Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine.
Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine.
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DOI：
10.1016/j.ijrobp.2021.12.153
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发表时间：
2022-04-01
期刊：
影响因子：
--
通讯作者：
Yang W
中科院分区：
文献类型：
--
作者：
Lao Y;Ruan D;Vassantachart A;Fan Z;Ye JC;Chang EL;Chin R;Kaprealian T;Zada G;Shiroishi MS;Sheng K;Yang W
为了提供早期和局部胶质母细胞瘤（GBM）复发预测，我们引入了一种新型的手术后多参数基于MR的支持载体机（SVM）方法，并与干细胞壁ni（SCN）接近度估计结合。
这项研究在50例复发性GBM的患者中临床诊断出复发之前，使用了手术后的MRI扫描，主要的预测管道由基于接近度的估计器组成，以识别具有高度的SVM分类器的重复率，并在HRR中提供了Voxel-wister inter sclrr的范围。肿瘤随后，将HRR内的多参数体素（从T1，T1CE，FLAIR，T2，ADC）分组为复发（从临床诊断中扭曲）和非透明的子区域，并随机地培训了SVMPE，并将其分别为60％然后将VMPE推算为早期复发的较早时间点预测。
在30名测试队列患者的2个月前MRI上，SVMPE分类器的召回率为0.80，精度为0.69，F1评分为0.73，平均边界距离和7.49 mm的平均边界距离通常显示出较小的较小的亚赛车量和较小的亚列表。
我们证明了一种新型的体素预测方法SVMPE，用于基于临床随访的GBM复发，SVMPE有望在临床诊断前2个月内将复发的亚临床痕迹定位，并且可以用于指导更多有效的个性化早期救助治疗。
To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel post-surgery multi-parametric MR-based support vector machine (SVM) method coupling with stem cell niches (SCN) proximity estimation. This study utilized post-surgery MRI scans ~2 months before clinically diagnosed recurrence from 50 patients with recurrent GBM. The main prediction pipeline consists of a proximity-based estimator to identify regions with high risks of recurrence (HRR), and an SVM classifier to provide voxel-wise prediction in HRR. The HRRs were estimated using the weighted sum of inverse distances to two possible origins of recurrence – SCN and tumor cavity. Subsequently, multi-parametric voxels (from T1, T1ce, FLAIR, T2, ADC) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and non-recurrent subregions, and fed into the proximity estimation coupled SVM classifier - SVMPE. The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVMPE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVMPE predictive cluster sizes and the image intensities from the five MR sequences were compared across time to assess the progressive subclinical traces. On 2-month pre-recurrence MRIs from 30 test cohort patients, the SVMPE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and an average boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1ce and ADC values over time. We demonstrated a novel voxel-wise early prediction method, SVMPE, for GBM recurrence based on clinical follow-up MR scans. SVMPE is promising in localizing subclinical traces of recurrence 2-month ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.
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影响因子：
4.7
作者：
Lombard A;Digregorio M;Delcamp C;Rogister B;Piette C;Coppieters N
通讯作者：
Coppieters N
影响因子：
15.9
作者：
Cordova, James S.;Shu, Hui-Kuo G.;Shim, Hyunsuk
通讯作者：
Shim, Hyunsuk
DOI：
10.1016/j.radonc.2015.07.032
发表时间：
2015-08
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Chen L;Chaichana KL;Kleinberg L;Ye X;Quinones-Hinojosa A;Redmond K
通讯作者：
Redmond K
DOI：
10.1007/s00259-005-0038-6
发表时间：
2006-06-01
影响因子：
9.1
作者：
Fischer, BM;Olsen, MWB;Kristjansen, PEG
通讯作者：
Kristjansen, PEG
放射疗法的胶质母细胞瘤进展。
影响因子：
3.4
作者：
Mann J;Ramakrishna R;Magge R;Wernicke AG
通讯作者：
Wernicke AG