Sittiwong Wiwatchai, Dankulchai Pittaya, Wongsuwan Pitchayut, Prasartseree Tissana, Thaweerat Wajana, Thornsri Nerisa, Tuntapakul Pongpop
Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Research Unit, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand.
Clin Transl Radiat Oncol. 2025 Apr 19;53:100965. doi: 10.1016/j.ctro.2025.100965. eCollection 2025 Jul.
The study included 100 patients with LACC who underwent definitive CCRT with IMRT/VMAT technique followed by 3D-IGABT. MRI-based contouring included T2WI and DWI images for primary tumor (GTVp) and lymph nodes (GTVn). The contours were imported to MIM software to extract first-order radiomic features. Radiomic values from pre-treatment (PreRx), pre-brachytherapy (PreBT), differences between PreRx and PreBT (Diff) radiomic and clinical factors were analyzed using univariate and multivariate Cox regression analysis. Predictive models of PFS, LRFS, DMFS, and OS were created along with the optimism index and calibration plot.
The median follow-up time was 24.5 months. The 2-year of PFS, LRFS, DMFS, and OS rates were 71, 88.6, 83.1, and 83.5 %, respectively. For all clinical outcomes, CF + RF combined from PreRx and PreBT resulted in the highest Harrell's C-index compared with the CF or RF alone. Compare with Diff models, models from PreRx and PreBT resulted in higher Harrell's C-index. The C-indexes from the CF + RF model from PreRx and PreBT for PFS, LRFS, DMFS, and OS were 0.739, 0.873, 0.830 and 0.967 with the optimism indexes of 0.312, 0.381, 0.316, and 0.242, respectively.
Radiomic features from the first-order statistics added values to clinical factors to predict the outcomes after CCRT. The highest prediction model performance was for the combined clinical and radiomics from PreRx and PreBT.
本研究纳入了100例接受调强放射治疗（IMRT）/容积调强弧形放疗（VMAT）技术的根治性同步放化疗后行三维图像引导下近距离放疗（3D-IGABT）的局部晚期宫颈癌（LACC）患者。基于磁共振成像（MRI）的轮廓勾画包括用于勾画原发肿瘤（GTVp）和淋巴结（GTVn）的T2加权成像（T2WI）和扩散加权成像（DWI）图像。将轮廓导入MIM软件以提取一阶放射组学特征。使用单因素和多因素Cox回归分析对治疗前（PreRx）、近距离放疗前（PreBT）、PreRx与PreBT之间的差异（Diff）的放射组学和临床因素的放射组学值进行分析。创建了无进展生存期（PFS）、局部区域复发无进展生存期（LRFS）、远处转移无进展生存期（DMFS）和总生存期（OS）的预测模型以及乐观指数和校准图。
中位随访时间为24.5个月。2年PFS、LRFS、DMFS和OS率分别为71%、88.6%、83.1%和83.5%。对于所有临床结局，与单独的临床因素（CF）或放射组学因素（RF）相比，PreRx和PreBT联合的CF + RF产生了最高的Harrell C指数。与Diff模型相比，PreRx和PreBT的模型产生了更高的Harrell C指数。PreRx和PreBT的CF + RF模型对于PFS、LRFS、DMFS和OS的C指数分别为0.739、0.873、0.830和0.967，乐观指数分别为0.312、0.381、0.316和0.242。
一阶统计量的放射组学特征为临床因素增加了价值，以预测同步放化疗后的结局。最高的预测模型性能是PreRx和PreBT联合的临床和放射组学模型。
