本文介绍了Phenomics期刊2023年第六期收录文章合集，文章概览如下，请查收！

（Phenomics期刊2023年第六期封面图）

01

 Visualizing Macrophage Phenotypes and Polarization in Diseases: From Biomarkers to Molecular Probes

体内巨噬细胞的表型及其相应的刺激物

DOI：

https://doi.org/10.1007/s43657-023-00129-7

引用格式：

Ni, D., Zhou, H., Wang, P. et al. Visualizing Macrophage Phenotypes and Polarization in Diseases: From Biomarkers to Molecular Probes. Phenomics 3, 613–638 (2023). https://doi.org/10.1007/s43657-023-00129-7

这篇综述旨在通过全面概述体内巨噬细胞表型可视化的各种方法来填补体内巨噬细胞表型的成像策略的空白。首先，该研究介绍了区分巨噬细胞表型的潜在生物标记物，并从受体、代谢物和线粒体特性等方面将其分为三类。每一类都为了解巨噬细胞亚型的独特特征提供了有价值的见解。接下来，该研究介绍了用于观察不同表型巨噬细胞的相应成像探针。这些成像探针是准确识别和监测体内巨噬细胞极化状态的重要工具。最后，该研究讨论了对体内巨噬细胞表型进行精确成像所面临的挑战和前景。

Abstract

Macrophage is a kind of immune cell and performs multiple functions including pathogen phagocytosis, antigen presentation and tissue remodeling. To fulfill their functionally distinct roles, macrophages undergo polarization towards a spectrum of phenotypes, particularly the classically activated (M1) and alternatively activated (M2) subtypes. However, the binary M1/M2 phenotype fails to capture the complexity of macrophages subpopulations in vivo. Hence, it is crucial to employ spatiotemporal imaging techniques to visualize macrophage phenotypes and polarization, enabling the monitoring of disease progression and assessment of therapeutic responses to drug candidates. This review begins by discussing the origin, function and diversity of macrophage under physiological and pathological conditions. Subsequently, we summarize the identified macrophage phenotypes and their specific biomarkers. In addition, we present the imaging probes locating the lesions by visualizing macrophages with specific phenotype in vivo. Finally, we discuss the challenges and prospects associated with monitoring immune microenvironment and disease progression through imaging of macrophage phenotypes.

02

Phenomic Imaging

表型组成像研究的过程包括三个主要步骤：图像分析、整合来自其他表型组的数据以及数据分析

DOI：

https://doi.org/10.1007/s43657-023-00128-8

引用格式：

Lan, L., Feng, K., Wu, Y. et al. Phenomic Imaging. Phenomics 3, 597–612 (2023). https://doi.org/10.1007/s43657-023-00128-8

该研究提出了“表型影像学”这一新概念，系统介绍了表型影像学所涵盖的多种成像模态及其在表型组学中的应用，深入探讨了表型影像学与其他组学的相互关联、交叉与联系，为表型组学和精准医学的未来发展提供了新的思路、方法和技术支撑。

Abstract

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. “Phenomic imaging” utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

03

  Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review

核心脏病学的常规工作流程

DOI：

https://doi.org/10.1007/s43657-023-00137-7

引用格式：

Li, J., Yang, G. & Zhang, L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. Phenomics 3, 586–596 (2023). https://doi.org/10.1007/s43657-023-00137-7

该综述旨在介绍人工智能在核心脏病学中应用的最新进展。该研究以 2013 年 1 月 1 日至今的 "PubMed"、"Embase "和 "Ovid Technologies "数据库为基础，针对三个主要检索词设计了检索策略："人工智能"、"心血管疾病 "和 "正电子发射计算机断层扫描/单光子发射计算机断层扫描"。首先简要介绍了人工智能的基本知识，然后主要关注人工智能在核心脏病学中的应用，包括成像方案、数据处理、心血管疾病诊断、风险分类和预后评估。

Abstract

Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.

