Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer
BMC Medical Imaging, cilt.26, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s12880-026-02463-4
- Dergi Adı: BMC Medical Imaging
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, EMBASE, MEDLINE, Directory of Open Access Journals, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Metastatic breast cancer, Delta radiomics, CDK4/6 inhibitors, Machine learning, Bone metastasis, Non-contrast CT
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Purpose: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predictive value of delta-radiomics derived solely from the low-dose, non-contrast CT component acquired during routine PET/CT imaging—without requiring an additional dedicated CT examination or extra contrast administration—for monitoring response to CDK4/6 inhibitors in de novo metastatic hormone receptor-positive (HR+)/HER2-negative breast cancer. Methods: This retrospective study included 33 patients with bone-predominant metastatic breast cancer. Delta radiomic features were extracted from the non-contrast CT component of paired baseline and follow-up 18 F-FDG PET/CT scans. Patients were stratified into Responders (Complete or Partial Response) and Non-Responders (Stable or Progressive Disease) based on standard PERCIST criteria. We developed an integrated machine learning model using logistic regression with elastic net regularization, validated via leave-one-out cross-validation (LOOCV). Results: The cohort consisted of 25 Responders and 8 Non-Responders. Non-Responders exhibited distinct longitudinal increases in Delta_Pct_shape_Elongation and Delta_Pct_firstorder_90Percentile compared to Responders. The integrated model, combining these features with clinical variables, achieved an Area Under the Curve (AUC) of 0.930, significantly outperforming the baseline clinical-only model (AUC = 0.775). While the default threshold prioritized sensitivity (96.0%) with limited specificity (25.0%), post-hoc threshold optimization maximizing the Youden index demonstrated a highly balanced performance, achieving 88.0% sensitivity and 87.5% specificity. Conclusions: Delta radiomics analysis of the routinely acquired non-contrast CT component of PET/CT provides substantial incremental prognostic value over standard clinical variables. This approach demonstrates the potential of utilizing existing low-dose CT data as a cost-effective, supportive biomarker for the early prediction of therapeutic resistance.