ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026
Use of contrast-enhanced mammography for preoperative prediction of lymphovascular invasion status in invasive breast cancer.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 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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Abstract
backgroundTo investigate the use of contrast-enhanced mammography (CEM) for preoperative prediction of lymphovascular invasion (LVI) status in invasive breast cancer.
methodsA total of 243 female patients diagnosed with invasive breast cancer (median age: 49 years; range: 27-77 years) who received preoperative CEM examination in our hospital between September 2018 and February 2024 were retrospectively collected and analyzed. The study population were chronologically divided into training and test datasets in an approximate ratio of 7:3. LVI status was determined using postoperative histopathologic examination. CEM features were analyzed on the low energy and the recombined images. To identify independent predictors for LVI status, univariable and multivariable logistic regression analyses were performed on CEM and clinicopathologic features. Logistic regression and six machine learning methods were used to construct prediction models in the training dataset, and their performance were evaluated with ROC curve in the test dataset.
resultsIn training and test datasets, the rates of LVI-positive were 39% (67 of 172) and 34% (24 of 71), respectively. High Ki67 index, BI-RADS category 5, breast composition category c/d, axillary adenopathy, mild to marked background parenchymal enhancement level, and lesion with complete enhancement or enhancement extending on CEM images were significantly correlated with LVI-positive (all P < 0.05) and were incorporated to construct prediction models. The AUCs of seven prediction models were in the range of 0.713-0.850 in the test datasets, where the logistic regression model yielded an AUC of 0.835 (95%CI: 0.717-0.924), showing similar or higher AUC than the six machine learning models.
conclusionsCEM could be useful for preoperative noninvasive prediction of LVI status in invasive breast cancer. The prediction model integrating contrast-enhanced mammography features and Ki67 index may serve as a complementary tool to assist clinicians in preoperative prediction of lymphovascular invasion status in patients with invasive breast cancer.
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