Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Eur J Radiol. 2019 Jul;116:144-149. doi: 10.1016/j.ejrad.2019.05.006. Epub 2019 May 7.
Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum diameter and volume are currently considered of value to increase diagnostic accuracy. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with csPCa presence.
We retrospectively enrolled 75 consecutive subjects, who underwent mpMRI on a 3 T scanner, divided based on MRI index lesion Gleason Score in a csPCa group (GS > 3 + 4, n = 41) and a non-csPCa one (n = 34). Ten shape features were extracted both from axial T2-weighted and ADC maps images, after lesion tridimensional segmentation. Univariable and multivariable logistic analysis were used to evaluate the relationship between shape features and csPCa. Diagnostic performance was assessed measuring the area under the curve of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC. A P value < 0.05 was considered statistically significant.
Univariable analysis demonstrated that almost every shape feature was statistically significant between csPCa e non-csPCa groups. However, multivariable analysis revealed that the parameter defined as surface area to volume ratio (SAVR), especially when extracted from ADC maps is the strongest independent predictor of csPCa among tested shape features.
The radiomic shape feature SAVR, extracted from ADC maps after index lesion segmentation, appears as a promising tool for csPCa detection.
前列腺多参数磁共振成像（mpMRI）是检测临床显著前列腺癌（csPCa）的首选影像学方法。在各种参数中，病灶最大直径和体积目前被认为有助于提高诊断准确性。定量放射组学允许提取更先进的形状特征。我们的目的是评估从 MRI 指数病变中提取的哪些形状特征与 csPCa 的存在相关。
我们回顾性纳入了 75 名连续患者，这些患者在 3T 扫描仪上接受了 mpMRI，根据 MRI 指数病变 Gleason 评分将患者分为 csPCa 组（GS>3+4，n=41）和非 csPCa 组（n=34）。在三维分割病灶后，从轴向 T2 加权和 ADC 图中提取了 10 个形状特征。采用单变量和多变量逻辑分析评估形状特征与 csPCa 之间的关系。通过接受者操作特征（ROC）分析的曲线下面积评估诊断性能。使用每个 ROC 的最佳截断值确定诊断准确性、敏感性和特异性。P 值<0.05 被认为具有统计学意义。
单变量分析表明，csPCa 和非 csPCa 组之间几乎每个形状特征都具有统计学意义。然而，多变量分析表明，在测试的形状特征中，表面积与体积比（SAVR）参数，特别是从 ADC 图中提取的参数，是 csPCa 的最强独立预测因子。
从 ADC 图中提取的放射组学形状特征 SAVR，作为检测 csPCa 的一种很有前途的工具。
