whole-body PET ？全人研究的催化剂？

转自微信公众号：机器学习炼丹术
笔记：陈亦新

记录英文的关键原话，需要背下来有一些。作为专业英语储备和背景知识储备。

Introduction

Whole-body (WB) and total-body (TB) PET imaging systems, in particular, can serve as exploration tools for the scientific community. They allow the study of inter-organ interactions with their unique ability to visualise multiple organs simultaneously operating at different time scales.

living organisms maintain "homeostasis" through dynamic multi-organ systemic interactions.

活得生物通过动态的多器官系统交互来实现体内平衡。

A considerable amount of enermy is needed to fuel these interactions to promptly orchestrate multiple organs to respond to perturbations ('allostatic load"). For example, inflammation in response to infection or tissue damage is a critical survival mechanism to return to the original homeostatic state.

大量的能量被需要作为燃料给这些交互，来快速的编排多个组织应对扰动（同种异体负荷）.例如，对感染或组织损伤的反应炎症是恢复到原始稳态的关键生存机制。

PS:同种异体负荷是身体及其调节机制在应对慢性、无缓冲压力时经历的“磨损”。可通过同种异体负荷影响的系统包括神经内分泌、心血管、免疫和代谢系统。

These pathologies can, in theory, be characterised by deviations in parameters that describe a normative multi-organ network and that extend beyond their usual range.

不理解。deviations指的是什么？

Molecular imaging modalities, such as Positron Emission Tomography PET, can provide valuable insights into the underlying homeostasis of living subjects using target-specific radiotracer imaging.

如PET一样的分子成像技术，可以使用靶标特定的放射性示踪剂对体内内在稳态提供宝贵的视角。

With the introduction of a "Whole-body" acquisition mode, that is the successive translation of the subject through the axial FOV of a PET system with slightly overlapping bed positions, the identification of hypermetabolic tumor lesions in oncology patients became the primary application of PET.

随着“全身”采集模式的引入，即通过床位略微重叠的PET系统的轴向FOV连续平移受试者，肿瘤患者代谢亢进肿瘤病变的识别成为PET的主要应用。

Such a reductionist "lumpology" approach, however, caused a wealth of molecular information available from PET to be overlooked and discarded the concept of humar physiology imaging.

然而，这种还原论的“肿块学”方法导致PET提供的大量分子信息被忽视，并抛弃了Humar生理学成像的概念。就是说，只用PET来看肿瘤，虽然这是PET的商业价值，但是PET不仅仅可以干这些。我们不应该从肿块学的还原角度出发，我们可以利用PET来考虑更多的人生理学成像的信息。、

The recent extension of the WB-PET concept to imaging extended axial imaging ranges with larger FOV systems, colloquially refered to as a total-body PET (TB-PET) has sparked interest in the PET community to conduct multi-organ systemic investigations.

最近将WB-PET概念扩展到具有更大FOV系统的成像扩展轴向成像范围，俗称全身PET（TB-PET），这引发了PET界对进行多器官系统研究的兴趣。WB和TB什么区别呢？

TB-PET systems cover axial scan ranges of 1m to 2m, which allows the synchronous measurement of signals from multiple organs.

TB-PET系统覆盖1m至2m的轴向扫描范围，允许同步测量来自多个器官的信号。可以推测，WB可能是多床位的一种扫描，只能构建静态PET无法短时间构建动态PET图像。换句话说，TB的detector的axial length更长，WB的可能很短

In addition, the richness of the multi-organ data derived from WB-pET notwithstanding, TB-PET is particularly unique as it satisties two critical criteria for such causal investigations: the simultaneous acquisition of signals from multiple investigated distant organs and a high temporal resolution across the FOV.

此外，尽管来自WB-PET的多器官数据丰富，但TB-PET特别独特，因为它满足此类因果调查的两个关键标准：同时从多个被调查的远处器官获取信号和整个视场的高时间分辨率。这句话也是强调，一个axial方向很长的detector的好处，就是快。不需要切换多个床位

The combination of increased sensitivity and sub-second temporal sampling provided by TB-PET could potentially aid in probing real-time multi-organ interactions.

