腹腔镜胃癌根治术的研究进展

胃癌是全球发病率较高的恶性肿瘤之一,且发病率呈逐年上升的趋势。传统的治疗方法是开腹胃癌根治术,但该方法对患者机体造成的创伤较大,不利于患者术后恢复,在一定程度上影响了手术治疗的效果。随着医学技术的发展及"微创外科"理念的不断深入,腹腔镜手术以其创伤小、术中出血量少以及术后恢复快等特点被广泛应用于外科手术治疗中。近年来,3D腹腔镜技术的出现使手术视野更加清晰,术中操作更加简便,在一定程度上提高了手术的安全性,但临床对于进展期胃癌根治术的远期疗效一直存在争议。因此,本文对腹腔镜在胃癌根治术中的作用及意义作以综述,为胃癌的微创治疗提供理论参考。     

完整胃系膜切除治疗胃癌的研究进展

目前胃癌的主要治疗方式仍是手术治疗,标准D2根治术已得到推广,但胃癌术后的局部复发仍是导致患者远期预后不佳的重要因素。早期胃癌患者的检出率低和手术淋巴结清扫的不规范及胃周软组织切除的不彻底是导致胃癌患者局部复发的重要因素。全直肠系膜切除(TME)和完整结肠系膜切除(CME)对降低结直肠癌术后局部复发效果明显,相同进展程度下远期预后明显好于胃癌。近年提出的完整胃系膜切除治疗胃癌可能会降低胃癌术后局部复发,改善患者预后,规范了完成胃癌根治术的完整流程标准,对于胃癌手术的规范化实施达到整块切除具有指导意义,随着微创理念不断的深入,腹腔镜的应用与发展使我们对系膜的认识更加深入,我们对完整胃系膜切除治疗胃癌的现状及研究进展进行综述如下。     

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.     

Radiomics in radiooncology – Challenging the medical physicist

PurposeNoticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions.MethodsBased on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach.ResultsClinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data (‘panomics’) challenges the medical physicist as member of the radiooncology team.ConclusionsThe new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.     

Mouse models in neurological disorders: Applications of non-invasive imaging

Neuroimaging techniques represent powerful tools to assess disease-specific cellular, biochemical and molecular processes non-invasively in vivo. Besides providing precise anatomical localisation and quantification, the most exciting advantage of non-invasive imaging techniques is the opportunity to investigate the spatial and temporal dynamics of disease-specific functional and molecular events longitudinally in intact living organisms, so called molecular imaging (MI). Combining neuroimaging technologies with in vivo models of neurological disorders provides unique opportunities to understand the aetiology and pathophysiology of human neurological disorders. In this way, neuroimaging in mouse models of neurological disorders not only can be used for phenotyping specific diseases and monitoring disease progression but also plays an essential role in the development and evaluation of disease-specific treatment approaches. In this way MI is a key technology in translational research, helping to design improved disease models as well as experimental treatment protocols that may afterwards be implemented into clinical routine. The most widely used imaging modalities in animal models to assess in vivo anatomical, functional and molecular events are positron emission tomography (PET), magnetic resonance imaging (MRI) and optical imaging (OI). Here, we review the application of neuroimaging in mouse models of neurodegeneration (Parkinson's disease, PD, and Alzheimer's disease, AD) and brain cancer (glioma).     

Application of radiomics and machine learning in head and neck cancers

With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.     

