Evidence map›Paper›PMID 41972079›Full record

ArticleQuantitative imaging in medicine and surgery2026

Predicting postoperative malignancy upgrading in B1-B3 breast lesions: combined utility of conventional ultrasound and contrast-enhanced ultrasound features.

Ying-Lun Zhang, Jing Yao, Wei-Kui Jin, Jin-Qiu Tao, Xia Li, Hai-Wen Du, Jun-Lan Qiu, Hao Han

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Article in Quantitative imaging in medicine and surgery, 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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8 authors.

Ying-Lun ZhangDepartment of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jing YaoDepartment of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Wei-Kui JinDepartment of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jin-Qiu TaoDivision of Breast Surgery, Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Xia LiDepartment of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Hai-Wen DuDepartment of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jun-Lan Qiu *Department of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Hao Han *Department of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While conventional ultrasound (US)-guided core needle biopsy (CNB) is routinely used for preoperative diagnosis of breast lesions, it is susceptible to false negatives and underestimation, especially in B1-B3 categories, making accurate diagnosis a significant clinical challenge. This study aimed to explore the conventional US and contrast-enhanced ultrasound (CEUS) features that predict postoperative malignancy upgrading of category B1-B3 breast lesions diagnosed by CEUS-guided CNB. Methods: A total of 84 breast lesions that underwent US, CEUS, CEUS-guided CNB and surgery were collected from February 2023 to February 2025, retrospectively. The malignancy upgrading was defined as CNB showing a B1-B3 classification, but surgical pathology showing a B5 classification. Uni-variable analysis compared US and CEUS features between non-upgrading and upgrading groups, and significant features were entered into multi-variable logistic regression. The performances of each independent predictor and their combinations were evaluated using receiver operating characteristic curve analysis. Results: Thirteen B1-B3 lesions were upgraded to B5 lesions. The multivariate analysis showed that size >20 mm on conventional US [odds ratio (OR) =5.85; 95% confidence interval (CI): 1.49-22.92; P=0.011], and enlarged enhancement range on CEUS (OR =17.44; 95% CI: 2.03-150.02; P=0.009) were independent predictors. The multi-variable model incorporating these two features yielded an area under the curve (AUC) of 0.83 and showed significantly higher predictive efficiency than size >20 mm (AUC =0.68) and enlarged enhancement range (AUC =0.74) (P=0.027 and P=0.013, respectively). Conclusions: Both conventional US and CEUS can provide useful information for postoperative malignancy upgrading in B1-B3 lesions. The integration of them improved the predictive performance.

Indexed as

Breast cancercontrast-enhanced ultrasound (CEUS)malignancy upgradingultrasound (US)

Identifiers

PMID41972079
PMCPMC13066852

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.