BACKGROUND Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP. METHODS We evaluated independent discovery (n = 40) and replication (n = 23) cohorts of glioblastoma patients who underwent second resection due to progressive radiographic changes suspicious for recurrence. Deep learning and conventional feature extraction methods were used to extract quantitative characteristics from the mpMRI scans. Multivariate analysis of these features revealed radiophenotypic signatures distinguishing among TP, PsP, and mixed response that compared with similar categories blindly defined by board-certified neuropathologists. Additionally, interinstitutional validation was performed on 20 new patients. RESULTS Patients who demonstrate TP on neuropathology are significantly different (P < .0001) from those with PsP, showing imaging features reflecting higher angiogenesis, higher cellularity, and lower water concentration. The accuracy of the proposed signature in leave-one-out cross-validation was 87% for predicting PsP (area under the curve [AUC], 0.92) and 84% for predicting TP (AUC, 0.83), whereas in the discovery/replication cohort, the accuracy was 87% for predicting PsP (AUC, 0.84) and 78% for TP (AUC, 0.80). The accuracy in the interinstitutional cohort was 75% (AUC, 0.80). CONCLUSION Quantitative mpMRI analysis via machine learning reveals distinctive noninvasive signatures of TP versus PsP after treatment of glioblastoma. Integration of the proposed method into clinical studies can be performed using the freely available Cancer Imaging Phenomics Toolkit.

中文翻译：

---

经组织病理学验证的机器学习射线照相生物标记物，用于胶质母细胞瘤的真实进展与伪进展之间的非侵入性区分。

背景技术胶质母细胞瘤患者在最大安全切除和化学放疗后的影像学检查通常显示出新的增强作用，引起了对肿瘤进展的担忧。但是，在30％至50％的患者中，这些增强作用主要代表治疗效果或伪进展（PsP）。我们假设对临床获得的多参数磁共振成像（mpMRI）进行定量机器学习分析可以识别亚视觉成像特征，以提供可以区分PsP真正进展（TP）的强大，无创成像特征。方法我们评估了胶质母细胞瘤患者的独立发现（n = 40）和复制（n = 23）队列，这些患者由于进行性影像学改变可疑复发而进行了第二次切除。深度学习和常规特征提取方法用于从mpMRI扫描中提取定量特征。对这些特征的多变量分析显示，与经董事会认证的神经病理学家盲目定义的类似类别相比，放射性表型特征可区分TP，PsP和混合反应。此外，对20名新患者进行了机构间验证。结果在神经病理学上表现为TP的患者与PsP的患者显着不同（P <.0001），其影像学特征反映出更高的血管生成，更高的细胞密度和更低的水浓度。建议的签名在留一法式交叉验证中的准确性对于预测PsP（曲线下面积[AUC]为0.92）为87％，对于预测TP为84％（AUC为0.83），而在发现/复制队列中，预测PsP的准确性为87％（AUC，0.84），而TP的准确性为78％（AUC，0.80）。机构间队列的准确性为75％（AUC，0.80）。结论通过机器学习进行的mpMRI定量分析显示，胶质母细胞瘤治疗后TP与PsP的独特非侵入性特征。可以使用免费提供的Cancer Imaging Phenomics Toolkit将建议的方法整合到临床研究中。