Release date: 2016-04-27
President Dai Jianping's wonderful speech on "The Future of Medical Imaging" (Excerpt)
Future medical imaging will be an important part of precision medicine. The National Academy of Medical Sciences (IOM) issued a report in November 2011, emphasizing that precision medicine is a new approach to disease treatment and prevention based on understanding individual genes, environments, and lifestyles. We use life sciences to study this field, at the level of protein, genes, or at the molecular level, to explore how big, whether it can be seen, and what role it plays in proteins and genes.
Precision medicine is first of all aimed at individualization, there must be markers, but also must be paired, and at the same time need the support of evidence-based medicine, and then the processing of big data. Common areas of biomedicine, where we can see electrons, atoms, molecules, DNA, cells, bees, mice, and humans. The best resolution for PACS is 5 mm, CT and MRI are about 1600, or 0.1. -0.2 mm, the optical microscope is smaller, and the electron microscope is smaller. Today, the development of genes, gene sequencing DNA, optical and electronic observation of viruses.
Obviously our PACS gives us the storage and transmission of images, not including the bioimaging techniques and genetic matching commonly used in traditional medicine. That is to say, the data of genes and proteins is a prerequisite for PACS. The first paragraph of medical development is to do whether there is any disease, what disease is done in the latter stage, where it is; where the image is to be solved, so PACS It is worth paying attention to in the processing of images. We pay attention to gene sequences, gene sequencing, proteins, etc., and can observe and contrast under molecular imaging.
In general, medical imaging has been developed for 120 years. Since the 1970s, for more than 40 years, we mainly refer to digital images. Digital images give PACS a good opportunity, so our transmission can be so smooth. In terms of medical digital imaging, the main problems solved are spatial resolution, contrast resolution, temporal resolution and energy resolution. For the surrounding image, PACS is to solve these four resolutions.
From a clinical point of view, PACS mainly solves three problems, the first tumor, the second cardiovascular and cerebrovascular disease, and the third dementia. Inquiring about the US NIS and the domestic research fund are basically based on these three, PACS is also evolving with the mainstream of the disease. The hardest thing to ask us here is the time resolution and energy resolution, which is dynamic. Furthermore, with the functions and functions of metabolism, pop, and quantification, a new requirement is put forward for PACS. As for big data, the main thing is to look at the metabolism and quantification of metabolism, especially quantification. Although the 120-year development of imaging has solved the problem of four resolutions, it is an indirect tool based on morphology, and it cannot be diagnosed at the level of histology and molecular science. This is our current medical image. Knowing where the disease is, PACS solves the problem of where the disease is, rather than solving the problem of what is the disease.
In the medical imaging of the biomedical era, we are faced with three directions: the first point is individualization. Our high-grade hospitals can transfer films from grassroots hospitals, but they are only a line. They do not form a network and cannot be deployed to each other. Small hospitals cannot get films from tertiary hospitals, so this is a challenge for PACS. We must be truly individualized. First of all, we must change our medical model, from family to hospital to family, circle, diagnosis-treatment-study, both of which are in the family, just as PACS is not distributed at both ends. Then there is no way to evoke social participation and family participation. Similarly, we are very concerned about the construction of the network, but what we actually accomplish is a line, which is adjusted below. The following cannot be adjusted. It is not shuttled, let alone the deployment of the entire network. This aspect is a barrier. . And our standardization has yet to be further developed.
Second, PACS should pay attention to the integration of imaging diagnosis, that is, intergrate, which is the integration of laboratory, pathology, image synthesis or molecular laboratory. We do not have the ability to solve this problem by PACS or TMT. . So PACS and TMT are a tool for integrating medical imaging in the future. In the past, we used the information center to integrate information. Now we can call the integrated information center as intergrate information. We integrate everything together. But we still build a center on the traditional way of thinking, so this aspect needs us to consider.
The third point, from the perspective of precision medicine, the analogy of the military in the 18th and 19th centuries was generally solved by duel. Nowadays, mainly relying on missiles, then GPS is needed for positioning. Then the positioning in the medical field is done by PACS. PACS can solve this positioning problem, and the future TMT technology gives a great support to precision medicine, which is what I will introduce below. As for the reason, I will explain it. Everyone knows that this is a normal gene. If the gene is mutated, you can say that there is a disease. Some of the mutated genes are recovered by their own immune system, and the other part cannot be recovered. We put this part in Cancer was previously called a cancerous phase, or a subtle cancer molecule or cancer cell. After a sudden proliferation, you can see that from cancer to death is 1-3 years, we see images from the stage of cancer to death, the scope of genetic testing and protein testing is invisible. Taking the example of Hollywood actress Angelina Jolie, the doctor told her that 80% of her genes are likely to have breast cancer. She chose to remove the breast, which is better solved in the breast. However, if it is in the neurology department, a pair of genes may cause disease, which may lead to glioma, then it can not be solved as well as the breast, there is no way to remove it. In this case, the bigger part of us is doing the accumulation and comparison of genetic and protein data. This work and the storage of data need us to do it. We have to jump out of the simple image. Adapt to current needs. Of course, we still need to know how big the tumor is, and we must strengthen the processing of the data. If the genetic abnormality is found in breast cancer screening, the abnormal 100 people will be gathered together for examination. It may be found that 20 of them are sick, but if there is no genetic comparison, it is simply how many years old to many years old. It may not be ideal to get together for a diagnosis. Therefore, the integration of information is very important.
Taking gliomas as an example, the classification of gliomas is currently based on the classification of WHO in 2003. In June of this year, we used new molecular classifications to classify, and we can use big data to transfer past images. Then we observe its characteristics and whether this feature has a certain relationship with molecular typing. So PACS can not put all the data, all the features like the computer early auxiliary diagnosis, can be transferred early; can you simultaneously transfer the MR or CT images, this one for the software in the PACS Claim.
For today, we want to develop drugs, to treat diseases, we don't see where the lesions are, but we know that it already exists, so we can take this part out, and then we suspect what it is, we Then hit in and let it mark the lesions. Even with the treatment of drugs to diagnose and treat, that is, targeted therapy, this part has no medical imaging, but the medical image tells us where the disease is, and then treated with drugs. This process also requires big data, and there is a specification. This is a passion. In fact, this passion is the formation of drugs, data and norms, which is a very important challenge for the future PACS.
Text / Ma Shuai finishing WeChat (mobile phone)
Master of Medicine, Shanghai Jiaotong University, pharmacy analyst of GF Securities, has rich pharmaceutical industry resources, joined GF Securities in 2015. New wealth, crystal ball, core member of the Golden Bull Awards research team.
Source: Shell Society
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