半定量和定量动态对比增强（DCE）MRI 参数作为生物靶向放射治疗的前列腺癌成像生物标志物。

BACKGROUND: Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS: Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS: All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS: Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

背景：生物靶向放射治疗计划需要对肿瘤进行体素级别的特征描述。动态对比增强（DCE）DCE MRI 已显示出在定义体素水平生物学特征方面的潜力。在这项研究中，我们考虑了定性、半定量和定量评估 DCE MRI 与扩散加权成像（DWI）和 T2 加权（T2w）成像相比，在体素水平检测前列腺癌的相对价值。

方法：70 例前列腺癌患者在根治性前列腺切除术前进行了多参数 MRI 检查，包括 T2w、DWI 和 DCE MRI。从 DWI 计算表观扩散系数（ADC）图，并从 DCE MRI 计算半定量和定量参数。肿瘤位置和分级通过与共配准的全切片组织学进行验证。应用 Kolmogorov-Smirnov 检验确定肿瘤和良性体素中的 MRI 参数是否存在显著差异。计算 Cohen's d 以量化最有前途的生物标志物。比较 Parker 和 Weinmann 动脉输入函数（AIF）在区分肿瘤和良性组织方面的能力。使用分类器模型确定 DCE MRI 参数是否比 ADC 和 T2w 单独提高了肿瘤检测的准确性。

结果：所有 MRI 参数在肿瘤和良性体素中的数据分布均有显著差异。对于低级别肿瘤，半定量 DCE MRI 参数达峰时间（TTP）是最具区分力的，优于 ADC。对于高级别肿瘤，ADC 是最具区分力的，其次是 DCE MRI 参数 Ktrans、初始增强率（IRE）和 TTP。利用 Parker AIF 的定量参数能更好地区分肿瘤和良性体素值，优于 Weinmann AIF。包括 DCE 参数的分类器模型与 T2w 和 ADC 单独使用相比，低级别肿瘤的检测准确率为 78%对 58%，高级别肿瘤的检测准确率为 85%对 72%。

结论：将 DCE MRI 参数与 DWI 和 T2w 结合使用，可提高体素水平肿瘤检测的准确性。DCE MRI 参数应用于空间描述肿瘤生物学，为生物靶向放射治疗计划提供依据。