04

Protocol for Brain Magnetic Resonance Imaging and Extraction of Imaging-Derived Phenotypes from the China Phenobank Project

多器官成像方案概述

DOI：

https://doi.org/10.1007/s43657-022-00083-w

引用格式：

Wang, C., Shi, Z., Li, Y. et al. Protocol for Brain Magnetic Resonance Imaging and Extraction of Imaging-Derived Phenotypes from the China Phenobank Project. Phenomics 3, 642–656 (2023). https://doi.org/10.1007/s43657-022-00083-w

本文旨在介绍 CHPP (China Phenobank Project) 大脑磁共振成像方案。本文可作为使用 CHPP 成像数据的出版物的参考资料，并为研究人员提供访问共享资源的信息。

Abstract

Imaging-derived phenotypes (IDPs) have been increasingly used in population-based cohort studies in recent years. As widely reported, magnetic resonance imaging (MRI) is an important imaging modality for assessing the anatomical structure and function of the brain with high resolution and excellent soft-tissue contrast. The purpose of this article was to describe the imaging protocol of the brain MRI in the China Phenobank Project (CHPP). Each participant underwent a 30-min brain MRI scan as part of a 2-h whole-body imaging protocol in CHPP. The brain imaging sequences included T1-magnetization that prepared rapid gradient echo, T2 fluid-attenuated inversion-recovery, magnetic resonance angiography, diffusion MRI, and resting-state functional MRI. The detailed descriptions of image acquisition, interpretation, and post-processing were provided in this article. The measured IDPs included volumes of brain subregions, cerebral vessel geometrical parameters, microstructural tracts, and function connectivity metrics.

05

 A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

辐射组学免疫分数（RIS）构建

DOI：

https://doi.org/10.1007/s43657-023-00136-8

引用格式：

Wu, J., Liu, W., Qiu, X. et al. A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma. Phenomics 3, 549–564 (2023). https://doi.org/10.1007/s43657-023-00136-8

该研究开发了一种新方法来评估肿瘤内的 TIME (tumor immune microenvironment) 并预测 HCC (hepatocellular carcinoma) 患者的预后。该研究检测了肿瘤内 17 种免疫相关蛋白标记物的表达。通过免疫相关标记物的表达来评估 TIME 值。随后，利用机器学习方法建立了一个放射组学模型来预测 TIME 值，并研究了其对预后和抗 PD-1 免疫疗法反应的潜在预测能力。

Abstract

It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

06

Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study

放射组学pipeline描述了建立放射组学 ML 模型并进行验证的数据处理和操作。

DOI：

https://doi.org/10.1007/s43657-023-00108-y

引用格式：

Li, Y., Li, F., Han, S. et al. Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study. Phenomics 3, 576–585 (2023). https://doi.org/10.1007/s43657-023-00108-y

该研究通过基于18F-DCFPyL正电子发射断层扫描/计算机断层扫描（PET/CT）影像组学而建立的机器学习模型，能够实现在无创条件下，有效预测前列腺病变的良性与恶性、前列腺癌的病理学高分级（Gleason评分>7）和前列腺癌的高风险（高D’Amico评分）。

Abstract

This study aimed to investigate the performance of 18F-DCFPyL positron emission tomography/computerized tomography (PET/CT) models for predicting benign-vs-malignancy, high pathological grade (Gleason score > 7), and clinical D'Amico classification with machine learning. The study included 138 patients with treatment-naïve prostate cancer presenting positive 18F-DCFPyL scans. The primary lesions were delineated on PET images, followed by the extraction of tumor-to-background-based general and higher-order textural features by applying five different binning approaches. Three layer-machine learning approaches were used to identify relevant in vivo features and patient characteristics and their relative weights for predicting high-risk malignant disease. The weighted features were integrated and implemented to establish individual predictive models for malignancy (Mm), high path-risk lesions (by Gleason score) (Mgs), and high clinical risk disease (by amico) (Mamico). The established models were validated in a Monte Carlo cross-validation scheme. In patients with all primary prostate cancer, the highest areas under the curve for our models were calculated. The performance of established models as revealed by the Monte Carlo cross-validation presenting as the area under the receiver operator characteristic curve (AUC): 0.97 for Mm, AUC: 0.73 for Mgs, AUC: 0.82 for Mamico. Our study demonstrated the clinical potential of 18F-DCFPyL PET/CT radiomics in distinguishing malignant from benign prostate tumors, and high-risk tumors, without biopsy sampling. And in vivo 18F-DCFPyL PET/CT can be considered a noninvasive tool for virtual biopsy for personalized treatment management.

