% CI, 0.489-0.616), 0.621 (95% CI, 0.544-0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan-Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

Dataset Information

A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

Background

Methods

Results

Conclusion

Publications

A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

Similar Datasets

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