Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
Nuclear Medicine, Institut Godinot, 51100 Reims, France.
Int J Mol Sci. 2022 Aug 14;23(16):9120. doi: 10.3390/ijms23169120.
The aim of this study is to assess whether there are some correlations between radiomics and baseline clinical-biological data of prostate cancer (PC) patients using Fluorine-18 Fluoroethylcholine (F-FECh) PET/CT.
Digital rectal examination results (DRE), Prostate-Specific Antigen (PSA) serum levels, and bioptical-Gleason Score (GS) were retrospectively collected in newly diagnosed PC patients and considered as outcomes of PC. Thereafter, Volumes of interest (VOI) encompassing the prostate of each patient were drawn to extract conventional and radiomic PET features. Radiomic bivariate models were set up using the most statistically relevant features and then trained/tested with a cross-fold validation test. The best bivariate models were expressed by mean and standard deviation to the normal area under the receiver operating characteristic curves (mAUC, sdAUC).
Semiquantitative and radiomic analyses were performed on 67 consecutive patients. tSUVmean and tSkewness were significant DRE predictors at univariate analysis (OR 1.52 [1.01; 2.29], = 0.047; OR 0.21 [0.07; 0.65], = 0.007, respectively); moreover, tKurtosis was an independent DRE predictor at multivariate analysis (OR 0.64 [0.42; 0.96], = 0.03) Among the most relevant bivariate models, szm_2.5D.z.entr + cm.clust.tend was a predictor of PSA levels (mAUC 0.83 ± 0.19); stat.kurt + stat.entropy predicted DRE (mAUC 0.79 ± 0.10); cm.info.corr.1 + szm_2.5D.szhge predicted GS (mAUC 0.78 ± 0.16).
tSUVmean, tSkewness, and tKurtosis were predictors of DRE results only, while none of the PET parameters predicted PSA or GS significantly; F-FECh PET/CT radiomic models should be tested in larger cohort studies of newly diagnosed PC patients.
本研究旨在评估使用氟-18 氟乙基胆碱（F-FECh）PET/CT 对前列腺癌（PC）患者的放射组学与基线临床生物学数据之间是否存在相关性。
回顾性收集新诊断的 PC 患者的直肠指检（DRE）结果、前列腺特异性抗原（PSA）血清水平和活检 Gleason 评分（GS），并将其作为 PC 的结果。然后，在每位患者的前列腺上绘制感兴趣的体积（VOI），以提取常规和放射组学 PET 特征。使用最具统计学意义的特征建立放射组学双变量模型，并使用交叉折叠验证测试进行训练/测试。最优双变量模型用平均和标准偏差表示受试者工作特征曲线下的面积（mAUC、sdAUC）。
对 67 例连续患者进行了半定量和放射组学分析。单变量分析时，tSUVmean 和 tSkewness 是 DRE 的显著预测因子（OR 1.52[1.01；2.29]， = 0.047；OR 0.21[0.07；0.65]， = 0.007）；此外，tKurtosis 是多变量分析中 DRE 的独立预测因子（OR 0.64[0.42；0.96]， = 0.03）。在最相关的双变量模型中，szm_2.5D.z.entr + cm.clust.tend 是 PSA 水平的预测因子（mAUC 0.83 ± 0.19）；stat.kurt + stat.entropy 预测 DRE（mAUC 0.79 ± 0.10）；cm.info.corr.1 + szm_2.5D.szhge 预测 GS（mAUC 0.78 ± 0.16）。
tSUVmean、tSkewness 和 tKurtosis 仅可预测 DRE 结果，而 PET 参数均不能显著预测 PSA 或 GS；在新诊断的 PC 患者的更大队列研究中，应测试 F-FECh PET/CT 放射组学模型。
