A radiomics-driven approach for predicting response to induction therapy in multiple myeloma: Leveraging CT-based...

This work established and validated a radiomics-driven framework to quantify therapeutic response in multiple myeloma (MM). By leveraging CT-based Delta features, we aimed to delineate sub-visual temporal changes and assess their incremental predictive value beyond conventional clinical biomarkers.

We retrospectively evaluated 131 newly diagnosed MM patients (April 2019-November 2024), partitioned into training and validation cohorts (7:3 ratio). Baseline and post-induction CT images (mean interval: 132 days), reconstructed via high-resolution sharp kernels, served as the basis for analysis. Following a rigorous inter-observer stability filter (ICC > 0.90), Delta-radiomic features were extracted from the ribs, manubrium, and thoracic spine. A stability-first selection pipeline, utilizing mRMR and LASSO regression, identified the most robust signatures for model construction.

ISS staging (I/II/III: 19.9%/29.0%/51.1%) was balanced across cohorts. The multi-regional radiomics signature (RadDeltaAll) demonstrated superior stability compared to single-site models. In the training set, the integrated framework yielded an AUC of 0.855. In the independent validation cohort, while single-site performance varied, the comprehensive RadDeltaAll model maintained high diagnostic stability . AUC = 0.842. Notably, the clinical biomarker 24hU-Pro was retained as a "biological anchor"; although it did not statistically enhance predictive accuracy, it provided essential context for systemic renal burden without compromising model integrity.

CT-based Delta radiomics constitutes a robust, non-invasive biomarker for MM response assessment. While 24hU-Pro serves as an indispensable biological reference, the multi-regional radiomic signature remains the primary engine of predictive power. This underscores the clinical necessity of quantitative, skeleton-aware analysis in steering individualized induction therapy. MM: Multiple myeloma; CT: Computed tomography; ROI: Region of interest; AUC: Area under the curve; ESM: Electronic supplementary material; WBLDCT: Whole-body low-dose computed tomography; IMWG: International Myeloma Working Group; LASSO: Least Absolute Shrinkage and Selection Operator; mRMR: minimum Redundancy Maximum Relevance; SVM: Support Vector Machine; RF: Random Forest; XGBoost: Extreme Gradient Boosting; GLCM: Gray-Level Co-occurrence Matrix; GLRLM: Gray-Level Run-Length Matrix; GLSZM: Gray-Level Size Zone Matrix; NGTDM: Neighboring Gray-Tone Difference Matrix; DSC: Dice Similarity Coefficient; DCA: Decision Curve Analysis.