基于3D APTw影像组学模型预测脑胶质瘤IDH突变状态及WHO分级的研究

目的 探讨3D酰胺质子转移加权(APTw)影像组学模型在预测脑胶质瘤异柠檬酸脱氢酶(IDH)突变状态和WHO分级中的诊断价值。方法 回顾性分析2021年4月至2022年9月经手术病理证实的98例脑胶质瘤患者的临床资料及术前MRI图像。基于常规MRI平扫和增强图像对病灶的强化区、坏死区及瘤周水肿区进行手动分割,然后在原始3D APTw图像及衍生图像上进行特征提取。利用Least Absolute Shrinkage and Selection Operator(LASSO)模型选择方法,以预测脑胶质瘤IDH突变状态和WHO分级为目的,分别进行特征选择。再采用4种分类器构建预测脑胶质瘤IDH突变状态和WHO分级的影像组学模型,利用五折交叉验证训练并评估模型,最后通过受试者工作特征(ROC)曲线评估模型的效能。结果 采用XGBoost、Random Forest、Logistic Regression及Support Vector Machine 4种机器学习算法来预测脑胶质瘤IDH突变状态,以全瘤和瘤周水肿构建影像组学模型,其曲线下面积(AUC)值分别为0.778、0.800、0.797、...     

超声影像组学在肝脏病变的诊断应用进展

近年来,随着医学技术的成熟,影像组学得到快速的发展和广泛的应用,超声影像组学作为影像组学的一个分支,逐渐应用到肝癌、乳腺癌等领域,一些研究成果得到了临床医生的认可.在肝脏病变的研究中,超声诊断是一种重要的影像诊断方法,但存在一定的局限性,对良恶性的诊断特异性不如增强电子计算机断层扫描或磁共振成像.随着超声影像组学的引入及进展,为提高肝脏病变良恶性鉴别能力,肿瘤分期分级以及疾病的预后研究提供了新的方法和思路.     

人工智能和影像组学在免疫检查点抑制剂治疗非小细胞肺癌中的应用进展

肺癌是我国发病率和死亡率居首位的恶性肿瘤,非小细胞肺癌为其主要病理类型,免疫检查点抑制剂使非小细胞肺癌的治疗模式进入了新的阶段。随着人工智能和影像组学的发展,医学影像中所包含的高维度特征信息作为生物标志物为术前无创评估肿瘤免疫信息、评估肿瘤异质性、预测免疫治疗反应及预后、预测及鉴别不良反应等方面提供了更多可靠的信息,从而实现精准选择免疫治疗受众以改善患者预后。本文对上述人工智能和影像组学的研究现状作一综述。     

影像组学在良恶性肾肿瘤鉴别诊断、病理分级及治疗反应预测中的应用进展

影像组学利用人工智能将传统影像学图像进行数据化处理,以此获得更深层次的信息。影像组学在肿瘤的诊断、治疗及预后方面发挥了较大优势,促进了精准医疗的发展。影像组学可用于肾肿瘤良恶性的鉴别、预测病理分级和预测肾恶性肿瘤药物治疗效果。影像组学不仅在肾嗜酸细胞瘤和乏脂肪血管平滑肌脂肪瘤与肾细胞癌的鉴别有很好的区分准确度,而且能利用术前图像预测肾恶性肿瘤病理分级,以及利用影像组学评估对药物的治疗反应,这些对疾病的诊疗有重要意义。     

人工智能在肾癌诊疗中的运用及展望

人工智能现已逐步渗透入肾癌基因组学、转录组学、影像组学和病理组学等多方面的研究中,并在肾癌的诊疗领域取得了重要成果和初步运用.但是,现有的研究还存在着偏向于肾癌的诊断以及简单的临床预后预测,缺少对肾癌靶向治疗等疗效的预测,缺少多中心的临床验证研究等不足.未来仍需开展大规模、多中心研究进一步验证人工智能在肾癌诊疗领域中的...     

代谢组学及其在肿瘤生物学研究中的应用进展

本文主要阐明了代谢组学的概念,代谢与肿瘤的关系,介绍代谢组学的研究状况及研究技术,着重讲述了其在肿瘤生物学领域研究中的应用,归纳了代谢组学在肿瘤的早期诊断、治疗和预后评估中的最新应用进展.     

辅助技术在脑胶质瘤手术中应用进展

胶质瘤在我国发病率约12/105〔1〕,治疗以手术切除为主要手段。术中辅助技术不断发展与更新给术者提供极大帮助。目前,影像学诊断仍未能精确地做出病理分级,影像学方面难以与脑梗死、脑炎、脑白质病等相鉴别。准确的组织学诊断对指导术中切除范围及术后相关治疗具有重要意义。     

组学技术在旋毛虫研究中的应用进展北大核心CSCD

旋毛虫是一种典型的食源性人兽共患寄生线虫,呈全球分布且寄生无宿主特异性,给畜牧业养殖带来重大经济损失,且严重威胁食品安全。近年来,组学技术的应用为解析旋毛虫致病机制提供了有效的手段。本文主要综述了基因组学、转录组学、蛋白质组学等技术在旋毛虫研究中的应用及研究进展,以期为旋毛虫的生长发育、感染与宿主免疫等研究提供新思路,同时也为其他同类寄生虫研究提供参考。     

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.     

人工智能专家系统在呼吸系统疾病诊治中的应用进展

人工智能是电子计算机应用的重要方面,医学专家系统是人工智能在医学方面的重要应用分支。本文对人工智能专家系统的概念、结构、发展及其在呼吸病诊治中的应用和评价进行综述。     

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives

Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.     

Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.     

胼胝体区脑胶质瘤34例MRI诊断及手术病理对照研究

目的　探讨胼胝体区胶质瘤的MRI表现 ,提高对该类肿瘤的诊断和鉴别诊断能力。方法　对 34例胼胝体区胶质瘤行MR平扫及增强扫描 ,并与手术及病理结果做对照分析。结果　胼胝体胶质瘤表现为胼胝体长T1长T2 异常信号 ,信号强度均匀或不均匀 ,有明显占位效应 ,注射Gd DTPA后增强扫描 ,根据肿瘤病理类型的不同可出现明显强化、轻度强化或不强化。胼胝体区肿瘤侵及双侧或单侧脑叶时 ,可出现“蝴蝶征”或“半蝴蝶征” ,此二种征象是诊断胼胝体区胶质瘤的重要征象。结论　胼胝体区胶质瘤是颅内特殊部位的肿瘤 ,MRI对该类肿瘤的诊断和鉴别诊断具有重要临床价值     

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.     

肿瘤病理诊断进展

［摘要］　肿瘤病理精准诊断对指导临床治疗及判断预后至关重要,形态学是病理诊断的基石,免疫组化检测帮助病理诊断,而分子检测助力病理诊断及指导临床精准治疗。人工智能技术与病理形态学诊断不断融合,基于深度学习的人工智能使计算机自动处理病理形态并辅助精确诊断成为可能,进而使病理诊断逐渐走向智能化。该文就肿瘤病理诊断新进展进行阐述。     

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.     

人工智能在消化内镜中的临床应用现状

随着人工智能（artificial intelligence,AI）技术的不断发展,以大数据为支撑兼具强大计算能力和学习能力的AI技术已用于解决复杂的医学问题。利用AI分析大量非结构化医学数据,并执行临床任务,开始出现在胃肠镜检查中。即使与专业的内镜医师相比,AI技术也能够表现出优异的灵敏度和准确率,计算机辅助检查和计算机辅助诊断技术有望改变传统的内镜检查模式。本文就AI技术在消化系统内镜中的应用进行探讨。