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Improved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data

Patients with metastatic breast cancer (mBC) undergo continuous medical imaging during treatment, making accurate lesion detection and monitoring over time critical for clinical decisions. Predicting drug response from post-treatment data is essential for personalized care and pharmacological research. In collaboration with the U.S. Food and Drug Administration and Novartis Pharmaceuticals, we analyzed serial chest CT scans from two large-scale Phase III trials, MONALEESA 3 and MONALEESA 7. This paper has two objectives (a) Data Structuring developing a Registration Aided Automated Correspondence (RAMAC) algorithm for precise lesion tracking in longitudinal CT data, and (b) Survival Analysis creating imaging features and models from RAMAC structured data to predict patient outcomes. The RAMAC algorithm uses a two phase pipeline: three dimensional rigid registration aligns CT images, and a distance metric-based Hungarian algorithm tracks lesion correspondence. Using structured data, we developed interpretable models to assess progression-free survival (PFS) in mBC patients by combining baseline radiomics, post-treatment changes (Weeks 8, 16, 24), and demographic features. Radiomics effects were studied across time points separately and through a non-correlated additive framework. Radiomics features were reduced using (a) a regularized (L1-penalized) additive Cox proportional hazards model, and (b) variable selection via best subset selection. Performance, measured using the concordance index (C-index), improved with additional time points. Joint modeling, considering correlations among radiomics effects over time, provided insights into relationships between longitudinal radiomics and survival outcomes.

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