Background/Objectives: Germ Cell Neoplasia In Situ (GCNIS) is considered the precursor lesion for the majority of testicular germ cell tumors (TGCTs). The aim of this study was to evaluate whether first-order radiomics features derived from volumetric diffusion tensor imaging (DTI) metrics—specifically apparent diffusion coefficient (ADC) and fractional anisotropy (FA) histogram parameters—can detect GCNIS. Methods: This study included 15 men with TGCTs and 10 controls. All participants underwent scrotal MRI, including DTI. Volumetric ADC and FA histogram metrics were calculated for the following tissues: group 1, TGCT; group 2: testicular parenchyma adjacent to tumor, histologically positive for GCNIS; and group 3, normal testis. Non-parametric statistics were used to assess differences in ADC and FA histogram parameters among the three groups. Pearson’s correlation analysis was followed by ordinal regression analysis to identify key predictive histogram parameters. Results: Widespread distributional differences (p< 0.05) were observed for many ADC and FA variables, with both TGCTs and GCNIS showing significant divergence from normal testes. Among the ADC statistics, the 10th percentile and skewness (p= 0.042), range (p= 0.023), interquartile range (p= 0.021), total energy (p= 0.033), entropy and kurtosis (p= 0.027) proved the most significant predictors for tissue classification. FA_energy (p= 0.039) was the most significant fingerprint of the carcinogenesis among the FA metrics. These parameters correctly characterized 88.8% of TGCTs, 87.5% of GCNIS tissues and 100% of normal testes. Conclusion: Radiomics features derived from volumetric ADC and FA histograms have promising potential to differentiate TGCTs, GCNIS, and normal testicular tissue, aiding early detection and characterization of pre-cancerous lesions.
背景/目的：生殖细胞原位瘤（GCNIS）被认为是大多数睾丸生殖细胞肿瘤（TGCTs）的前驱病变。本研究旨在评估基于体积扩散张量成像（DTI）指标——特别是表观扩散系数（ADC）和分数各向异性（FA）直方图参数——提取的一阶影像组学特征能否检测GCNIS。方法：本研究纳入15例TGCT患者和10例对照者。所有参与者均接受阴囊磁共振成像（MRI），包括DTI序列。对以下组织计算体积ADC和FA直方图指标：第1组为TGCT肿瘤组织；第2组为肿瘤旁睾丸实质（组织学证实存在GCNIS）；第3组为正常睾丸组织。采用非参数统计评估三组间ADC和FA直方图参数的差异。通过皮尔逊相关性分析及序数回归分析确定关键预测性直方图参数。结果：多数ADC和FA变量在三组间呈现广泛分布差异（p<0.05），TGCT与GCNIS均与正常睾丸组织存在显著区别。在ADC统计量中，第10百分位数、偏度（p=0.042）、极差（p=0.023）、四分位距（p=0.021）、总能量（p=0.033）、熵与峰度（p=0.027）被证明是组织分类的最显著预测因子。在FA指标中，FA能量（p=0.039）是癌变过程最显著的特征标识。基于这些参数实现了对TGCT组织88.8%、GCNIS组织87.5%及正常睾丸组织100%的准确分类。结论：基于体积ADC和FA直方图提取的影像组学特征在区分TGCT、GCNIS与正常睾丸组织方面具有良好潜力，有助于癌前病变的早期检测与特征识别。
肿瘤(癌症)患者之家