Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.
Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.
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DOI：
10.1016/j.ijrobp.2018.05.041
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发表时间：
2018-11-15
期刊：
影响因子：
--
通讯作者：
Kleinberg L
中科院分区：
文献类型：
--
作者：
Peng L;Parekh V;Huang P;Lin DD;Sheikh K;Baker B;Kirschbaum T;Silvestri F;Son J;Robinson A;Huang E;Ames H;Grimm J;Chen L;Shen C;Soike M;McTyre E;Redmond K;Lim M;Lee J;Jacobs MA;Kleinberg L
脑转移瘤立体定向放射外科手术（SRS）后的治疗效果或放射性坏死是一种常见现象，通常与真实进展难以区分。放射组学是一个新兴的领域，有望改善传统的成像。在这项研究中，我们试图将基于放射学的预测模型应用于SRS后诊断治疗效果的问题。我们纳入了约翰霍普金斯卫生系统的患者，他们接受SRS治疗脑转移，随后因症状性生长而接受切除术。我们还纳入了可能具有治疗效果的病例，其中病变生长但随后自发消退。病变在术前T1对比后和T2液体衰减反转恢复磁共振成像上半自动分割，并使用内部开发的软件提取放射组学特征。将单变量逻辑回归上表现最好的特征输入到混合特征选择/分类模型IsoSVM中，使用留一交叉验证进行参数优化和进一步的特征选择。通过100次重复的10倍交叉验证评估最终模型性能。所有病例均由委员会认证的神经放射科医生进行独立审查以进行比较。我们在66名患者中确定了82个治疗病灶，其中77个病灶得到病理学证实。在每个磁共振成像序列上，每个分段病变提取了51个放射组学特征。一个优化的IsoSVM分类器的基础上排名靠前的放射组学特征的灵敏度和特异性分别为65.38%和86.67%，留一交叉验证的曲线下面积为0.81。只有73%的病例可由神经放射科医生分类，敏感性为97%，特异性为19%。放射组学有望区分SRS治疗脑转移瘤的治疗效果和真实进展。建立在来自机构队列的放射组学特征上的预测模型在交叉验证测试中表现良好。这些结果需要在独立数据集中进一步验证。这些工作对于指导个体患者的管理和评估新干预措施的结果是非常宝贵的。脑转移瘤立体定向放射外科手术后的治疗效果是一种常见的现象，通常与真实进展难以区分。我们应用基于放射组学的预测模型，在机构队列中区分立体定向放射外科手术后的治疗效果与真实进展，在留一交叉验证中表现出色。随着独立数据集的进一步验证，这些工作对于指导个体患者的管理和评估新干预措施的结果可能是非常宝贵的。
Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional imaging. In this study, we sought to apply a radiomics-based prediction model to the problem of diagnosing treatment effect after SRS. We included patients in the Johns Hopkins Health System who were treated with SRS for brain metastases who subsequently underwent resection for symptomatic growth. We also included cases of likely treatment effect in which lesions grew but subsequently regressed spontaneously. Lesions were segmented semiautomatically on preoperative T1 postcontrast and T2 fluid-attenuated inversion recovery magnetic resonance imaging, and radiomic features were extracted with software developed in-house. Top-performing features on univariate logistic regression were entered into a hybrid feature selection/classification model, IsoSVM, with parameter optimization and further feature selection performed using leave-one-out cross-validation. Final model performance was assessed by 10-fold cross-validation with 100 repeats. All cases were independently reviewed by a board-certified neuroradiologist for comparison. We identified 82 treated lesions across 66 patients, with 77 lesions having pathologic confirmation. There were 51 radiomic features extracted per segmented lesion on each magnetic resonance imaging sequence. An optimized IsoSVM classifier based on top-ranked radiomic features had sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%. Radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions. Treatment effect after stereotactic radiosurgery for brain metastases is a common phenomenon often indistinguishable from true progression. We applied a radiomics-based prediction model to discriminate treatment effect from true progression after stereotactic radiosurgery in an institutional cohort, with excellent performance on leave-one-out cross-validation. With further validation in independent datasets, such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.
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DOI：
10.1016/j.nicl.2014.08.008
发表时间：
2014
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Shinohara RT;Sweeney EM;Goldsmith J;Shiee N;Mateen FJ;Calabresi PA;Jarso S;Pham DL;Reich DS;Crainiceanu CM;Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
DOI：
10.1080/23808993.2016.1164013
发表时间：
2016
影响因子：
1.2
作者：
Parekh V;Jacobs MA
通讯作者：
Jacobs MA
影响因子：
--
作者：
Soliman H;Das S;Larson DA;Sahgal A
通讯作者：
Sahgal A
影响因子：
10.6
作者：
Yu, Huan;Caldwell, Curtis;Mozeg, Daniel
通讯作者：
Mozeg, Daniel
影响因子：
4.8
作者：
Kano, Hideyuki;Kondziolka, Douglas;Lunsford, L. Dade
通讯作者：
Lunsford, L. Dade