|Place of Conferral||中国科学院自动化研究所|
|Keyword||影像组学 深度学习 胃癌 TNM 分期 计算机断层扫描|
2、针对临床对胃癌淋巴结转移精确诊断的迫切需求，并结合胃周淋巴结由于数量众多、分布广泛、解剖关系复杂等因素导致难以对其直接评估的实际情况，本文提出了基于CT深层特征的胃癌淋巴结转移预测算法，提高影像组学量化肿瘤原发灶表型的能力，提高特征提取对目标任务的适应性。该算法通过迁移学习及多阶段训练的策略引导深度学习网络学习到图像和临床任务之间的深层次映射关系，同时有效降低过拟合风险。为提取深度学习特征，我们使用正则化方法和相关性分析筛选出高信息量的非冗余节点，进而对节点输出的特征图进行多重测量，得到与任务显著相关的高通量特征。此外，我们设计了多阶段的特征选择方案，以进一步提高模型在多中心数据集上的稳定性。最终，我们构建了可以对胃癌具体N分期进行预测的影像组学模型，并在国内中心和国际中心构成的多个独立数据集上进行了测试。实验结果表明，与人工预定义特征、医生判断、现有临床指标相比，我们提出的基于CT深层特征的预测模型对胃癌淋巴结转移具有更好的预测效果。英国皇家马斯登医院临床研究主任David Cunningham教授在临床肿瘤学顶级期刊《Annals of Oncology》上撰写述评论文评价我们的工作“比临床N分期等临床指标更准确，可用于判断需要切除的淋巴结范围”。
Gastric cancer is one of the most common malignant tumors worldwide. In recent years, about 70% of the new gastric cancer cases occurred in Asia, of which nearly half in China. Early detection, diagnosis, and treatment are the keys to improving the life quality and prognosis of gastric cancer patients. The tumor-node-metastasis staging (TNM staging) of gastric cancer describes the progression of the primary tumor invasion (T stage), the regional lymph node metastasis (N stage), and the distant metastasis (M stage). In the clinic, early and accurate prediction of pathological TNM staging of gastric cancer is an important basis for analyzing tumor progression, evaluating patient prognosis, and making optimal individualized treatment decisions.
The gold standard for TNM staging of gastric cancer is determined by the observation of human histopathological samples obtained during surgical treatment under the microscope, which is invasive and lagging. For the pretreatment diagnosis of gastric cancer patients, the Chinese Society of Clinical Oncology (CSCO) and the National Comprehensive Cancer Network recommend non-invasive medical imaging technologies such as computed tomography (CT), magnetic resonance imaging, and gastroscope as the main examination means. Among them, CT scanning has the advantages of rapid imaging, high resolution, and can obtain rich information on human internal tissue, and is currently the most commonly used and most important imaging modality in the diagnosis of gastric cancer. However, at present, the CT-based TNM staging method of gastric cancer mainly depends on the evaluation of the imaging intensity and morphological characteristics of the primary lesion and potential metastasis sites of gastric cancer. There are problems such as relying on doctors' experience, lack of accurate diagnostic criteria that are uniform worldwide, insufficient accuracy, low repeatability, and time-consuming and labor-intensive. Therefore, it is an urgent need for clinical diagnosis and treatment of gastric cancer to develop new methods for predicting the TNM staging of gastric cancer, so as to provide doctors with stable, accurate, and reliable quantitative prediction results as reference indicators.
The radiomics based on medical imaging big data and artificial intelligence technology provides a new way to achieve the above goals. Radiomics can extract high-throughput lesion information that cannot be obtained by the human eye from medical images, transform tumor imaging phenotypes into high-dimensional features, deeply excavate the correlation between image features and key clinical tumor indicators, and then construct radiomic signatures and models with quantitative prediction ability. In previous studies, radiomics has been proved to have certain predictive value in clinical application tasks, such as histological diagnosis, classification and staging prediction, efficacy evaluation, and prognostic risk stratification in the field of oncology. However, at this stage, few radiomic models have been applied to clinical practice. This is mainly because the models still need to be further improved in terms of prediction accuracy, stability, interpretability, and applicability, and it also needs to be evaluated by experiments with a wider population, higher efficacy, and stricter control. Especially for TNM staging prediction of gastric cancer, the relevant research on radiomics is still in its infancy, and the previous studies mainly focused on analyzing the clinical significance of a few features on a small-scale single-center dataset.
