| Autor: |
Jian-man Zhu, Lei Sun, Linjing Wang, Tong-Chong Zhou, Yawei Yuan, Xin Zhen, Zhi-Wei Liao |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
BMC Research Notes, Vol 15, Iss 1, Pp 1-6 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1756-0500 |
| DOI: |
10.1186/s13104-022-06019-x |
| Popis: |
Abstract Objective This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified. Results There were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC = 0.797, ACC = 0.722, sensitivity = 0.758, specificity = 0.693). The median R-score was 0.518 (IQR, 0.023–0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p = 0.000). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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