Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

TL;DR

This study develops a multiomics machine learning model combining radiomics, dosiomics, and delta features from MRI scans to predict brain metastases treatment response early during PULSAR therapy, showing high accuracy and potential for personalized treatment adaptation.

Contribution

The paper introduces an ensemble feature selection model integrating multiomics features from pretreatment and intra-treatment MRI data for early prediction of treatment response in brain metastases, demonstrating superior performance.

Findings

The ensemble model achieved an AUC of 0.979 and accuracy of 91.7%.

Features from wavelet-transformed images contributed significantly to prediction.

Multiomics integration improved early response prediction over individual models.

Abstract

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.