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Purpose <p>To determine whether automated quantification of background parenchymal enhancement (BPE) from dynamic contrast-enhanced MRI (DCE-MRI) can serve as an imaging biomarker for clinical outcomes including overall survival (OS), recurrence-free survival (RFS), and pathological complete response (pCR) in breast cancer.</p> Methods <p>The multi-institutional data consisted of 922 biopsy-confirmed invasive breast cancer patients from the Duke-Breast-Cancer-MRI dataset and 152 patients with whole-breast pre- (T<sub>0</sub>) and/or post (T<sub>3</sub>) DCE-MRI from the I-SPY2 dataset for validation. Automated fibroglandular tissue (FGT) segmentation and BPE quantification were performed on DCE-MRI. The optimal intensity enhancement threshold by volume-based method was established against four radiologist-defined BPE categories. The area under the curve (AUC) was obtained for classification of BPE categories. Cox proportional hazards models were used to predict OS and RFS. Logistic regression was used to predict pCR.</p> Results <p>Peak-contrast BPE showed strong correlation with radiologist-defined BPE, achieving the best performance at a 55% signal enhancement threshold (AUC 0.70–0.86). The calculated BPE decreased after neoadjuvant chemotherapy. A reduction in calculated BPE grade after neoadjuvant chemotherapy was predictive of pCR for the high baseline BPE group (adjusted odds ratio = 5.88 [1.03, 33.33]) and for the low baseline BPE group (adjusted odds ratio = 6.54 [1.26, 33.94]). Baseline BPE was independently associated with improved OS (adjusted hazard ratio 0.58 [0.34, 0.99]) but not associated with RFS.</p> Conclusion <p>Automated quantification of BPE from DCE-MRI provides an objective and reproducible imaging biomarker associated with treatment response and overall survival in breast cancer. These results support its potential utility for individualized risk stratification and therapeutic decision-making.</p>

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Automated calculation of background parenchymal enhancement as a biomarker of treatment responses and recurrence-free survival in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Yihui Zhu,
  • Roham Hadidchi,
  • Hien Quang Nguyen,
  • Surya Hariharan,
  • Jeremy Weiss,
  • Wei Hou,
  • Chris Chung,
  • Ha Manh Luu,
  • Siddarth Ragupathi,
  • Takouhie Maldjian,
  • Tim Q. Duong

摘要

Purpose

To determine whether automated quantification of background parenchymal enhancement (BPE) from dynamic contrast-enhanced MRI (DCE-MRI) can serve as an imaging biomarker for clinical outcomes including overall survival (OS), recurrence-free survival (RFS), and pathological complete response (pCR) in breast cancer.

Methods

The multi-institutional data consisted of 922 biopsy-confirmed invasive breast cancer patients from the Duke-Breast-Cancer-MRI dataset and 152 patients with whole-breast pre- (T0) and/or post (T3) DCE-MRI from the I-SPY2 dataset for validation. Automated fibroglandular tissue (FGT) segmentation and BPE quantification were performed on DCE-MRI. The optimal intensity enhancement threshold by volume-based method was established against four radiologist-defined BPE categories. The area under the curve (AUC) was obtained for classification of BPE categories. Cox proportional hazards models were used to predict OS and RFS. Logistic regression was used to predict pCR.

Results

Peak-contrast BPE showed strong correlation with radiologist-defined BPE, achieving the best performance at a 55% signal enhancement threshold (AUC 0.70–0.86). The calculated BPE decreased after neoadjuvant chemotherapy. A reduction in calculated BPE grade after neoadjuvant chemotherapy was predictive of pCR for the high baseline BPE group (adjusted odds ratio = 5.88 [1.03, 33.33]) and for the low baseline BPE group (adjusted odds ratio = 6.54 [1.26, 33.94]). Baseline BPE was independently associated with improved OS (adjusted hazard ratio 0.58 [0.34, 0.99]) but not associated with RFS.

Conclusion

Automated quantification of BPE from DCE-MRI provides an objective and reproducible imaging biomarker associated with treatment response and overall survival in breast cancer. These results support its potential utility for individualized risk stratification and therapeutic decision-making.