Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Br J Radiol. 2021 May 1;94(1121):20210005. doi: 10.1259/bjr.20210005. Epub 2021 Mar 11.
To explore the potential value of multiparametric magnetic resonance imaging (mpMRI) texture analysis (TA) to predict new Gleason Grade Group (GGG).
Fifty-eight lesions of fifty patients who underwent mpMRI scanning, including -weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters were obtained by the postprocessing software, and each lesion was assigned to its corresponding GGG. TA parameters derived from T2WI and DWI were statistically analyzed in detail.
Energy, inertia, and correlation derived from apparent diffusion coefficient (ADC) maps and T2WI had a statistically significant difference among the five groups. Kurtosis, energy, inertia, correlation on ADC maps and Energy, inertia on T2WI were moderately related to the GGG trend. ADC-energy and T2-energy were significant independent predictors of the GGG trend. ADC-energy, T2WI-energy, and T2WI-correlation had a statistically significant difference between GGG1 and GGG2-5. ADC-energy were significant independent predictors of the GGG1. ADC-energy, T2WI-energy, and T2WI-correlation showed satisfactory diagnostic efficiency of GGG1 (area under the curve (AUC) 84.6, 74.3, and 83.5%, respectively), and ADC-energy showed excellent sensitivity and specificity (88.9 and 95.1%, respectively).
TA parameters ADC-energy and T2-energy played an important role in predicting GGG trend. Both ADC-energy and T2-correlation produced a high diagnostic power of GGG1, and ADC-energy was perfect predictors of GGG1.
TA parameters were innovatively used to predict new GGG trend, and the predictive factors of GGG1 were screen out.
探讨多参数磁共振成像（mpMRI）纹理分析（TA）预测新 Gleason 分级分组（GGG）的潜在价值。
回顾性分析 50 例患者的 58 个病灶，这些患者在经直肠超声（TRUS）引导下前列腺核心穿刺活检前均接受了 mpMRI 扫描，包括 T2 加权成像（T2WI）和弥散加权成像（DWI）。通过后处理软件获得 TA 参数，将每个病灶分配到相应的 GGG。对 T2WI 和 DWI 得出的 TA 参数进行详细的统计学分析。
表观弥散系数（ADC）图和 T2WI 得出的能量、惯性和相关性在 5 组间有统计学差异。峰度、ADC 图上的能量、惯性和相关性以及 T2WI 上的能量和惯性与 GGG 趋势中度相关。ADC 能量和 T2WI 能量是 GGG 趋势的显著独立预测因子。ADC 能量、T2WI 能量和 T2WI 相关性在 GGG1 和 GGG2-5 之间有统计学差异。ADC 能量是 GGG1 的显著独立预测因子。ADC 能量、T2WI 能量和 T2WI 相关性对 GGG1 具有较好的诊断效率（曲线下面积（AUC）分别为 84.6%、74.3%和 83.5%），ADC 能量具有较好的敏感性和特异性（分别为 88.9%和 95.1%）。
TA 参数 ADC 能量和 T2 能量在预测 GGG 趋势方面起着重要作用。ADC 能量和 T2 相关性均对 GGG1 具有较高的诊断效能，而 ADC 能量是 GGG1 的完美预测因子。
TA 参数被创新性地用于预测新的 GGG 趋势，并筛选出 GGG1 的预测因素。