Médicaments radiopharmaceutiques : du diagnostic au théranostique des tumeurs neuroendocrines

Neuroendocrine tumors (NET) are a heterogeneous group originating from endocrine cells, which have the ability to develop themselves on various organs. Most of NET are well differentiated and have the capacity to produce different hormones and biogenic amines. NETs usually appear sporadically and can also be associated with different syndromes (multiple endocrine neoplasia). For the majority of NETs, surgical resection is the treatment of choice requiring the precise location of the tumor before surgery as well as the determination of the stage, followed by monitoring the progression of the disease. In the diagnostic process, nuclear medicine with molecular imaging plays a fundamental role. The secretory functions of these tumors enable the use of molecular imaging by targeting specific metabolic pathways or receptors. In addition, nuclear medicine also plays an important role in the field of therapy by replacing in one radiopharmaceutical drugs, the imaging suited radionuclide by replacing with a radionuclide emitting radiation suitable for therapy, also called vectorized internal radiotherapy. The activity of nuclear medicine, which enables diagnosis and treatment to be carried out using the same structures specific to the molecular targets of neuroendocrine tumors, is fully integrated into the new theragnostic approach, and constitutes one of its main pillars. The objective of this work is to describe the molecular targets expressed by NETs and corresponding radiopharmaceuticals, validated for human use (diagnosis and therapy).     

基于深度学习的作物基因组学和遗传改良

随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。     

利用人工智能预测癌症的易感性、复发性和生存期

癌症具有较高的发病率和致死率,对人类健康具有重大威胁。癌症预后分析可以有效避免过度治疗及医疗资源的浪费,为医务人员及家属进行医疗决策提供科学依据,已成为癌症研究的必要条件。随着近年来人工智能技术的迅速发展,对癌症患者的预后情况进行自动化分析成为可能。此外,随着医疗信息化的发展,智慧医疗的理念受到广泛关注。癌症患者作为智慧医疗的重要组成部分,对其进行有效的智能预后分析十分必要。本文综述现有基于机器学习的癌症预后方法。首先,对机器学习与癌症预后进行概述,介绍癌症预后及相关的机器学习方法,分析机器学习在癌症预后中的应用;然后,对基于机器学习的癌症预后方法进行归纳,包括癌症易感性预测、癌症复发性预测、癌症生存期预测,梳理了它们的研究现状、涉及到的癌症类型与数据集、用到的机器学习方法及预后性能、特点、优势与不足;最后,对癌症预后方法进行总结与展望。     

综合医院大数据应用需求调查与分析

目的 分析综合医院对于大数据应用的内在需求,为医院的大数据研发与应用提供导向和依据。方法 采用德尔菲法自制医院大数据应用需求调查问卷,随机抽取中国研究型医院学会医疗分会64家会员单位进行调查,获得有效问卷104份,有效回收率为94.55%。结果 精准医疗（4.31±0.42）分,精益管理（4.23±0.56）分,科学研究（4.19±0.52）分,健康管理（4.16±0.52）分,数字医疗（4.06±0.60）分,教育培训（3.69±0.69）分。不同性别、年龄、职称、岗位组间的需求差异有统计学意义（P＜0.05）。多元线性回归分析结果显示,医学人工智能（b=0.324,P=0.000）和互联网+医疗（b=0.161,P=0.047）的需求程度会对医院大数据应用前景态度产生显著的正向影响关系。结论 综合性医院对大数据具有较强的、多样化的应用需求,应以实际需求为导向,重点推进精准医疗、医学人工智能和互联网+医疗等相关应用的研发。     

应用siRNA干扰胃癌的研究进展

胃癌是消化系统最常见的恶性肿瘤,而我国是胃癌高发区,其发病率和死亡率均高于世界平均水平。在我国大多数患者明确诊断时已进入进展期,所以大多数患者再行手术切除后还需放化疗治疗。近来随着对胃癌的研究深入,可通过对胃癌肿瘤标本行分子检测给予患者药物靶向治疗,实现个体化治疗。RNA干涉(RNAi)技术被广泛用于基因功能的研究,并且在哺乳动物研究中得到飞速发展.si RNA是在RNAi中起中心作用,其可抑制特定m RNA,以调节不同蛋白在肿瘤发生时的异常表达。对si RNA的研究将为胃癌的基因治疗供更广阔的空间。     

