Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.
Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.
复制标题
DOI：
10.1038/s41598-021-96189-8
复制
发表时间：
2021-09-23
影响因子：
4.6
通讯作者：
Peet AC
中科院分区：
文献类型：
--
作者：
Grist JT;Withey S;Bennett C;Rose HEL;MacPherson L;Oates A;Powell S;Novak J;Abernethy L;Pizer B;Bailey S;Clifford SC;Mitra D;Arvanitis TN;Auer DP;Avula S;Grundy R;Peet AC
脑肿瘤是儿童肿瘤人群中死亡率最高的原因。诊断通常采用磁共振成像。由于个体肿瘤类型的数量相对较少，生存生物标志物的识别具有挑战性。69名经活检证实患有脑瘤的儿童被纳入这项研究。所有参与者在诊断时均进行灌注和扩散加权成像。影像学资料采用常规方法处理，并进行贝叶斯生存分析。对生存特征进行无监督和有监督机器学习，以确定与生存相关的新子组。进行亚组分析以了解影像学特征的差异。生存分析显示，弥散和灌注联合成像能够确定两个新的具有不同生存特征的脑肿瘤亚组（p < 0.01），随后通过神经网络对其进行分类，准确率高达98%。对高级别肿瘤的分析显示，具有高风险和低风险影像学特征的两组肿瘤生存率有显著差异（p = 0.029）。本研究建立了一种新的儿童脑肿瘤生存模型。肿瘤灌注在决定生存中起着关键作用，应被视为未来成像方案的重中之重。
Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.
登录
查看更多内容
影响因子：
3
作者：
Annest A;Bumgarner RE;Raftery AE;Yeung KY
通讯作者：
Yeung KY
影响因子：
3.5
作者：
Morrell, Glen R.;Schabel, Matthias C.
通讯作者：
Schabel, Matthias C.
DOI：
10.1177/0271678x17713434
发表时间：
2018-09
期刊：
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
影响因子：
--
作者：
van Osch MJ;Teeuwisse WM;Chen Z;Suzuki Y;Helle M;Schmid S
通讯作者：
Schmid S
影响因子：
4.6
作者：
Schwalbe EC;Hicks D;Rafiee G;Bashton M;Gohlke H;Enshaei A;Potluri S;Matthiesen J;Mather M;Taleongpong P;Chaston R;Silmon A;Curtis A;Lindsey JC;Crosier S;Smith AJ;Goschzik T;Doz F;Rutkowski S;Lannering B;Pietsch T;Bailey S;Williamson D;Clifford SC
通讯作者：
Clifford SC
DOI：
10.1016/s1470-2045(17)30243-7
发表时间：
2017-07
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Schwalbe EC;Lindsey JC;Nakjang S;Crosier S;Smith AJ;Hicks D;Rafiee G;Hill RM;Iliasova A;Stone T;Pizer B;Michalski A;Joshi A;Wharton SB;Jacques TS;Bailey S;Williamson D;Clifford SC
通讯作者：
Clifford SC