Department of Neurosurgery, University of Minnesota Medical School, Minneapolis, Minnesota, USA.
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
Neurosurgery. 2021 Jul 15;89(2):323-328. doi: 10.1093/neuros/nyab130.
The rarity of Isocitrate Dehydrogenase mutated (mIDH) glioblastomas relative to wild-type IDH glioblastomas, as well as their distinct tumor physiology, effectively render them "outliers". Specialized tools are needed to identify these outliers.
To carefully craft and apply anomaly detection methods to identify mIDH glioblastoma based on radiomic features derived from magnetic resonance imaging.
T1-post gadolinium images for 188 patients and 138 patients were downloaded from The Cancer Imaging Archive's (TCIA) The Cancer Genome Atlas (TCGA) glioblastoma collection, and from the University of Minnesota Medical Center (UMMC), respectively. Anomaly detection methods were tested on glioblastoma image features for the precision of mIDH detection and compared to standard classification methods.
Using anomaly detection training methods, we were able to detect IDH mutations from features in noncontrast-enhancing regions in glioblastoma with an average precision of 75.0%, 69.9%, and 69.8% using three different models. Anomaly detection methods consistently outperformed traditional two-class classification methods from 2 unique learning models (67.9%, 67.6%). The disparity in performances could not be overcome through newer, popular models such as neural networks (67.4%).
We employed an anomaly detection strategy in the detection of IDH mutation in glioblastoma using preoperative T1 postcontrast imaging. We show these methods outperform traditional two-class classification in the setting of dataset imbalances inherent to IDH mutation prevalence in glioblastoma. We validate our results using an external dataset and highlight new possible avenues for radiogenomic rare event prediction in glioblastoma and beyond.
相较于野生型 IDH 胶质母细胞瘤，异柠檬酸脱氢酶突变（mIDH）胶质母细胞瘤较为罕见，且其肿瘤生理学特征也明显不同，因此它们属于“异常值”。需要专门的工具来识别这些异常值。
利用基于磁共振成像的放射组学特征，精心设计并应用异常检测方法来识别 mIDH 胶质母细胞瘤。
从癌症成像档案（TCIA）的癌症基因组图谱（TCGA）胶质母细胞瘤数据库和明尼苏达大学医学中心（UMMC）分别下载了 188 例和 138 例患者的 T1 对比后图像。在胶质母细胞瘤图像特征上测试了异常检测方法，以评估其对 mIDH 检测的精确性，并与标准分类方法进行了比较。
使用异常检测训练方法，我们能够从胶质母细胞瘤非增强区的特征中检测出 IDH 突变，平均准确率分别为 75.0%、69.9%和 69.8%，使用了 3 种不同的模型。异常检测方法在来自 2 个独特学习模型的传统两分类方法中表现一致（67.9%、67.6%）。通过更新的、流行的模型（如神经网络）也无法克服性能上的差异（67.4%）。
我们在使用术前 T1 对比后成像检测胶质母细胞瘤中的 IDH 突变时采用了异常检测策略。我们表明，在 IDH 突变在胶质母细胞瘤中普遍存在的数据集不平衡的情况下，这些方法优于传统的两分类方法。我们使用外部数据集验证了我们的结果，并强调了放射基因组学罕见事件预测在胶质母细胞瘤及其他领域的新的可能途径。
