Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI.
Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI.
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DOI：
10.3390/cancers15061894
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发表时间：
2023-03-22
期刊：
影响因子：
5.2
通讯作者：
Kuttner, Samuel
中科院分区：
文献类型：
--
作者：
Cepeda, Santiago;Luppino, Luigi Tommaso;Perez-Nunez, Angel;Solheim, Ole;Garcia-Garcia, Sergio;Velasco-Casares, Maria;Karlberg, Anna;Eikenes, Live;Sarabia, Rosario;Arrese, Ignacio;Zamora, Tomas;Gonzalez, Pedro;Jimenez-Roldan, Luis;Kuttner, Samuel
在这项研究中，我们开发了一种预测模型，该模型采用多参数结构MRI的数据来预测胶质母细胞瘤的局部复发，为临床医生在日常实践中面临的问题提供了实用的解决方案：区分水肿和肿瘤浸润。预测这些复发风险高的区域的位置将可能允许个性化和优化胶质母细胞瘤的局部治疗，创造新的手术切除限制和放射治疗靶点。我们的研究结果可能会提高这些患者的生存率，并开辟一条新的研究路线，使人们能够更好地了解胶质瘤侵袭的机制。此外，我们还评估了外部多中心患者队列的结果，从而证明了该模型的适用性，尽管存在MRI采集协议和扫描仪制造商。该模型将通过一个存放处向公众开放，供任何机构实施。全球公认的胶质母细胞瘤手术策略是切除增强型肿瘤。然而，肿瘤周围区域具有浸润区域，这是未来肿瘤复发的原因。本研究旨在评估一种预测模型，该模型使用基于体素的磁共振成像（MRI）数据放射组学分析来识别未来复发的区域。这项多机构研究包括对诊断为胶质母细胞瘤的患者进行回顾性分析，这些患者接受了手术，并完全切除了增强肿瘤。55例患者符合选择标准。研究样本分为训练（N = 40）和测试（N = 15）数据集。随访MRI用于地面实况定义，术后结构多参数MRI用于提取基于体素的放射组学特征。形变配准用于配准每个患者的MRI序列，然后在术后扫描中分割瘤周区域，并在随访扫描中分割增强的肿瘤。与增强肿瘤体素重叠的瘤周体素被标记为复发，而不重叠的体素被标记为非复发。从肿瘤周围区域提取基于体素的放射组学特征。四个基于机器学习的分类器被训练用于递归预测。基于区域的评价方法用于模型评价。使用基于区域的评估，分类提升（CatBoost）分类器在测试数据集上获得了最佳性能，平均曲线下面积（AUC）为0.81 ± 0.09，准确度为0.84 ± 0.06。预测和实际复发区域之间存在明显的视觉对应关系。我们开发了一种方法，可以准确预测胶质母细胞瘤患者MRI扫描中未来肿瘤复发的区域。这可以使手术和放射治疗适应这些地区，以潜在地延长这些患者的生存期。
In this study, we developed a predictive model that employs data from multiparametric structural MRI to predict local recurrence in glioblastoma, providing a practical solution to an issue clinicians face in our daily practice: discriminating edema from tumor infiltration. Predicting the location of these areas at high risk of recurrence will potentially allow for personalizing and optimizing the local treatment of glioblastomas, creating new surgical resection limits and radiotherapy targets. Our findings could potentially improve the survival rate of these patients and open a new line of research that permits a better understanding of the mechanisms of glioma invasion. In addition, we evaluated our results in an external multicenter cohort of patients, thus demonstrating the applicability of the model despite the MRI acquisition protocols and scanner manufacturers. The model will be publicly available through a repository for its implementation by any institution. The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.
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