Cone-beam Breast CT Features Associated With Lymphovascular Invasion in Patients With Breast Cancer.

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Tác giả: Keyi Bian, Yue Ma, Yafei Wang, Zhaoxiang Ye, Yueqiang Zhu

Ngôn ngữ: eng

Ký hiệu phân loại: 618.19 *Diseases of breast

Thông tin xuất bản: United States : Academic radiology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 99769

 RATIONALE AND OBJECTIVES: To explore the association between contrast-enhanced cone-beam breast CT (CE-CBBCT) imaging features and lymphovascular invasion (LVI) status. MATERIALS AND METHODS: Female patients with breast cancer by postoperative histopathology who underwent preoperative CE-CBBCT from August 2020 to December 2023 were retrospectively enrolled. Two radiologists reviewed the CBBCT image features independently. Clinicopathologic and CBBCT characteristics between LVI-positive and LVI-negative were compared by χ2 or Fisher's exact tests and Student's t or Mann-Whitney U test, as appropriate. Multivariate logistic regression analysis was performed to identify independent predictive factors of LVI. The receiver operating characteristic curve was used to evaluate predictive performance. RESULTS: A total of 401 women were enrolled. LVI status of breast cancer was significantly associated with histologic type, Ki-67 index, adjacent vessel sign (AVS), increased ipsilateral whole-breast vascularity (IIV) number, and IIV degree (all p<
 0.05). In mass, calcification, AVS, IIV number, and IIV degree were significantly associated with LVI (all p<
 0.05). In non-mass enhancement (NME), AVS, IIV number, and IIV degree were associated with LVI (all p<
 0.05). Multivariate logistic regression showed AVS (OR=4.367, p<
 0.001) and IIV degree (OR of moderate and prominent IIV=4.732, 3.641, both p<
 0.005) as independent risk factors for LVI. Specifically, in mass, AVS (OR=4.397, p<
 0.001) and moderate-to-prominent IIV (OR=4.815, 3.563, both p<
 0.01) were independent predictors. For NME, moderate-to-prominent IIV (OR=13.695, 4.054, both p=0.001) was also an independent factor. The combined LVI prediction model which included AVS and IIV degree showed excellent performance (AUC=0.804). CONCLUSION: CBBCT imaging features can help identify LVI status in breast cancer patients, which will guide the accurate planning of treatment management.
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