Kavli Institute for Systems Neuroscience, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
PLoS One. 2021 May 27;16(5):e0252387. doi: 10.1371/journal.pone.0252387. eCollection 2021.
Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment.
To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI).
62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models.
All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant.
Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.
磁共振成像（MRI）对于前列腺癌的检测和分期至关重要。然而，为了选择合适的治疗方法，需要改进工具来区分低风险和高风险的癌症。
研究使用 T2-和弥散加权成像（T2-DWI）联合的双组份模型估计的信号分数在诊断中的潜力。
62 例前列腺癌患者和 14 例前列腺增生（BPH）患者因临床怀疑癌症而行 T2-DWI 检查（TE = 55 和 73 ms，b 值 = 50 和 700 s/mm2），每个体素有 4 个测量值。MRI 检查后根据活检结果证实癌症，根据放射学报告划定感兴趣区（ROI）。通过 2 个自由参数的模型拟合计算所提出的双组份模型的慢组份（SFslow）信号分数，并与传统的双指数和单指数表观扩散系数（ADC）模型进行比较。
所有三种模型均显示外周带（PZ）肿瘤与正常组织 ROI 之间有显著差异（p<0.0001），而非 PZ 肿瘤与 BPH ROI 之间无差异。用于区分肿瘤与前列腺体素的受试者工作特征曲线下面积分别为双组份、双指数和单指数模型的 0.956、0.949 和 0.949。肿瘤值与 Gleason 分级组之间的 Spearman 相关系数分别为 0.370、0.499 和-0.490，均为中度相关，但无统计学意义。
基于 T2-DWI 联合的双组份模型的信号分数可以区分肿瘤与正常前列腺组织，显示出用于前列腺癌诊断的潜力。该模型的表现与传统的弥散模型相似。
