Clinical value of a multiple regression model based on multimodal ultrasound for predicting luminal and non-luminal breast cancer subtypes

KANG Jinjuan; HUANG Ling; YU Peng; QIU Jingjing; JIANG Yuyang; WANG Jinrong

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Journal of Hunan Normal University(Medical Science) ›› 2025, Vol. 22 ›› Issue (3) : 126-133.

Clinical value of a multiple regression model based on multimodal ultrasound for predicting luminal and non-luminal breast cancer subtypes

KANG Jinjuan, HUANG Ling, YU Peng, QIU Jingjing, JIANG Yuyang, WANG Jinrong

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Abstract

Objective To analyze the multimodal ultrasound features of different breast cancers (BC) and explore their diagnostic value in molecular subtyping. Methods This study included 120 BC patients admitted to our hospital, with pathological diagnosis as the gold standard. Data from ultrasonic imaging (US), strain ultrasound elastography (SUE), contrast-enhanced ultrasound (CEUS), and relevant clinical information were collected. Chi-square tests were used to screen for significantly different features between groups, and two-way stepwise logistic regression analysis was performed to construct prediction models for molecular subtypes (Luminal vs. non-Luminal). Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of single-modal and multimodal combined ultrasound techniques. Results US examination showed that Luminal subtypes (LA/LB) were significantly correlated with lymph node morphology, enhanced range, and internal echo, while non-Luminal subtypes (HER2⁺/TNBC) were significantly associated with lesion morphology, calcification, lymph node morphology, and margin. SUE results indicated that all molecular subtypes were significantly correlated with elasticity scores. CEUS analysis revealed that both Luminal and non-Luminal subtypes were significantly associated with enhancement defects and enhanced range. The area under the ROC curve (AUC) of the constructed prediction models was 0.859 for Luminal subtype differentiation and 0.947 for non-Luminal subtype discrimination. Efficacy comparison showed that the multimodal ultrasound combined model exhibited superior diagnostic performance compared with single-modal models. Conclusion Multimodal ultrasound can effectively distinguish BC molecular subtypes, and the combined model significantly improves diagnostic efficacy, providing an imaging basis for clinical precise subtyping.

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breast cancer / multimodal ultrasound / contrast-enhanced ultrasound / molecular subtypes

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KANG Jinjuan, HUANG Ling, YU Peng, QIU Jingjing, JIANG Yuyang, WANG Jinrong. Clinical value of a multiple regression model based on multimodal ultrasound for predicting luminal and non-luminal breast cancer subtypes[J]. Journal of Hunan Normal University(Medical Science). 2025, 22(3): 126-133

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