School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China.
PLoS One. 2024 May 31;19(5):e0304419. doi: 10.1371/journal.pone.0304419. eCollection 2024.
In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.
近年来，各种数据驱动算法已被应用于脑胶质瘤 MRI 检测的分类和分期。然而，在纯数据驱动的深度学习算法中，脑胶质瘤 MRI 数据的有限可用性在提取高质量特征和捕捉其复杂模式方面带来了挑战。此外，为 2D 数据设计的分析方法需要选择理想的肿瘤图像切片，这与实际临床情况不符。我们的研究提出了一种新颖的脑胶质瘤分期模型，即用于 3D 数据的医学认知嵌入（MCE）模型。该模型将知识特征嵌入到数据驱动方法中，以提高特征提取的质量。方法包括以下关键组件：（1）深度特征提取，利用不同 MRI 序列的成像技术特征，在算法和策略两个层面上设计了两种方法，以模拟医学专业人士在阅片过程中对真实图像的解读学习过程；（2）我们进行了广泛的放射组学特征提取，捕捉到相关特征，如纹理、形态和灰度分布；（3）通过参考放射学诊断要点、放射组学特征实验结果以及各种 MRI 序列的成像特征，我们手动创建诊断特征（Diag-Features）。所提出方法的有效性在公开的 BraTS2018 和 BraTS2020 数据集上进行了严格评估。与大多数知名的纯数据驱动模型相比，我们的方法实现了更高的准确性、召回率和精度，分别达到 96.14%、93.4%、97.06%和 97.57%、92.80%、95.96%。
