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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | He, Bo Zhao, Wei Pi, Jiang-Yuan Han, Dan Jiang, Yuan-Ming Zhang, Zhen-Guang |
| 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. |
| Related Links | https://respiratory-research.biomedcentral.com/counter/pdf/10.1186/s12931-018-0887-8.pdf |
| Ending Page | 8 |
| Page Count | 8 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| DOI | 10.1186/s12931-018-0887-8 |
| Journal | Respiratory Research |
| Issue Number | 1 |
| Volume Number | 19 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2018-10-10 |
| Access Restriction | Open |
| Subject Keyword | Pneumology Respiratory System Non-small cell lung cancer Radiomics CT Random forest Survival status Pneumology/Respiratory System |
| Content Type | Text |
| Resource Type | Article |
| Subject | Pulmonary and Respiratory Medicine |
| Journal Impact Factor | 4.7/2023 |
| 5-Year Journal Impact Factor | 5.3/2023 |
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