子宫内膜癌手术治疗的研究进展

子宫内膜癌是妇科常见恶性肿瘤之一,手术是其主要的治疗方式。尽管开腹手术是治疗子宫内膜癌的传统方式,但随着医学科技的不断发展以及人们对术后生活质量要求的提高,妇科肿瘤的外科治疗方式也随之发生了革命性的变化。从传统开腹手术、腹腔镜手术、单孔腔镜技术,到2005年美国FDA批准应用于妇科手术的达芬奇机器人手术系统,子宫内膜癌的手术治疗方式也有了更多的选择。与传统开腹手术相比,微创手术凭借其创伤小、恢复快等优点,在子宫内膜癌的应用越来越广泛,但临床应用时间较短,仍需大样本多中心的长期随访研究来证实其安全性和有效性。本文主要围绕以上几种手术方式治疗子宫内膜癌的最新观点及研究进展进行综述。     

射频消融术治疗肝癌的研究新进展

肝癌为我国常见的恶性肿瘤之一。到目前为止,肝癌的治疗方法中,手术治疗仍为肝癌患者能获得较好生存率的首选方法。但由于很多患者发现肝癌时,晚期患者较多,很多肝功能较差剩余肝组织不能代偿,或全身情况较差,已不适合手术治疗。基于此种情况,现很多非手术治疗方法广泛应用于临床。而射频消融术治疗肝癌,作为一种非手术治疗方法,有着微创,疗效好,并发症少,安全,可反复应用等优点,近年来已成为治疗肝癌的一种常用手段。射频消融术治疗肝癌可分为开腹射频,腔镜下射频,影像学引导下经皮射频等。治疗方式可单独射频治疗,也可与介入治疗,酒精注射,静脉全身化疗等联合应用。现从射频消融术治疗肝癌的原理,适应症,方式,并发症,及预后几方面回顾总结该技术。     

Pancreatic Cancer Surgical Resection Margins: Molecular Assessment by Mass Spectrometry Imaging

BackgroundSurgical resection with microscopically negative margins remains the main curative option for pancreatic cancer; however, in practice intraoperative delineation of resection margins is challenging. Ambient mass spectrometry imaging has emerged as a powerful technique for chemical imaging and real-time diagnosis of tissue samples. We applied an approach combining desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with the least absolute shrinkage and selection operator (Lasso) statistical method to diagnose pancreatic tissue sections and prospectively evaluate surgical resection margins from pancreatic cancer surgery.ConclusionsOur findings provide evidence that the molecular information obtained by DESI-MSI/Lasso from pancreatic tissue samples has the potential to transform the evaluation of surgical specimens. With further development, we believe the described methodology could be routinely used for intraoperative surgical margin assessment of pancreatic cancer.     

How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.     

探究新辅助化疗对胃癌细胞Bcl-2和p53基因表达的影响

胃癌在中国的发病率和死亡率居恶性肿瘤前列,现在胃癌的治疗主要以手术和化疗为主的综合治疗,新辅助化疗是胃癌综合治疗的重要组成部分,通过新辅助化疗能够有效抑制癌细胞增殖、缩小肿瘤体积等优点,从而为手术切除创造条件。本研究用新辅助化疗处理患胃癌的小鼠,并检测了新辅助化疗处理前后胃癌细胞内p53和Bcl-2 (细胞凋亡相关因子)基因在组织内的表达变化情况,以及与对照相比新辅助化疗对肿瘤大小的影响。结果表明,新辅助化疗可以减缓肿瘤的增长,显著上调小鼠胃癌组织内细胞凋亡因子p53的表达,并且显著下调Bcl-2抗凋亡因子的表达,从而有效地抑制胃癌细胞的增殖。这一结果可能为新辅助化疗对胃癌的治疗分子机制提供一些理论支持。     

