基于T2WI与DWI多参数MRI纹理特征融合的前列腺癌预测模型构建

李露; 高勇; 周妤盼

doi:10.11938/cjmr20253144

波谱学杂志 >

2025 , Vol. 42 >Issue 4: 355 - 363

DOI: https://doi.org/10.11938/cjmr20253144

研究论文

基于T2WI与DWI多参数MRI纹理特征融合的前列腺癌预测模型构建

李露 ,
高勇 ,
周妤盼

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宣城市中心医院影像科，安徽宣城 242000

收稿日期: 2025-02-17

网络出版日期: 2025-04-23

基金资助

安徽省中医药学会中医药科研基金(2024ZYYXH145)

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

LI Lu ,
GAO Yong ,
ZHOU Yupan

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Imaging Department of Xuancheng Central Hospital, Xuancheng 242000, China

Received date: 2025-02-17

Online published: 2025-04-23

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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）纹理特征分析构建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

本文引用格式

李露 , 高勇 , 周妤盼 . 基于T2WI与DWI多参数MRI纹理特征融合的前列腺癌预测模型构建[J]. 波谱学杂志, 2025 , 42(4) : 355 -363 . DOI: 10.11938/cjmr20253144

Abstract

Prostate cancer (PCa) is one of the most common cancers in men, and imaging techniques have been increasingly 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 the prostate cancer prediction model incorporating ${\sigma }_{\text{T2WI}}^{\text{2}}$ and ${\mu }_{\text{DWI}}^{}$ achieved an AUC value of 0.994, demonstrating high predictive performance. This study confirms that the textural features based on T2WI and DWI have good application value in distinguishing prostate cancer.

Key words： T₂ weighted imaging (T2WI); diffusion-weighted imaging (DWI); prostate cancer (PCa); benign prostatic hyperplasia (BPH); texture analysis

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