Evidence map›Paper›PMID 40874222›Full record

ArticleFrontiers in oncology2025

Analysis of dynamic contrast-enhanced T1-weighted imaging parameters in type II TIC breast lesions.

Jimei Jiang, Weibin Ma, Ming Li, Shanhua Han, Yu Luo

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Article in Frontiers in oncology, 2025. 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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5 authors.

Jimei Jiang *Department of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Weibin Ma *Department of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Ming LiDepartment of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Shanhua HanDepartment of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Yu LuoDepartment of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI) of breast lesions, type III time-intensity curves (TICs) are associated with malignant lesions, and type I TICs are associated with benign lesions, but the association of type II curves with the status of breast lesions remains controversial. This study aimed to analyze the semi-quantitative parameters derived from DCE-T1WI in patients with type II TIC breast lesions and to develop a nomogram for the benign/malignant classification of lesions with type II TICs. Methods: Data of patients with type II TIC breast lesions were retrospectively collected. The following semi-quantitative parameters were collected: signal intensity of pre-contrast (SIpre), peak signal intensity (SIp), signal intensity of wash-in (SIwi), slope of peak (Sp), slope of wash-in (Swi), time to peak (Tp), time of wash-in (Twi), enhancement rate of peak (ERp), and enhancement rate of wash-in (ERwi). Univariable and multivariable analyses were performed to select useful clinical and DCE-T1WI features. Selected features were used for nomogram model development. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) were used for model performance evaluation. Results: Ninety-eight female patients with type II TIC breast lesions were included (53 with malignant lesions). After univariable and multivariable logistic regression analyses, only Tp showed an odds ratio of 0.95 (p = 0.014, 95% confidence interval: 0.93-0.97). A nomogram was constructed and included Swi, SIp, SIwi, SIpre, Tp, ERp, and Sp. The sensitivity, specificity, PPV, NPV, and accuracy of the nomogram were 0.827, 0.761, 0.795, 0.796, and 0.796, respectively. The AUC was 0.862. Conclusion: DCE-T1WI semi-quantitative parameters were different among benign and malignant lesions in patients with type II TIC lesions. Tp showed the most significant difference after a multivariable logistic regression analysis. The results suggest that DCE-T1WI semi-quantitative parameters can be used to predict malignant lesions in patients with type II TIC.

Indexed as

breast cancerclassificationmultivariable analysisnomogramtime signal intensity curve

Identifiers

PMID40874222
PMCPMC12378278

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