基于精神影像和人工智能的抑郁症客观生物学标志物研究进展

抑郁症是当今社会上造成首要危害且病因和病理机制最为复杂的精神疾病之一,寻找抑郁症的客观生物学标志物一直是精神医学研究和临床实践的重点和难点,而结合人工智能技术的磁共振影像(magnetic resonance imaging,MRI)技术被认为是目前抑郁症等精神疾病中最有可能率先取得突破进展的客观生物学标志物.然而,当前基于精神影像学的潜在抑郁症客观生物学标志物还未得到一致结论 .本文从精神影像学和以机器学习(machine learning,ML)与深度学习(deep learning, DL)等为代表的人工智能技术相结合的角度,首次从疾病诊断、预防和治疗等三大临床实践环节对抑郁症辅助诊疗的相关研究进行归纳分析,我们发现:a.具有诊断价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、脑岛、丘脑以及海马等;b.具有预防价值的脑区主要集中在楔前叶、中央后回、背外侧前额叶、眶额叶、颞中回等;c.具有预测治疗反应价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、额中回、枕中回、枕下回、舌回等.未来的研究可以通过多中心协作和数据变换提高样本量,同时将多元化的非影像学数据应用于数据挖掘,这将有利于提高人工智能模型的辅助分类能力,为探寻抑郁症的精神影像学客观生物学标志物及其临床应用提供科学证据和参考依据.     

Implementing biological markers as a tool to guide clinical care of patients with pancreatic cancer

A major obstacle for the effective treatment of pancreatic ductal adenocarcinoma (PDAC) is its molecular heterogeneity, reflected by the diverse clinical outcomes and responses to therapies that occur. The tumors of patients with PDAC must therefore be closely examined and classified before treatment initiation in order to predict the natural evolution of the disease and the response to therapy. To stratify patients, it is absolutely necessary to identify biological markers that are highly specific and reproducible, and easily measurable by inexpensive sensitive techniques. Several promising strategies to find biomarkers are already available or under development, such as the use of liquid biopsies to detect circulating tumor cells, circulating free DNA, methylated DNA, circulating RNA, and exosomes and extracellular vesicles, as well as immunological markers and molecular markers. Such biomarkers are capable of classifying patients with PDAC and predicting their therapeutic sensitivity. Interestingly, developing chemograms using primary cell lines or organoids and analyzing the resulting high-throughput data via artificial intelligence would be highly beneficial to patients. How can exploiting these biomarkers benefit patients with resectable, borderline resectable, locally advanced, and metastatic PDAC? In fact, the utility of these biomarkers depends on the patient''s clinical situation. At the early stages of the disease, the clinician''s priority lies in rapid diagnosis, so that the patient receives surgery without delay; at advanced disease stages, where therapeutic possibilities are severely limited, the priority is to determine the PDAC tumor subtype so as to estimate the clinical outcome and select a suitable effective treatment.     

基于机器学习的药物-靶标相互作用预测*

近年来,随着计算机硬件、软件工具和数据丰度的不断突破,以机器学习为代表的人工智能技术在生物、基础医学和药学等领域的应用不断拓展和融合,极大地推动了这些领域的发展,尤其是药物研发领域的变革。其中,药物-靶标相互作用(drug-target interactions, DTI)的识别是药物研发领域中的重要难题和人工智能技术交叉融合的热门方向,研究人员在DTI预测方面做了大量的工作,构建了许多重要的数据库,开发或拓展了各类机器学习算法和工具软件。对基于机器学习的DTI预测的基本流程进行了介绍,并对利用机器学习预测DTI的研究进行了回顾,同时对不同的机器学习方法运用于DTI预测的优缺点进行了简单总结,以期对开发更加有效的预测算法和DTI预测的发展提供帮助。     

Immunochemical and molecular-Genetic markers in diagnostics of gastric cancer

Intensive studies of molecular mechanisms responsible for tumor transformation results in identification of new proteins and their genes involved into tumor development. These proteins may be used as markers of tumor transformation of cells and the level of their expression may be evaluated by means of modern highly sensitive and technological methods of analysis. This review summarized literature data on currently used immunohistochemical and molecular genetic markers of gastric cancer. It highlights genetic and epigenetic changes detected in nucleic acids of tumor tissue cells in malignant and benign gastric diseases as well as in the level of DNA circulating in blood of patients with gastric cancer.