对于胶质母细胞瘤(GBM),已经提出了各种预后列线图。这项研究旨在评估机器学习模型,以预测患者的总体生存(OS)和无进展生存(PFS)的基础上的临床,病态,基于语义MRI,和FET-PET/CT衍生的信息。最后,评估了增加治疗特征的价值.
回顾性分析了一百八十九例患者。我们评估了临床,病态,和治疗信息。在MRI上确定VASARI语义成像特征集。保留了术前FET-PET/CT图像的代谢信息。我们在患者训练集上生成了多个随机生存森林预测模型,并进行了内部验证。创建了单特征类模型,包括“临床,“病态”,\"\"基于核磁共振成像,\"和\"基于FET-PET/CT的\"模型,以及组合。将治疗特征与所有其他特征组合。
在所有单要素类模型中,在OS(C指数:0.61[95%置信区间:0.51-0.72])和PFS(C指数:0.61[0.50-0.72])的验证集中,基于MRI的模型具有最高的预测性能.所有功能的组合确实提高了所有单个功能类模型的性能,操作系统和PFS的C指数为0.70(0.59-0.84)和0.68(0.57-0.78),分别。在OS和PFS的验证集上,添加治疗信息可进一步提高预后性能,直至C指数为0.73(0.62-0.84)和0.71(0.60-0.81)。分别,允许对患者组的OS进行显著分层。
基于MRI的特征是预后评估中最相关的特征类别。结合临床,病态,和成像信息增加了OS和PFS的预测能力。通过添加处理特征实现了进一步的增加。
For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients\' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, pathological, semantic MRI-based, and FET-PET/CT-derived information. Finally, the value of adding treatment features was evaluated.
One hundred and eighty-nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET-PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including \"clinical,\" \"pathological,\" \"MRI-based,\" and \"FET-PET/CT-based\" models, as well as combinations. Treatment features were combined with all other features.
Of all single feature class models, the MRI-based model had the highest prediction performance on the validation set for OS (C-index: 0.61 [95% confidence interval: 0.51-0.72]) and PFS (C-index: 0.61 [0.50-0.72]). The combination of all features did increase performance above all single feature class models up to C-indices of 0.70 (0.59-0.84) and 0.68 (0.57-0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C-indices of 0.73 (0.62-0.84) and 0.71 (0.60-0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS.
MRI-based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.