Extratumoral signs on mammography as a novel prognostic indicator for breast cancer: evidence from malignant nonspiculate and noncalcified masses
- Ye Xu 1, Fei Wang 1, Dongbo Wu 1, Xinghua Liang 2, Jianghong Sun 1, Xiushi Zhang 1
- 1Department of Radiology, Harbin Medical University Cancer Hospital Harbin, Heilongjiang, China.
- 2Department of Radiology, Heilongjiang University Hospital Harbin, Heilongjiang, China.
- 0Department of Radiology, Harbin Medical University Cancer Hospital Harbin, Heilongjiang, China.
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View abstract on PubMed
Summary
This summary is machine-generated.Mammographic signs of extratumoral structural abnormalities, specifically the contraction and parallel signs, predict breast cancer progression in nonspiculate and noncalcified masses (NSNCMs). These findings aid in personalized treatment strategies.
Area Of Science
- Radiology
- Oncology
- Breast Cancer Imaging
Background
- Malignant nonspiculate and noncalcified masses (NSNCMs) on mammography present diagnostic challenges.
- Prognostic indicators for NSNCMs are crucial for guiding patient treatment strategies.
Purpose Of The Study
- To evaluate the prognostic significance of extratumoral structural abnormalities in patients with malignant NSNCMs detected on mammography.
- To determine if specific mammographic signs can predict disease progression.
Main Methods
- Retrospective analysis of 354 breast cancer patients with mammographically detected NSNCMs.
- Cox regression and Kaplan-Meier analyses to assess the impact of mammographic findings on progression-free survival (PFS).
Main Results
- Extratumoral contraction sign (HR=2.56) and parallel sign (HR=2.71) were independent predictors of NSNCM progression.
- Lymph node metastasis (HR=2.37) and histological grade (HR=2.03) also predicted disease progression.
- Tumor signs lacked significant predictive value.
Conclusions
- Specific extratumoral structural abnormalities on mammography are independent prognostic indicators for breast cancer patients with malignant NSNCMs.
- These mammographic findings can stratify risk and inform personalized treatment strategies.
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