山东大学学报 (医学版) ›› 2025, Vol. 63 ›› Issue (1): 73-80.doi: 10.6040/j.issn.1671-7554.0.2024.0810
• 临床研究 • 上一篇
孙婧1,杨瑞敏2,王聪3,张月4,罗兵2
SUN Jing1, YANG Ruimin2, WANG Cong3, ZHANG Yue4, LUO Bing2
摘要: 目的 探讨基于术前超声特征、炎症指标及超声影像组学特征构建的模型预测乳腺癌腋窝淋巴结(axillary lymph node, ALN)转移的价值。 方法 回顾性分析175例乳腺癌患者的乳腺超声图像和临床资料,使用3D Slicer软件勾画感兴趣区并提取影像组学特征,运用组间相关系数、皮尔森相关系数和递归特征消除法筛选特征,计算影像组学评分(radiomics score, Radscore),构建影像组学模型。通过单因素、多因素逻辑回归筛选临床危险因素构建临床模型,加入Radscore构建联合预测模型。使用受试者工作特征(receiver operating characteristic, ROC)曲线、校准曲线及决策曲线分析评估各模型的预测效能和临床价值。 结果 18个影像组学特征被纳入影像组学模型,肿瘤大小、超声ALN状态和血小板/淋巴细胞比值(platelet to lymphocyte ratio, PLR)被纳入临床模型,肿瘤大小、超声ALN状态、PLR与Radscore被纳入联合预测模型。联合预测模型的预测效能最高,在训练集和验证集的曲线下面积(area under the curve, AUC)分别为0.935、0.858。 结论 基于肿瘤大小、超声ALN状态、炎症指标PLR及超声影像组学构建的联合预测模型能有效预测乳腺癌患者ALN转移。
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