Evidence map›Paper›PMID 41872564›Full record

ArticleBreast cancer research and treatment2026

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

Yihui Zhu, Roham Hadidchi, Hien Quang Nguyen, Surya Hariharan, Jeremy Weiss, Wei Hou, Chris Chung, Ha Manh Luu, Siddarth Ragupathi, Takouhie Maldjian and 1 more

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Article in Breast cancer research and treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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11 authors.

Yihui ZhuDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Roham HadidchiDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Hien Quang NguyenDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Surya HariharanDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Jeremy WeissDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Wei HouDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Chris ChungDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Ha Manh LuuDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Siddarth RagupathiDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA.
Takouhie MaldjianDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA. tamaldjian@montefiore.org.
Tim Q DuongDepartment of Radiology, Albert Einstein College of Medicine and Montefiore Health System, Bronx, NY, USA. tim.duong@einsteinmed.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo 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.

methodsThe 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

resultsPeak-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.

conclusionAutomated 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.

Indexed as

Breast NeoplasmsAdultAgedBiomarkers, TumorContrast MediaDisease-Free SurvivalDynamic Contrast Enhanced Magnetic Resonance ImagingFemaleHumansMiddle AgedNeoplasm Recurrence, LocalPathologic Complete ResponsePrognosisTreatment OutcomeBiomarkers, TumorContrast MediaHuman epidermal growth factor receptor 2 (HER2) statusMolecular subtypesNeoadjuvant chemotherapyProgression-free survivalTriple negative

Identifiers

PMID41872564
PMCPMC13009020

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