波谱学杂志

• •    

联合 T2WI 和 DWI 纹理分析对鉴别前列腺癌和前列腺增生的价值研究

李露*, 高勇, 周妤盼   

  1. 宣城市中心医院影像科,安徽 宣城 242000
  • 收稿日期:2025-02-17 修回日期:2025-04-16 出版日期:2025-04-23 在线发表日期:2025-04-23
  • 通讯作者: 李露 E-mail:xiebok31777@163.com

Construction of Prostate Cancer Model Based on T2WI Combined with DWI Multi Parameter MRI Texture Feature Fusion

LI Lu*,GAO Yong,ZHOU Yupan   

  1. Imaging Department of Xuancheng Central Hospital, Xuancheng 242000, China
  • Received:2025-02-17 Revised:2025-04-16 Published:2025-04-23 Online:2025-04-23
  • Contact: LI Lu E-mail:xiebok31777@163.com

摘要: 前列腺癌(PCa)是男性最常见的癌症之一,影像学检查已逐渐应用于其诊断中.本研究基于磁共振T2加权成像(T2WI)联合扩散加权成像(DWI)纹理分析构建PCa模型分析.筛选出差异显著的特征参数构建PCa预测模型,利用受试者工作特征(ROC)曲线及曲线下面积(AUC)明确纹理特征对疾病鉴别诊断价值.结果表明,将方差T2WI、平均值DWI构建PCa预测模型,AUC值为0.994,具有较高的预测价值.证明基于T2WI与DWI的纹理特征构建预测模型对鉴别PCa有较好应用价值.

关键词: T2加权成像, 弥散加权成像, 前列腺癌, 前列腺增生, 纹理分析

Abstract: Prostate cancer (PCa) is one of the most common cancers in men, and imaging techniques have been progressively used in its diagnosis. This study develops a prostate cancer model based on magnetic resonance T2-weighted imaging (T2WI) combined with diffusion-weighted imaging (DWI) texture analysis. Significant feature parameters were selected to construct a predictive model for prostate cancer, and the diagnostic value of texture features for disease discrimination was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The results showed that constructing a prostate cancer prediction model using variance T2WI and mean DWI has a high predictive value, with an AUC value of 0.994, which demonstrated that the textural features based on T2WI and DWI have good application value in distinguishing prostate cancer.

Key words: T2 weighted imaging, Diffusion-weighted imaging, Prostate cancer, Benign prostatic hyperplasia, texture analysis