Zhou Kun-Peng, Huang Hua-Bin, Li Shu-Yi, Luo Zhong-Xing, Cheng Xian-Wen, Liu Di-Min, Bian Jie, Liu Qing-Yu
Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, No.628, Zhenyuan Road, Xinhu Street, Guangming District, Shenzhen, Guangdong, 518107, P.R. China.
Department of Radiology, Second Affiliated Hospital of Dalian Medical University, No.467, Zhongshan Road, Shahekou District, Dalian, Liaoning, 116027, P.R. China.
BMC Med Imaging. 2025 Aug 19;25(1):339. doi: 10.1186/s12880-025-01881-0.
Reactive stroma plays a pivotal role in the genesis, progression, and metastasis of prostate cancer (PCa). Higher reactive stromal grade (RSG) generally portends a poorer prognosis. The aim of the study is non-invasively evaluate RSG by preoperative mono-exponential model, stretch-exponent model (SEM) and diffusion kurtosis imaging (DKI), and isolate the independent predictor of high RSG (> 50% reactive stroma) in parameters of mono-exponential model, SEM and DKI.
Totally, 54 low RSG (≤ 50% reactive stroma) patients and 26 high RSG patients were prospectively enrolled in the study. Apparent diffusion coefficient (ADC), mean kurtosis (MK), mean diffusivity (MD), distributed diffusion coefficient (DDC), and heterogeneity index (α) values of all lesions were measured on GE Workstation 4.6. Spearman's rank correlation analysis was used to analysis the correlation between RSG and parameters of SEM and DKI. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of those parameters in differentiating low RSG and high RSG. DeLong's test was used to assess whether the differences of AUC for each parameter were statistically significant. Binary logistic regression analysis was performed to identify independent predictors of high RSG.
ADC (r = - 0.352, p = 0.001), DDC (r = - 0.579, p < 0.001) and MD (r = - 0.597, p < 0.001) values showed significant negative correlations with RSG, while MK value (r = 0.658, p < 0.001) demonstrated a significant positive correlation. MK (AUC = 0.816, p < 0.001) was superior to ADC (AUC = 0.717, p < 0.001), DDC (AUC = 0.781, p < 0.001) and MD (AUC = 0.774, p < 0.001) in differentiating low and high RSG, but the differences between these AUCs were not statistically significant (all p > 0.05). Binary logistic regression analysis demonstrated a statistically significant model (χ² =43.222, p < 0.001), and showed that MK (odds ratio = 10.185; 95% CI: 2.467 ~ 21.694; p < 0.001) and MD (odds ratio = 0.014; 95% CI: 0.003 ~ 0.367; p < 0.001) were the independent predictors of high RSG.
Although ADC, DDC, and MD values were significantly negatively correlated with RSG, and MK was significantly positively correlated, and all three models-mono-exponential model, SEM, and DKI-demonstrated good performance in differentiating between low and high RSG, only parameters MD and MK values of DKI were identified as independent predictors of high RSG.
反应性基质在前列腺癌（PCa）的发生、发展和转移中起关键作用。较高的反应性基质分级（RSG）通常预示预后较差。本研究的目的是通过术前单指数模型、拉伸指数模型（SEM）和扩散峰度成像（DKI）对RSG进行无创评估，并在单指数模型、SEM和DKI参数中分离出高RSG（>50%反应性基质）的独立预测因子。
本研究前瞻性纳入了54例低RSG（≤50%反应性基质）患者和26例高RSG患者。在GE工作站4.6上测量所有病变的表观扩散系数（ADC）、平均峰度（MK）、平均扩散率（MD）、分布扩散系数（DDC）和异质性指数（α）值。采用Spearman等级相关分析来分析RSG与SEM和DKI参数之间的相关性。进行受试者操作特征（ROC）曲线分析，以评估这些参数在区分低RSG和高RSG方面的诊断性能。使用DeLong检验评估每个参数的AUC差异是否具有统计学意义。进行二元逻辑回归分析以确定高RSG的独立预测因子。
ADC（r = -0.352，p = 0.001）、DDC（r = -0.579，p < 0.001）和MD（r = -0.597，p < 0.001）值与RSG呈显著负相关，而MK值（r = 0.658，p < 0.001）呈显著正相关。在区分低RSG和高RSG方面，MK（AUC = 0.816，p < 0.001）优于ADC（AUC = 0.717，p < 0.001）、DDC（AUC = 0.781，p < 0.001）和MD（AUC = 0.774，p < 0.001），但这些AUC之间的差异无统计学意义（均p > 0.05）。二元逻辑回归分析显示模型具有统计学意义（χ² = 43.222，p < 0.001），并表明MK（比值比 = 10.185；95%CI：2.467～21.694；p < 0.001）和MD（比值比 = 0.014；95%CI：0.003～0.367；p < 它0.001）是高RSG的独立预测因子。
虽然ADC、DDC和MD值与RSG呈显著负相关，MK呈显著正相关，且单指数模型、SEM和DKI这三种模型在区分低RSG和高RSG方面均表现良好，但只有DKI的MD和MK值参数被确定为高RSG的独立预测因子。