07

 Comments on Study of “Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study”

DOI：

https://doi.org/10.1007/s43657-023-00143-9

引用格式：

Kreissl, M.C. Comments on Study of “Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study”. Phenomics 3, 639–641 (2023). https://doi.org/10.1007/s43657-023-00143-9

该论文为Michael C. Kreissl教授为“Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study”研究撰写的评论。

Abstract

Prostate cancer remains one of the most prevalent malignancies in men globally (Preisser et al. 2020). Accurate diagnosis and differentiation of the disease are paramount for effective treatment planning and improved patient outcomes (Hsieh et al. 2022). Traditionally, the diagnosis of prostate cancer heavily relied on invasive biopsy procedures, which, although effective, are associated with potential complications and discomfort for patients. With the advancements in medical imaging, positron emission tomography/computed tomography (PET/CT) using prostate-specific membrane antigen (PSMA) tracers has emerged as a revolutionary diagnostic tool (Obek et al. 2017). This technique, often termed as 'virtual biopsy', provides a non-invasive alternative to traditional biopsy. Baseline PSMA PET/CT offers detailed and precise imaging of prostate lesions, allowing clinicians to pinpoint not only the location but also the potential aggressiveness of the tumor. The capability of PSMA PET/CT to bind specifically to PSMA-expressing cells gives it a unique edge in differentiating between benign and malignant prostate lesions. This specificity also aids in identifying metastatic or recurrent diseases, often before they become evident on conventional imaging modalities (Papp et al. 2021).

In this comprehensive investigation, a patient cohort, consisting of 138 individuals exhibiting clinical indicators suggestive of prostate carcinoma, underwent detailed imaging studies using piflufolastat F-18 (18F-DCFPyL) PET/CT. This imaging modality captured a wide spectrum of prostate pathologies, encompassing benign hyperplasia, benign inflammatory processes, and a range of malignant neoplasms, each varying in their gleason score (GS) and D'Amico scores. Beyond just providing the conventional PET metrics, which include but are not limited to standard uptake value max (SUVmax), standard uptake value mean (SUVmean), standard uptake value ratio (SUVR), total lesion PSMA (TL-PSMA), and total volume  PSMA (PSMA-TV), this advanced imaging modality yielded granular tumor texture attributes, contextualized against the background data. Li et al. leveraged an advanced machine learning algorithm, meticulously rooted in the radiomics of 18F-DCFPyL positron emission tomography/computed tomography (PET/CT) imaging. This algorithm was meticulously developed with the aim of providing an innovative, non-invasive strategy for the efficacious stratification of prostate lesions (Lambin et al. 2017; Perandini et al. 2016). Its capabilities extend from differentiating benign from malignant prostate lesions, pinpointing high-grade pathological prostate neoplasms (with a Gleason score exceeding seven), to identifying prostate malignancies associated with an elevated D'Amico risk score.

08

Influence of Gender on Tau Precipitation in Alzheimer’s Disease According to ATN Research Framework

该研究纳入和排除受试者的流程图（ADNI 数据集）

DOI：

https://doi.org/10.1007/s43657-022-00076-9

引用格式：

Zhang, Y., Lu, J., Wang, M. et al. Influence of Gender on Tau Precipitation in Alzheimer’s Disease According to ATN Research Framework. Phenomics 3, 565–575 (2023). https://doi.org/10.1007/s43657-022-00076-9

性别特异性阿尔兹海默症( AD) 临床病理表型的概念在很大程度上尚未被探索，性别对脑tau蛋白沉积的影响的体内研究有限，结论存在争议。为了扩大性别对大脑tau累积影响的认识，该研究使用两种不同的tau正电子发射计算机断层扫描（PET）示踪剂，在A+人群中开展了一项包括高加索人和亚洲人的双队列研究。通过解决这种表型变异，目前的研究可能对开发精确有效的 AD 治疗方法具有重要意义。

Abstract

Tau proteins accumulation and their spreading pattern were affected by gender in cognitive impairment patients, especially in the progression of Alzheimer’s disease (AD). However, it was unclear whether the gender effects for tau deposition influenced by amyloid deposition. The aim of this study was to investigate gender differences for tau depositions in Aβ positive (A+) subjects. In this study, tau and amyloid positron emission tomography images, structural magnetic resonance imaging images, and demographic information were collected from 179 subjects in Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and 63 subjects from Huashan Hospital. Subjects were classified as T+ or T− according to the presence or absence of tau (T) biomarkers. We used two-sample t test and one-way analysis of variance test to analyze the effect of gender with adjusting for age, years of education, and Minimum Mental State Examination. In the ADNI cohort, we found differences in Tau deposition in fusiform gyrus, inferior temporal gyrus, middle temporal gyrus and parahippocampal gyrus between the female T+ (FT+) and male T+ (MT+) groups (p < 0.05). Tau deposition did not differ significantly between female T− (FT−) and male T− (MT−) subjects (p > 0.05). In the Huashan Hospital cohort, there was no difference in Tau deposition between FT+ and MT+ (p > 0.05). The results show that tau depositions significantly increased in females in above brain regions. Our findings suggest that tau deposition is influenced by gender in the A+ subjects. This result has important clinical implications for the development of gender-guided early interventions for patients with both Tau and Amyloid depositions.

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