Ultrasound Department, Shenzhen Peoples Hospital, Shenzhen 518020, China; Ultrasound Department, The Second Clinical Medical College, Jinan University, Shenzhen, 518020, China; Ultrasound Department, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China.
The Second Clinical Medical College, Jinan University, Shenzhen 518020, China.
Eur J Cancer. 2024 Sep;209:114259. doi: 10.1016/j.ejca.2024.114259. Epub 2024 Aug 3.
HER2 is a key biomarker for breast cancer treatment and prognosis. Traditional assessment methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are effective but costly and time-consuming. Our model incorporates these methods alongside photoacoustic imaging to enhance diagnostic accuracy and provide more comprehensive clinical insights.
A total of 301 breast tumors were included in this study, divided into HER2-positive (3+ or 2+ with gene amplification) and HER2-negative (below 3+ and 2+ without gene amplification) groups. Samples were split into training and validation sets in a 7:3 ratio. Statistical analyses involved t-tests, chi-square tests, and rank-sum tests. Predictive factors were identified using univariate and multivariate logistic regression, leading to the creation of three models: ModA (clinical factors only), ModB (clinical plus ultrasound factors), and ModC (clinical, ultrasound, and photoacoustic imaging-derived oxygen saturation (SO2)).
The area under the curve (AUC) for ModA was 0.756 (95 % CI: 0.69-0.82), ModB increased to 0.866 (95 % CI: 0.82-0.91), and ModC showed the highest performance with an AUC of 0.877 (95 % CI: 0.83-0.92). These results indicate that the comprehensive model combining clinical, ultrasound, and photoacoustic imaging data (ModC) performed best in predicting HER2 expression.
The findings suggest that integrating clinical, ultrasound, and photoacoustic imaging data significantly enhances the accuracy of predicting HER2 expression. For personalised breast cancer treatment, the integrated model could provide a comprehensive and reproducible decision support tool.
HER2 是乳腺癌治疗和预后的关键生物标志物。传统的评估方法，如免疫组织化学（IHC）和荧光原位杂交（FISH），虽然有效，但成本高且耗时。我们的模型结合了这些方法以及光声成像，以提高诊断准确性并提供更全面的临床见解。
本研究共纳入 301 例乳腺肿瘤，分为 HER2 阳性（3+或 2+且基因扩增）和 HER2 阴性（低于 3+和 2+且无基因扩增）两组。样本按 7:3 的比例分为训练集和验证集。统计分析包括 t 检验、卡方检验和秩和检验。使用单因素和多因素逻辑回归识别预测因素，从而构建了三个模型：ModA（仅临床因素）、ModB（临床加超声因素）和 ModC（临床、超声和光声成像衍生的氧饱和度（SO2））。
ModA 的曲线下面积（AUC）为 0.756（95%置信区间：0.69-0.82），ModB 增加到 0.866（95%置信区间：0.82-0.91），而 ModC 的表现最佳，AUC 为 0.877（95%置信区间：0.83-0.92）。这些结果表明，结合临床、超声和光声成像数据的综合模型（ModC）在预测 HER2 表达方面表现最佳。
这些发现表明，整合临床、超声和光声成像数据可显著提高预测 HER2 表达的准确性。对于个性化乳腺癌治疗，该综合模型可为综合且可重复的决策支持工具提供依据。
