Liu Zhi, Xu Xinyi, Zhang Wang, Zhang Liqiang, Wen Ming, Gao Jueni, Yang Jun, Kan Yubo, Yang Xing, Wen Zhipeng, Chen Shanxiong, Cao Xu
Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Quant Imaging Med Surg. 2024 Jan 3;14(1):251-263. doi: 10.21037/qims-23-807. Epub 2024 Jan 2.
BACKGROUND: The mutational status of alpha-thalassemia X-linked intellectual disability () is an important indicator for the treatment and prognosis of high-grade gliomas, but reliable testing currently requires invasive procedures. The objective of this study was to develop a clinical trait-imaging fusion model that combines preoperative magnetic resonance imaging (MRI) radiomics and deep learning (DL) features with clinical variables to predict status in isocitrate dehydrogenase ()-mutant high-grade astrocytoma. METHODS: A total of 234 patients with -mutant high-grade astrocytoma (120 mutant type, 114 wild type) from 3 centers were retrospectively analyzed. Radiomics and DL features from different regions (edema, tumor, and the overall lesion) were extracted to construct multiple imaging models by combining different features in different regions for predicting status. An optimal imaging model was then selected, and its features and linear coefficients were used to calculate an imaging score. Finally, a fusion model was developed by combining the imaging score and clinical variables. The performance and application value of the fusion model were evaluated through the comparison of receiver operating characteristic curves, the construction of a nomogram, calibration curves, decision curves, and clinical application curves. RESULTS: The overall hybrid model constructed with radiomics and DL features from the overall lesion was identified as the optimal imaging model. The fusion model showed the best prediction performance with an area under curve of 0.969 in the training set, 0.956 in the validation set, and 0.949 in the test set as compared to the optimal imaging model (0.966, 0.916, and 0.936, respectively) and clinical model (0.677, 0.641, 0.772, respectively). CONCLUSIONS: The clinical trait-imaging fusion model based on preoperative MRI could effectively predict the mutation status of individuals with -mutant high-grade astrocytoma and has the potential to help patients through the development of a more effective treatment strategy before treatment.
背景：α-地中海贫血X连锁智力障碍（）的突变状态是高级别胶质瘤治疗和预后的重要指标，但目前可靠的检测需要侵入性操作。本研究的目的是开发一种临床特征-影像融合模型，该模型将术前磁共振成像（MRI）的影像组学和深度学习（DL）特征与临床变量相结合，以预测异柠檬酸脱氢酶（）突变型高级别星形细胞瘤中的状态。 方法：回顾性分析来自3个中心的234例突变型高级别星形细胞瘤患者（120例突变型，114例野生型）。提取不同区域（水肿、肿瘤和整个病变）的影像组学和DL特征，通过组合不同区域的不同特征构建多个影像模型，以预测状态。然后选择最佳影像模型，利用其特征和线性系数计算影像评分。最后，通过将影像评分与临床变量相结合，开发出融合模型。通过比较受试者工作特征曲线、构建列线图、校准曲线、决策曲线和临床应用曲线，评估融合模型的性能和应用价值。 结果：用来自整个病变的影像组学和DL特征构建的总体混合模型被确定为最佳影像模型。与最佳影像模型（分别为0.966、0.916和0.936）和临床模型（分别为0.677、0.641和0.772）相比，融合模型在训练集、验证集和测试集中的曲线下面积分别为0.969、0.956和0.949，显示出最佳的预测性能。 结论：基于术前MRI的临床特征-影像融合模型能够有效预测突变型高级别星形细胞瘤患者的突变状态，并有潜力通过在治疗前制定更有效的治疗策略来帮助患者。
Cancers (Basel). 2022-3-31
Neuro Oncol. 2021-8-2
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