Therefore, for the three clinical tasks of peritoneal metastasis (M stage), lymph node metastasis (N stage) and serosal invasion (T stage) prediction of gastric cancer, this thesis respectively developed new CT radiomic algorithms according to the inherent characteristics of each task and the current research difficulties, so as to improve the accuracy and stability of prediction and promote the application of radiomics in the clinical diagnosis and treatment of gastric cancer. We tested each prediction model on the multi-center datasets. The main work and contributions of this thesis are as follows:
1. Aiming at the problem of insufficient sensitivity of conventional CT subjective signs in diagnosing peritoneal metastasis of gastric cancer, inspired by the "seed-soil theory" of tumor metastasis, this thesis proposes an algorithm for predicting occult peritoneal metastasis of gastric cancer based on CT multi-regional radiomic features. Hypothesizing that the potential metastatic region of the peritoneum also contains early signs that can reflect the progression of gastric cancer, and guided by the clinical operability and interpretability of the model, we designed a multi-regional feature extraction framework for gastric cancer peritoneal metastasis, and proposed a radiomic signature construction algorithm to quantify the image representations of primary lesion and peritoneum simultaneously, and then established a prediction model of gastric cancer occult peritoneal metastasis. Compared with single-region radiomics, the proposed model integrated multi-regional information, which could more fully quantify the phenotype related to peritoneal metastasis and had higher prediction accuracy. Especially in the multi-center test, the model obtained an accuracy of over 85% for the patients with positive peritoneal metastasis but missed by the CT signs, indicating that it could greatly reduce the risk of unnecessary surgical treatment for patients with peritoneal metastasis. Due to the significant clinical application value of the algorithm, the published thesis based on this research has been included in CSCO guidelines for the diagnosis and treatment of gastric cancer for three consecutive years (2019-2021).
2. Given the urgent clinical demand for accurate diagnosis of lymph node metastasis of gastric cancer and the fact that perigastric lymph nodes are difficult to be directly evaluated due to their large number, wide distribution, and complex anatomical relationship, this thesis proposes a prediction algorithm of lymph node metastasis of gastric cancer based on CT deep radiomics features, to improve the quantification ability of radiomics for the phenotype of the primary lesion and the adaptability of feature extraction to the target task. Through the strategies of transfer learning and multi-stage training, the algorithm guided the deep learning network to learn the deep mapping relationship between images and clinical tasks while effectively reducing the risk of overfitting. In order to extract deep learning features, we used the regularization method and correlation analysis to screen non-redundant informative nodes and then conducted multiple measurements on the feature map output by the nodes to obtain high-throughput features significantly related to the task. Furthermore, we designed a multi-stage feature selection scheme to further improve the stability of the model on multi-center datasets. Finally, we constructed a radiomic model that can predict the specific N stage of gastric cancer and tested it on international multi-center datasets. Compared with the hand-crafted features, doctor diagnosis, and existing clinical indicators, the experimental results showed that the proposed prediction model based on CT deep radiomics features had a better prediction performance on lymph node metastasis of gastric cancer. Professor David Cunningham, director of clinical research at the Royal Marsden, wrote a commentary paper in the top journal of clinical oncology “Annals of Oncology”, commenting this work as "more accurately than clinical N stage and other clinical characteristics, and could be useful to determine the needed extent of lymphadenectomy".
3. Aiming at the key clinical issue of the lack of stable and accurate diagnostic methods for serous invasion of gastric cancer, and the key technical issue that the changes of CT imaging parameters have a great interference on the radiomics features in multi-center research, this thesis proposes a prediction algorithm for the serous invasion of gastric cancer based on CT harmonization radiomic features. The algorithm used a normalization-restoration strategy-based image harmonization (NRIH) framework to reduce the image variations caused by the changes of CT imaging parameters such as slice thickness, pixel size, image noise, and so on. In this framework, we used an interpolation algorithm to standardize the three-dimensional resolution of CT data from different centers and used a multi-attention mechanism-based image restoration network to enhance the key representation of regions of interest in CT images. Compared with other image preprocessing algorithms, NRIH framework had the characteristics of directly taking the image as the input and output, without collecting additional training samples with different imaging parameters, and only training a single network model. Based on NRIH framework, we constructed a gastric cancer serosal invasion prediction model by integrating unsupervised deep learning features and hand-crafted features. Experimental results showed that our NRIH framework could improve the generalization performance of the model and effectively avoid the loss of key image representation. The harmonization radiomic features were representative of tumor imaging phenotype and significant for the prediction of serosal invasion of gastric cancer. In the multi-center test, the prediction efficiency of our prediction model based on the CT harmonization radiomic feature is improved by more than 10.5% compared with the compared method.
|方梦捷. 基于CT影像组学的胃癌TNM分期预测算法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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