Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
影像组学（Radiomics）作为一种新兴的人工智能辅助诊断手段，有潜力准确地描述胃癌特征，并对临床指标进行预测。然而在相关研究中，选择使用三维（3D）感兴趣区域（Regions of Interest，ROI）标注，还是更加节省人力算力的二维（2D） ROI标注，长期以来存在争议。针对这一挑战性问题，本文开展了基于2D-3D多维CT影像组学特征的胃癌分期预测方法研究，探索最优的ROI标注方式和特征提取方式，并做出以下工作：
以上研究成果以第一作者发表在医学影像领域主流SCI期刊 IEEE Journal of Biomedical and Health Informatics（SCI IF: 5.223）。
Gastric cancer poses a serious threat to national life and brings a huge burden of diagnosis and treatment to our country. Precise diagnosis of gastric cancer staging before treatment has important reference value and influence for clinicians to formulate treatment plan. Computed Tomography (CT), the most commonly used method of preoperative assessment, provides a non-invasive means by which radiologists can observe the full picture of the stomach. However, it is difficult to achieve accurate staging of gastric cancer based solely on the subjective judgment of radiologists.
Radiomics, an emerging artificial intelligence-assisted diagnosis method, has the potential to accurately describe the characteristics of gastric cancer and predict clinical indicators. However, in related studies, the choice to use three-dimensional (3D) Regions of Interest (ROI) labeling, or the more labor-saving two-dimensional (2D) ROI labeling, has long been controversial. In response to this challenging problem, this paper has carried out studies on gastric cancer staging prediction methods based on 2D-3D multi-dimensional CT radiomic features, explored the optimal ROI labeling method and feature extraction method, and made the following work:
1. Aiming at the question of which of the 2D and 3D radiomic features are more suitable for the prediction of gastric cancer staging indicators, three tasks of clinical interest for gastric cancer were conducted (they are, whether the patient has a pathological T4 stage, whether the patient has lymph node metastasis, and whether the patient has vascular invasion), and the predictive performance of 3D radiomic features and 2D radiomic features were compared through six models. The experimental results showed that the effects of 2D models were better than those of 3D models, implying that the annotation of 3D ROI and the calculation of 3D radiomic features could not bring benefits to model performance.
2. In view of the possible reproducibility problem of the main experiment, this project established 45,000 models through an auxiliary experiment, and explored the performance of 2D and 3D models under different resampling intervals and different dataset divisions. The results showed that the effects of the 2D models were still generally better than those of the 3D models, which further expanded and strengthened the conclusion of the main experiment.
3. This project further combined 2D and 3D radiomic features to establish a hybrid model to explore whether the advantages of 2D and 3D features could be combined to make more accurate predictions. The results showed that compared with the 2D model, the hybrid model had no bonus, which further proved the conclusion of the main experiment from the side.
Through this project, we found that 3D ROI annotation and 3D radiomic features could not bring benefits compared to 2D, and the 2D radiomic model could better predict the clinical tasks of gastric cancer. This finding can provide a basis for the researchers to select features and algorithms in the radiomic analysis related to gastric cancer. By using a more time-saving and labor-saving 2D ROI annotation, researchers can significantly speed up the research while maintaining the model's predictive performance. This discovery has the potential to promote the standardization of radiomic research procedures related to gastric cancer and reduce the barriers.
The above research was published in the IEEE Journal of Biomedical and Health Informatics (SCI IF: 5.223) as the first author.
|Keyword||影像组学 模式识别 计算机辅助诊断 人工智能 计算机断层扫描成像 胃癌|
|Sub direction classification||医学影像处理与分析|
|孟令威. 基于2D-3D多维CT影像组学特征的胃癌分期预测方法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2021.|
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