An Effective Ultrasound Features-Based Diagnostic Model via Principal Component Analysis Facilitated Differentiating Subtypes of Mucinous Breast Cancer From Fibroadenomas

Abstract

BackgroundMucinous breast carcinoma (MBC) is often misdiagnosed as fibroadenoma (FA),which can lead to inappropriate or delayed treatments. This study aimed to establish an efficient ultrasound (US)-based diagnostic model to distinguish MBC subtypes from FAs. MethodsBetween January 2017 and 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, with varimax rotation applied for interpretability. Comprehensive models were developed to distinguish PMBCs and MMBCs from FAs. ResultsSix principal components were selected, achieving a cumulative contribution rate of 77.46% for PMBCs vs. FAs and 78.62% for MMBCs vs. FAs. The principal component of cystic-solid composition and posterior acoustic enhancement demonstrated the highest diagnostic value for distinguishing PMBCs from FAs (AUC: 0.86, ACC: 80.31%). Features including vascularization, irregular shape, ill-defined border, and larger size exhibited the highest diagnostic value for distinguishing MMBCs from FAs (AUC: 0.90, ACC: 87.43%). 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. ConclusionThis diagnostic model holds promise for effectively distinguishing PMBCs and MMBCs from FAs, assisting radiologists in mitigating diagnostic biases and enhancing diagnostic efficiency.