Purpose:To investigate if radiomics signatures generated from longitudinal CT scans could predict IRE treatment effectiveness and outcomes in patients with locally advanced pancreatic cancer (LAPC).Methods:A cohort of 50 (60% male, mean [SD] age 60.7 [8.7] years) LAPC patients treated with IRE were retrospectively selected. Preoperative and 12-week follow-up CT scans were reviewed by two radiologists for tumor segmentation. A total of 2078 features were extracted: shape (n= 16), texture (n= 68), filter (n= 1892), intensity (n= 18), and local texture (n= 84). Principal component analysis (PCA) was applied to develop composite radiomics features. Composite signatures and clinically relevant radiomics features were correlated with time to recurrence (TTR), time to local recurrence (TTLR), time to distant recurrence (TTDR), recurrence-free survival (RFS) and overall survival (OS). Risk stratification performance was evaluated using hazard ratios (HRs), and significance was evaluated using the log-rank test.Results:Statistically significant separation between high and low patient TTR risk groups was observed in the following: gray-level co-occurrence matrix (HR = 2.65,p< 0.01, median survival difference = 6.6 mo); composite radiomics features derived from the following feature groups: all radiomics features (HR = 2.27,p= 0.01, median survival difference = 6.4 mo), intensity features (HR = 3.13,p< 0.01, median survival difference = 14.0 mo), and filter features (HR = 2.27,p= 0.01, median survival difference = 6.4 mo).Conclusions:Pre-treatment radiomics signatures were significantly associated with LAPC patient outcomes. The observed correlations used pre-treatment CT scans, implying that the features predict the individual risk of disease recurrence.
目的：探讨基于纵向CT扫描的影像组学特征能否预测局部晚期胰腺癌（LAPC）患者不可逆电穿孔（IRE）治疗效果及预后。方法：回顾性纳入50例接受IRE治疗的LAPC患者（男性占60%，平均[标准差]年龄60.7[8.7]岁）。由两名放射科医师对术前及术后12周随访CT图像进行肿瘤分割。共提取2078个影像特征：形态特征（16个）、纹理特征（68个）、滤波特征（1892个）、强度特征（18个）及局部纹理特征（84个）。采用主成分分析法构建复合影像组学特征。将复合特征及临床相关影像组学特征与复发时间、局部复发时间、远处复发时间、无复发生存期及总生存期进行相关性分析。通过风险比评估风险分层效能，并采用对数秩检验评估显著性。结果：在以下特征组中观察到高、低复发时间风险患者组间存在统计学显著差异：灰度共生矩阵特征（风险比=2.65，p<0.01，中位生存期差异=6.6个月）；基于以下特征组构建的复合影像组学特征：全部影像组学特征（风险比=2.27，p=0.01，中位生存期差异=6.4个月）、强度特征（风险比=3.13，p<0.01，中位生存期差异=14.0个月）及滤波特征（风险比=2.27，p=0.01，中位生存期差异=6.4个月）。结论：治疗前影像组学特征与LAPC患者预后显著相关。基于治疗前CT扫描观察到的相关性表明，这些特征可预测个体疾病复发风险。
肿瘤(癌症)患者之家