Phenomic Imaging

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.

Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy

“…Among them, ultrasound elastography (UE) is an emerging ultrasound-based modality for in vivo imaging that has been used for visualization of tissue elasticity. − In addition to variations of biomolecular expression, elasticity or stiffness of tumor tissues is also an important “physical” marker to distinguish malignant tumors from normal or stromal tissues . Compared to the current standards of B-mode ultrasound and aspiration biopsy approach, differentiating the various benign or malignant tumors using UE proves to have more outstanding performance in tumor location and diagnostic accuracy for breast, − thyroid, and prostatic lesions analysis. , It is reported that the UE technique is used for prognostication of neoadjuvant chemotherapy response in different models with high specificity, providing a great potential for active detecting of tumor response to drug treatment, such as locally late-stage breast cancer as early as 2 weeks into treatment, IBC, and murine breast cancer model treated by chemotherapy …”

“… 18 Compared to the current standards of B-mode ultrasound and aspiration biopsy approach, differentiating the various benign or malignant tumors using UE proves to have more outstanding performance in tumor location and diagnostic accuracy for breast, 19 − 21 thyroid, and prostatic lesions analysis. 22 , 23 It is reported that the UE technique is used for prognostication of neoadjuvant chemotherapy response in different models with high specificity, providing a great potential for active detecting of tumor response to drug treatment, such as locally late-stage breast cancer as early as 2 weeks into treatment, 19 IBC, 20 and murine breast cancer model treated by chemotherapy. 21 …”

Sagittal balance parameters measurement on cervical spine MR images based on superpixel segmentation

“…Although standing cervical spine X-rays are economically feasible and readily available, they are limited to two-dimensional images with suboptimal resolution (Azimi et al, 2021;Boudreau et al, 2021). In contrast, MRI, being non-invasive and free from ionizing radiation risks, provides a more comprehensive evaluation of sagittal balance (Lan et al, 2023). However, supine MRI may not accurately reflect the loading state of the cervical spine in an upright posture, potentially leading to measurement inaccuracies (Boudreau et al, 2021).…”

Characteristics of auto-quantified tumor-infiltrating lymphocytes and the prognostic value in adenocarcinoma of the esophagogastric junction, gastric adenocarcinoma, and esophageal squamous cell carcinoma

“…Although the gold standard for evaluating TILs is based on routine haematoxylin–eosin (H&E) staining using a semi-quantitative scoring method, it may be subject to interobserver variability and costly [ 19 ]. Computational pathology has currently displayed promise in recognizing the biomarkers in tissues, and overcomes limitations related with manual grading and human bias [ 20 ]. Therefore, we establish cellular training sets for AEGJ, GAC, and ESCC based on the assessment of experienced pathologists.…”