TB-PET提供的灵敏度提高和亚秒级时间采样的结合可能有助于探测实时多器官相互作用。

Multi-organ analysis with standard WB-PET

Traditional WB-PET with an axial FOV of ~20cm can already be used for multi-organ analysiss. For example, simple inter-group comparisons of organ-based standardised uptake values (SUV) can provide curcial information regarding the underlying pathology.

轴向视场为~20cm的传统WB-PET已经可用于多器官分析。例如，基于器官的标准化摄取值（SUV）的简单组间比较可以提供有关潜在病理学的曲线信息。

A recent study demonstrated that in a patient cohort with resected breast cancer, a high metabolic tumour volume and increased spleen glucose metabolism on baseline were associated with poor 5-y recurrence-free survival.

最近的一项研究表明，在切除乳腺癌的患者队列中，高代谢肿瘤体积和基线时脾脏葡萄糖代谢增加与 5 年无复发生存率差有关。【14】

The bespoke study hints toward a possible interaction between the tumour and the host immune system through the upregulation of hematopoiesis.

这项定制的研究暗示了肿瘤与宿主免疫系统之间通过造血上调可能存在的相互作用。

Diseases formerly conveived as focal, such as myocardial infarction, have distributed effects throughout the body that are mediated through disease-specific netwokrs.

以前作为局灶性疾病，如心肌梗塞，具有通过疾病特异性网络介导的全身效应。对于disease-specific networks的理解并不清楚，这句话似乎是说，以前的被认为是focal disease，也就是局部的疾病，这种疾病其实有着通过某种方式影响全身的效应.这个参考文献值得阅读3S【15】

PS：局灶性，描述脑波的分布方式。局限在某一个局部的特殊脑波活动。

And finally, mental and societal stress triggers have been linked to various diseases associated with chronic inflammation that can be assessed already by WE-PET.

最后，精神和社会压力触发因素与与慢性炎症相关的各种疾病有关，WE-PET已经可以对其进行评估。这个也值得看，评级2S

Inter-organ networks through PET

Current multi-organ investigations using WB-PET are mostly fishing expeditions, aiming to pinpoint stable correlations between organs. 【10，11】

目前使用WB-PET的多器官研究主要是远征，旨在确定器官之间的稳定相关性。

In general, correlation analyses explore gross systemic effects between two groups without causal explanation. When performing correlation analyses, the chosen sample should represent the investigated population (e.g., healthy or pathological)

一般来说，相关性分析探索两组之间的总体系统效应，而没有因果解释。在进行相关性分析时，所选样本应代表所调查的人群（例如，健康或病理）

Other factors, such as variability, linearity, and variance of the samples must also be considered. Since most multi-organ correlation network studies seek to pinpoint monotonic relationships between investigated organs, Spearman correlation should be chosen over Pearson correlation, as it is non-parametric and insensitive to the linearity and homogeneity of the variance of observed data.

还必须考虑其他因素，例如样本的变异性、线性和方差。由于大多数多器官相关网络研究都试图查明所研究器官之间的单调关系，因此应选择Spearman相关性而不是皮尔逊相关性，因为它是非参数的，并且对观测数据方差的线性和均匀性不敏感。

The ultimate goal of inter-organ analysis is to identify causal relationships between organs that can facilitate the development of impactful interventions in medicine.

器官间分析的最终目标是确定器官之间的因果关系，以促进医学中有影响力的干预措施的发展。

Here, structure learning of Bayesian networks (18) in combination with graph models as visual representations of causal links in complex processes can be an attractive approach(19), which, however, still mandates the integration of a clinical expert to denounce spurious causal links.

在这里，贝叶斯网络的结构学习（18）与图模型相结合，作为复杂过程中因果关系的可视化表示可能是一种有吸引力的方法（19），然而，它仍然要求整合临床专家来谴责虚假的因果关系。

Both causal and correlation networks should be considered hypothesis-generating tools rather than tools that provide solid endpoint.

