Abstract
Tumor phenotypes can be characterized by radiomics features extracted from images. However, the prediction accuracy is challenged by difficulties such as small sample size and data imbalance. The purpose of the study was to evaluate the performance of machine learning strategies for the prediction of cancer prognosis. A total of 422 patients diagnosed with non-small cell lung carcinoma (NSCLC) were selected from The Cancer Imaging Archive (TCIA). The gross tumor volume (GTV) of each case was delineated from the respective CT images for radiomic features extraction. The samples were divided into 4 groups with survival endpoints of 1 year, 3 years, 5 years, and 7 years. The radiomic image features were analyzed with 6 different machine learning methods: decision tree (DT), boosted tree (BT), random forests (RF), support vector machine (SVM), generalized linear model (GLM), and deep learning artificial neural networks (DL-ANNs) with 70:30 cross-validation. The overall average prediction performance of the BT, RF, DT, SVM, GLM and DL-ANNs was AUC with 0.912, 0.938, 0.793, 0.746, 0.789 and 0.705 respectively. The RF and BT gave the best and second performance in the prediction. The DL-ANN did not show obvious advantage in predicting prognostic outcomes. Deep learning artificial neural networks did not show a significant improvement than traditional machine learning methods such as random forest and boosted trees. On the whole, the accurate outcome prediction using radiomics serves as a supportive reference for formulating treatment strategy for cancer patients.
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Data Availability
The data is available upon request to: Professor F.H. Tang by email: fhtang@twc.edu.hk
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Funding
We acknowledged the University Grants Council Faculty Development Scheme grant UGC/FDS17/M10/19 for support of model development of this project and Tung Wah College for the support of article publication charge. On behalf of all authors, the corresponding author states that there is no conflict of interest.
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Conceptualization, F.-H.T.; methodology, C.X., C.Y.W., T.H.C., C.K.L; data acquisition, C.K.L.; writing—original draft preparation, C.Y.W. and T.H.C; writing—review and editing, F.-H.T., C.X. and M.Y.L; All authors have read and agreed to the published version of the manuscript.
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Appendix
Appendix
Summary of machine learning methods and ROC curves for all the methods.
ROC of DT, BT, RF, SVM, and GLM for 1-year balanced data and all data
ROC of DT, BT, RF, SVM, and GLM for 3-year balanced data and all data
ROC of DT, BT, RF, SVM, and GLM for 5-year balanced data and all data
ROC of DT, BT, RF, SVM, and GLM for 7-year balanced data and all data
ROC of CNN for 1-year, 3-year, 5-year and 7-year balanced data and all data
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Tang, Fh., Xue, C., Law, M.Y. et al. Prognostic Prediction of Cancer Based on Radiomics Features of Diagnostic Imaging: The Performance of Machine Learning Strategies. J Digit Imaging 36, 1081–1090 (2023). https://doi.org/10.1007/s10278-022-00770-0
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DOI: https://doi.org/10.1007/s10278-022-00770-0