  Purpose: We aimed to develop a data-driven multiomics approach integrating
radiomics, dosiomics, and delta features to predict treatment response at an
earlier stage (intra-treatment) for brain metastases (BMs) patients treated
with PULSAR. Methods: We conducted a retrospective study of 39 patients with 69
BMs treated with PULSAR. Radiomics, dosiomics, and delta features were
extracted from pretreatment and intra-treatment MRI scans and dose
distributions. Six individual models and an ensemble feature selection (EFS)
model were constructed using SVM and evaluated via stratified 5-fold
cross-validation. The classification task distinguished lesions with >20%
volume reduction at follow-up. We assessed performance metrics including
sensitivity, specificity, accuracy, precision, F1 score, and AUC. Various
feature extraction and ensemble selection scenarios were explored to enhance
model robustness and reduce overfitting. Results: The EFS model, integrating
features from pre-treatment radiomics and dosiomics, intra-treatment radiomics,
and delta-radiomics, outperformed six individual models. It achieved an AUC of
0.979, accuracy of 0.917, and F1 score of 0.821. Among the top 9 features, six
were derived from post-wavelet transformation, and three from original images.
The discrete wavelet transform decomposes volumetric images into
multi-resolution components, offering a more comprehensive characterization of
underlying structures. Conclusion: Our study demonstrated the feasibility of
employing a data-driven multiomics approach to predict tumor volume changes in
BMs patients with PULSAR. The EFS model demonstrates enhanced performance
compared with six individual models, emphasizing the importance of integrating
both pretreatment and intra-treatment data. This application holds promise for
optimizing BMs management, potentially mitigating risks associated with under-
or over-treatment.
