Discussion
PIRADS, Gilles, tumour, glszm_GrayLevelNonUniformity
TUMOUR
We have demonstrated that 11 of 20 targets of interest can be predicted with an AUROC significantly greater than 0.5 (Contrary to previous studies [Radiomic predictions of the clonal status of the somatic alterations were also explored, but none of these targets could be predicted in this study. It is likely that radiological changes associated with different clones of the same genetic locus will be more subtle than changes associated with different genetic loci, and thus it is reasonable to assume that detecting mutational clonality from radiological imaging will be particularly challenging.Prediction of the punctuated EvoST did not reach significance in these data which may be due to the one vs all classification that was used for the EvoSTs due to the small sample size in this study. Multinomial classification models may be required to handle the EvoST using radiomics, and the complete cohort of 320 patients will provide a more suitable data set for such development.Two pipelines have been compared: the Conventional pipeline reflects standard practice in radiomics studies, and the Proposed pipeline includes two feature selection innovations tailored to radiomics studies that are designed to improve model interpretability. The AUROC performance is very similar for the Conventional and Proposed pipelines, suggesting that there is a high degree of redundancy between the radiomics features such that multiple combinations of features result in models with similar performance. This is a well-known effect with high-dimensional feature sets when the amount of training data is limited.Generic feature selection techniques, whether they are based on filter, wrapper, or embedded methods, only consider the predictive potential of each feature, and not their interpretability. In the context of feature sets that exhibit redundancy, there is therefore no guarantee that the use of generic feature selection techniques will result in a model that has the most straightforward interpretation. The results for models predicting RVI, IVC, ITH index, wGII Max and Loss9p21.3 are a clear demonstration of this – the AUROC estimates for the Conventional and Proposed pipelines are equivalent, but the Proposed pipeline selected fewer features from only the shape feature group for these targets. The LR coefficient values from the Conventional pipeline are given in Table SThe hierarchical CFR is a key innovation in the Proposed pipeline and arises from the idea that a judgement on the a priori interpretability of features from different feature groups can be used to intentionally bias the correlation-based feature selection step. Four feature groups were used in this study, but additional feature groups may be necessary with different radiomics feature sets. For example, filtered image features (wavelets, Laplacian of Gaussian, etc.) involve additional processing steps and may be placed after texture features in the hierarchy, whereas previously validated semantic imaging features (e.g., PIRADS features) may be placed before all the radiomics feature groups. In general, the availability of a meaningful interpretation and/or biological or clinical validation of the features within each group should be used to determine the group hierarchy.It is interesting to note that the use of hierarchical CFR is critical for this data set because the MeshVolume and glszm_GrayLevelNonUniformity (glszm_GLNU) features from the whole tumour ROI have a Spearman correlation of 0.979. In this case, application of the standard CFR algorithm to these data results in MeshVolume being rejected in favour of glszm_GLNU. In those targets where MeshVolume is an important predictor, use of the standard CFR algorithm would mean the feature group selection step would be forced to retain the texture feature group, which would confound interpretation. This has clearly impacted the Conventional pipeline models for ITH index, wGII Max and Loss9p21.3 (see Table STumour volume is a sufficiently well-known predictor that specific checks of the predictive power of this feature are included in some published radiomics studies. This is reflected in the hierarchy used here – MeshVolume is given special status by placing it in the first feature group, so it is always retained if it is correlated to any other feature. Texture features are placed at the other end of the hierarchy on the basis that they are in general more difficult to interpret – in many papers where texture features are discovered to be predictive, the interpretation given is that they are associated with “tumour heterogeneity”. Whilst this may be true, to fully elucidate the correlations between texture features and biological processes in the spirit of Tomaszewski and Gilles [Combining the hierarchical CFR with an explicit group selection step is designed to increase the ability of the Proposed pipeline to reject features that are not informative. Treating the group selection as a tuning parameter which is optimised using an inner CV has two potential benefits. Firstly, the frequency with which each feature group is selected across the CV splits can be measured, which gives information on model robustness. Secondly, the use of CV for parameter tuning allows the complexity of the model (i.e., how many feature groups are selected) to adapt to the data, both in terms of any informative patterns in the data, but also the data set size. Feature groups that lead to consistent performance improvements across the CV splits are likely to be selected, and this stability will tend to be greater in larger data sets since the different data splits will be statistically more similar. Conversely, if a feature group contains some weakly predictive features, then for a small data set the feature group is likely to be selected in fewer CV splits, and this can be detected in the feature group selection frequency.Sub-segmentations were used in this study for two reasons. Firstly, spatial analysis of the surgical specimens in the TRACERx study demonstrated different genomic profiles in tissue samples taken from the core and rim of the tumour [An advantage of the proposed group selection method is that it can be applied in conjunction with any predictive model (e.g. support vector machines, random forest etc.), which is in contrast to the priority LASSO [An important limitation of this study is the small sample size, which was also a key driver for the development of the proposed feature selection methods. On completion, the TRACERx study aims to recruit 320 patients, and so further work applying the proposed methods in this larger cohort will be of interest. A consequence of the small sample size is that the radiomics feature sets analysed here did not include the extended features available in pyradiomics that are derived using image filtering (e.g. wavelet filtering) as their inclusion would risk over-fitting. Future work investigating the impact of the feature group selection methods with these extended feature sets in a suitable data set is of interest.