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Purpose <p>18&#xa0;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 <i>de novo</i> metastatic hormone receptor-positive (HR+)/HER2-negative breast cancer.</p> Methods <p>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&#xa0;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).</p> Results <p>The cohort consisted of 25 Responders and 8 Non-Responders. Non-Responders exhibited distinct longitudinal increases in <i>Delta_Pct_shape_Elongation</i> and <i>Delta_Pct_firstorder_90Percentile</i> 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.</p> Conclusions <p>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.</p>

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Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月25日

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  • Emir Gokhan Kahraman,
  • Olcun Umit Unal,
  • Halil Taskaynatan,
  • Ozlem Ozdemir,
  • Emine Budak,
  • Mustafa Alper Selver

摘要

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.