Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients’ cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.
近期临床研究发现，部分胶质母细胞瘤患者在放疗开始前即出现放射治疗早期进展现象。现有文献主要依据临床病理学特征对该患者群体进行描述。本研究首次通过计算与统计建模方法，系统探讨了传统影像组学特征、复杂多分辨率分形纹理特征及不同分子特征（MGMT、IDH突变）在鉴别放射治疗早期进展与非进展病例中的诊断与预后预测价值。研究分析了70例患者的放疗计划T1增强磁共振序列，采用集成方法进行1000次五折交叉验证，结果显示放射治疗早期进展与非进展分类的曲线下面积达0.793±0.082。在考虑依赖删失（部分患者未能随访至死亡）的情况下，基于Copula的建模方法筛选出对生存概率及预后分组具有显著意义的特征（p值<0.05）。患者队列的生存预测精度达到0.881±0.056。通过融合特征计算的预后指数显示，84.62%的放射治疗早期进展病例归属于不良预后组，表明融合特征对放射治疗早期进展病例具有较高预测效能。实验结果进一步证实，在多分辨率分形纹理特征在放射治疗早期进展预测及生存结局评估方面均优于传统影像组学特征。
肿瘤(癌症)患者之家