Karami Golestan, Giuseppe Orlando Marco, Delli Pizzi Andrea, Caulo Massimo, Del Gratta Cosimo
Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy.
Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy.
Cancers (Basel). 2021 Oct 4;13(19):4976. doi: 10.3390/cancers13194976.
Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI with non-supervised and supervised machine learning methods to predict GBM patients' survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.
尽管肿瘤治疗取得了进展，但反应不一致仍是多形性胶质母细胞瘤（GBM）面临的主要挑战，这导致了不同的生存时间。我们的目标是将多模态MRI与无监督和有监督的机器学习方法相结合，以预测GBM患者的生存时间。为此，我们识别了肿瘤的不同区域并提取了它们的特征。接下来，我们应用随机森林递归特征消除法（RF-RFE）来识别最相关的特征，以输入到GBoost机器中。本研究纳入了29例生存时间已知的GBM患者。使用最佳特征对RF-RFE GBoost模型进行评估，以评估生存预测性能。此外，使用单变量和多变量Cox回归分析来分析总生存期（OS），以评估感兴趣区域（ROIs）及其特征对生存的影响。结果表明，RF-RFE Gboost机器能够以75%的准确率预测生存时间。结果还显示，低灌注区域的相对脑血容量（rCBV）在各组之间存在显著差异，并且在反应变量（生存时间）的变化率方面具有最大的效应量。总之，不仅多模态MRI的整合，而且特征选择方法都可以提高分类器的性能。