因果网络和相关网络都应被视为假设生成工具，而不是提供可靠终点的工具。

Such hypotheses must be proven or disproven in rigorous validation studyies, whereby investigators should be conscious of the confounders affecting the accuracy of standardised uptake values (SUV) or kinetic parameters as part of a multi-organ analysis.[20]

这些假设必须在严格的验证研究中得到证明或反驳，研究人员应该意识到影响标准化摄取值（SUV）或动力学参数准确性的混杂因素，作为多器官分析的一部分。

The promise of TB-PET

Despite the increasing installed base of TB-PET systems, the number of studies that explore TB-PET beyond dose reduction and higher throughput for the sake of assessing the human connectome studies is limited.

尽管TB-PET系统的安装基础不断增加，但为了评估人类连接体研究而探索TB-PET，而不是剂量减少和更高通量，的研究数量有限。

Preliminary studies have demonstrated the potential of using the temporal domain, namely raw time-activity curves, to derive metabolic associations between different bone compartments (21), or to construct normative networks for healthy male and female controls (22).

初步研究表明，使用时间域（即原始时间活动曲线）来推导出不同骨区间之间的代谢关联（21），或为健康的男性和女性对照构建规范网络（22）的潜力。

Although neither study explained causality, dynamic TB-PET has the potential to create personalised causal networks from a single subject.

虽然这两项研究都没有解释因果关系，但动态TB-PET有可能从单个受试者创建个性化的因果网络。

Such a paradigm requires, however, the subject to be challenged by a task, pharmacological intervention, or external stressor (e.g., pain, cold). By challenging (pertubing) the system, simultaneous or delayed changes in signals from different organs can be measured and used to establish causality.

然而，这种范式要求受试者受到任务、药物干预或外部压力源（例如疼痛、寒冷）的挑战。通过挑战（插管）系统，可以测量来自不同器官的信号的同时或延迟变化，并用于建立因果关系。

For decades, such studies have been performed with functional MRI to derive effectivity connectivity by conducting baseline and task paradigms in a single imaging session (23).

几十年来，此类研究一直使用功能性MRI进行，通过在单个成像会话中进行基线和任务范式来获得有效的连接性（23）。

Recent innovative brain studies in functional PET have shown the possibility of using 18F-FDG PET to study dynamic changes in glucose metabolism within a single session with the aid of constant infusion protocols (24). However, conducting such challenge-based studies is non-trivial in a TB-PET setting, particularly in view of unknown response times and downstream interactions. Therefore, test studies on well-understood paradigms (25) should be performed before conducting exploratory connectome investigations using TB-PET.

最近在功能性PET方面的大脑研究表明，在恒定输注方案的帮助下，使用18F-FDG PET在单次会话中研究葡萄糖代谢的动态变化的可能性（24）。然而，在TB-PET环境中进行这种基于挑战的研究并非易事，特别是考虑到未知的响应时间和下游相互作用。因此，在使用TB-PET进行探索性连接体研究之前，应先对易于理解的范式（25）进行测试研究。对于未知的响应时间和下游相互作用不太理解，这篇文献应该需要阅读很重要

Roadmap to the future: Connect to the connectome

To date, the PET imaging community is fragmented by vendor, geography and skillset. There needs to be more meaningful sharing of code, data and expertise to address the novel challenges and opportunities that arise with this technology. To fully leverage the potential of WB- and TB-PET alike for healthcare, new analysis methods are required, and new skills in the workforce are needed (Fig. 2). Automated data analytics pipelines, including automatic whole-body semantic segmentation (26) as well as WB- and TB-PET motion correction and spatial normalisation are prerequisites to robust TB-PET connectome studies.

迄今为止，PET 成像社区因供应商、地理位置和技能而分散。需要更有意义的代码、数据和专业知识共享，以应对这项技术带来的新挑战和机遇。为了充分利用WB和TB-PET在医疗保健方面的潜力，需要新的分析方法，并且需要新的劳动力技能（图2）。自动化数据分析管道，包括自动全身语义分割（26）以及WB和TB-PET运动校正和空间归一化，是稳健的TB-PET连接组研究的先决条件。运动矫正、空间归一化、分割、等等