Radiomics has recently shown potential in achieving personalized medicine for different disease such as cancer. However, the large number of radiomic features, small number of observations, and unbalanced datasets are major challenges when building radiomics-based prognosis models. In this study, different unsupervised feature selection methods, classification techniques, and subsampling methods were investigated for different outcomes to study the optimal configuration and workflow for radiomic-based prognosis analytics.
Generally, a radiomics dataset contains a large number of features with a significant amount of noise and a highly correlated feature structure. These factors together are detrimental to the predictive model building process and negatively affect the prediction performance of the models. With similar data structure, genomics studies have overcome the feature redundancy problem through different feature selection approaches32,33. In radiomics domain, it is necessary to apply feature reduction when building robust prognosis models in order to achieve higher accuracy, better data visualizations and understanding, reducing measurement and storage requirements, and minimizing the training and inference time.
Among feature selection methods, the unsupervised feature selection, which is based on dimensionality reduction algorithms transforming the high-dimensional feature space into a meaningful representation34 was selected for our study. Compared to filtered (supervised) feature selection methods, the dimensionality reduction algorithms are less prone to over-fitting16. In our study, PCA showed great value in reducing the number of features, and yielded the highest overall (average) predictive performance. It is important to realize that some radiomic features may not have high predictive value individually but become important when interaction effects among the features are taken into consideration. Compared to filtered feature reduction techniques which may eliminate important features, unsupervised feature reduction maintains the interaction among features, benefiting the predictive model training process.
Consistent with previous results, RF has the highest predictive value among different classifiers. Using RF to build a predictive model with high specificity and sensitivity, it is also important to tackle the unbalanced data problem. Down-sampling, up-sampling, SMOTE and ROSE sub-sampling techniques were applied and showed improvement in terms of AUC, and especially sensitivity. For unbalanced dataset, machine learning algorithms tend to sacrifice the minority group to achieve a higher accuracy. Thus, in clinical radiomics studies, the subsampling methods can significantly improve the sensitivity, leading to better predictive performance. Although all these subsampling methods improved AUC comparably, SMOTE was able to enhance AUC in a balanced way; while maintaining high specificity, it was also able to increase sensitivity significantly. In addition, SMOTE has been shown to be robust to the variation of unbalanced ratio with a variety of classifiers28. Thus the combination of PCA feature selection, RF classifier, and SMOTE subsampling (PCA + RF + SMOTE) constitutes an optimal radiomics pipeline for prognosis of clinical outcomes. The analysis of variance also showed that while individual classifiers and unsupervised feature selection methods have significant impact on the prediction performance, the interaction effect of these two on the results is not significant. In other words, when choosing non-filtering feature selection methods and classifiers, there is no need to look at specific combination of selection methods and classifiers for high accuracy prediction model.
Although it has been previously reported in the literature that radiomics-based signatures may have a complimentary role in predicting survival and other clinical outcomes in early or advanced stage lung cancer patients6,35, none of these studies has specifically compared different data reduction methods and classification techniques, as done in this work, or addressed how the radiomics-based prognostic model can be optimized. Moreover, these studies focus on survival analysis using univariate or multivariate analysis where one or a few (e.g., 3) imaging features are used. In contrast, in this study, we take the predictive modeling approach where a large radiomics feature set is analyzed for prognosis of the disease. Nevertheless, the most similar study to this work is by Ganeshan et al.6 where the prognostic value of CT imaging features were studied for lung cancer. While we analyzed 3 outcomes (recurrence, death, and recurrence-free survival), Ganeshan et al. only analyzed one outcome namely death. Moreover, we used 112 cases compared to 56 in Ganeshan’s study6.
There are other related studies on lung cancer, all of which have used PET images35,36,37. Apart from the fact that in our study, we used CT images, all these studies only looked at one or found significant results for one clinical outcome (e.g., distant metastasis, death or disease-free survival respectively) while our study included 3 different clinical outcomes. Therefore, while these studies found prognostic value for only one outcome, in this paper, we show the prognostic value of an optimal radiomics-based pipeline for all 3 clinical outcomes (i.e., recurrence, death, and recurrence-free survival).
Survival analysis of clinical outcomes tend to focus on individual risk factors which influence the outcome the most. This has also been widely investigated in radiomics analytics to find significant features which influence a given clinical outcome the most. However, those significant features can only explain a small amount of variation individually, limiting their applications in clinical practice. Furthermore, since the number of features is usually high, survival analysis also faces the multiple testing problems. Thus, without proper control, survival analyses are expected to give a high amount of false positive significant features reducing the reliability of analysis. Thus, a feature-selection based analytics may mitigate this obstacle and help develop a more robust prognosis framework.
It has been previously shown that for NSCLC patients, radiomics-based predictive models can significantly improve the prognosis performance of traditional methods, which usually have rather low accuracy36. On the contrary, a well-trained radiomics-based prognosis model could make the prediction more accurate and unbiased. As more the potential of radiomics is realized, the number of radiomics datasets will increase significantly. How to fully take advantage of such data is a challenge in this field. Through this study, we aimed to determine the optimal configuration of radiomics-based predictive modeling analytics in terms of feature selection, subsampling, and classifiers to improve the prediction performance for further clinical applications.
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Friend: how far are you with your essay? me: 800 words, you? friend: oh, i'm at 1300. me: quality over quantity.
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