An effective ultrasound features-based diagnostic model via principal component analysis facilitated differentiating subtypes of mucinous breast cancer from fibroadenomas

Background Mucinous breast carcinoma (MBC) risks misdiagnosis as a fibroadenoma (FA), leading to inappropriate or delayed treatments. This study aimed to establish an efficient ultrasound (US)-based diagnostic model for distinguishing MBC subtypes from FAs Methods Between January 2017 to February 2024, 240 lesions were enrolled, comprising 65 cases of pure mucinous breast carcinoma (PMBC), 47 cases of mixed mucinous breast carcinoma (MMBC), and 128 cases of FAs. Ten US feature variables underwent principal component analysis (PCA), models were constructed based on components explaining over 75% of the total variation, and varimax rotation was applied. Then, comprehensive models were constructed for distinguishing PMBCs or MMBCs from FAs Results Six principal components were selected, achieving a cumulative contribution rate of 77.46% (PMBCs vs. FAs) and 78.62% (MMBCs vs. FAs). The principal component of cystic-solid composition and posterior acoustic enhancement demonstrated the highest diagnostic value (AUC:0.86, ACC: 80.31%) for distinguishing PMBCs from FAs. Features including vascularization, irregular shape, ill-defined border, and larger size exhibited the highest diagnostic value (AUC:0.90, ACC:87.43%) for distinguishing MMBCs from FAs. The comprehensive models showed excellent clinical value in distinguishing PMBCs (AUC = 0.86, SEN = 86.15%, SPE = 73.44%, ACC = 77.72%) and MMBCs (AUC = 0.92, SEN = 80.85%, SPE = 95.31%, ACC = 91.43%) from FAs Conclusion This diagnostic model holds promise for effectively distinguishing PMBCs and MMBCs from FAs, assisting radiologists in mitigating diagnostic biases and enhancing diagnostic efficiency. MicroAbstract Distinguishing between Mucinous Breast Carcinoma (MBC) and Fibroadenoma (FA) by ultrasound presents a challenge. This study collected 240 cases of MBC and FA and developed principal component analysis models based on ultrasound features to differentiate pure MBC and mixed MBC from FA. Our findings revealed a high diagnostic efficacy for this approach, demonstrating its potential in clinical practice.