Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients.
Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients.
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DOI：
10.1155/2015/842923
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发表时间：
2015
影响因子：
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通讯作者：
Van Huffel S
中科院分区：
文献类型：
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作者：
Ion-Margineanu A;Van Cauter S;Sima DM;Maes F;Van Gool SW;Sunaert S;Himmelreich U;Van Huffel S
目的。我们一直专注于根据从随访的GBM患者中检索的多参数MR数据来寻找最好地区分肿瘤进展和退化的分类器。材料和方法29例 基底膜患者术后接受了几个月的辅助治疗，获得了包括常规和先进磁共振成像(灌注、弥散和波谱)的多参数磁共振数据。为每个时间点建立了27个特征向量，但由于数据缺失或质量问题，并不是所有的特征都能在所有时间点获得。我们使用LOPO方法在完全数据和输入数据上对分类器进行测试。我们通过计算每个时间点的误码率和所有时间点的wBER来衡量性能。结果。如果我们在具有完整特征的数据上训练随机森林、LogitBoost或RobustBoost，我们可以100%准确地区分肿瘤进展和退化，比医生根据既定的放射标准贴上标签(进展或反应)的日期早一个时间点(即约1个月)。当仅对完整的灌注数据训练相同的分类器时，我们得到相同的结果。结论。我们的发现表明，集成分类器(即随机森林分类器和Boost分类器)在预测肿瘤进展方面显示出比已建立的放射学标准更早的有希望的结果，应该进一步研究。
Purpose. We have focused on finding a classifier that best discriminates between tumour progression and regression based on multiparametric MR data retrieved from follow-up GBM patients. Materials and Methods. Multiparametric MR data consisting of conventional and advanced MRI (perfusion, diffusion, and spectroscopy) were acquired from 29 GBM patients treated with adjuvant therapy after surgery over a period of several months. A 27-feature vector was built for each time point, although not all features could be obtained at all time points due to missing data or quality issues. We tested classifiers using LOPO method on complete and imputed data. We measure the performance by computing BER for each time point and wBER for all time points. Results. If we train random forests, LogitBoost, or RobustBoost on data with complete features, we can differentiate between tumour progression and regression with 100% accuracy, one time point (i.e., about 1 month) earlier than the date when doctors had put a label (progressive or responsive) according to established radiological criteria. We obtain the same result when training the same classifiers solely on complete perfusion data. Conclusions. Our findings suggest that ensemble classifiers (i.e., random forests and boost classifiers) show promising results in predicting tumour progression earlier than established radiological criteria and should be further investigated.