Pub Date : 2024-07-01DOI: 10.1038/s44303-024-00018-2
Fan Fan, Georgia Martinez, Thomas DeSilvio, John Shin, Yijiang Chen, Jackson Jacobs, Bangchen Wang, Takaya Ozeki, Maxime W. Lafarge, Viktor H. Koelzer, Laura Barisoni, Anant Madabhushi, Satish E. Viswanath, Andrew Janowczyk
Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder ( http://cohortfinder.com ), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.
{"title":"CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models","authors":"Fan Fan, Georgia Martinez, Thomas DeSilvio, John Shin, Yijiang Chen, Jackson Jacobs, Bangchen Wang, Takaya Ozeki, Maxime W. Lafarge, Viktor H. Koelzer, Laura Barisoni, Anant Madabhushi, Satish E. Viswanath, Andrew Janowczyk","doi":"10.1038/s44303-024-00018-2","DOIUrl":"10.1038/s44303-024-00018-2","url":null,"abstract":"Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder ( http://cohortfinder.com ), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00018-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1038/s44303-024-00020-8
Jan-Philipp Redlich, Friedrich Feuerhake, Joachim Weis, Nadine S. Schaadt, Sarah Teuber-Hanselmann, Christoph Buck, Sabine Luttmann, Andrea Eberle, Stefan Nikolin, Arno Appenzeller, Andreas Portmann, André Homeyer
In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 83 publicly available research studies that have proposed AI-based methods for whole-slide histopathology images of human gliomas, covering the diagnostic tasks of subtyping (23/83), grading (27/83), molecular marker prediction (20/83), and survival prediction (29/83). All studies were reviewed with regard to methodological aspects as well as clinical applicability. It was found that the focus of current research is the assessment of hematoxylin and eosin-stained tissue sections of adult-type diffuse gliomas. The majority of studies (52/83) are based on the publicly available glioblastoma and low-grade glioma datasets from The Cancer Genome Atlas (TCGA) and only a few studies employed other datasets in isolation (16/83) or in addition to the TCGA datasets (15/83). Current approaches mostly rely on convolutional neural networks (63/83) for analyzing tissue at 20x magnification (35/83). A new field of research is the integration of clinical data, omics data, or magnetic resonance imaging (29/83). So far, AI-based methods have achieved promising results, but are not yet used in real clinical settings. Future work should focus on the independent validation of methods on larger, multi-site datasets with high-quality and up-to-date clinical and molecular pathology annotations to demonstrate routine applicability.
{"title":"Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review","authors":"Jan-Philipp Redlich, Friedrich Feuerhake, Joachim Weis, Nadine S. Schaadt, Sarah Teuber-Hanselmann, Christoph Buck, Sabine Luttmann, Andrea Eberle, Stefan Nikolin, Arno Appenzeller, Andreas Portmann, André Homeyer","doi":"10.1038/s44303-024-00020-8","DOIUrl":"10.1038/s44303-024-00020-8","url":null,"abstract":"In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 83 publicly available research studies that have proposed AI-based methods for whole-slide histopathology images of human gliomas, covering the diagnostic tasks of subtyping (23/83), grading (27/83), molecular marker prediction (20/83), and survival prediction (29/83). All studies were reviewed with regard to methodological aspects as well as clinical applicability. It was found that the focus of current research is the assessment of hematoxylin and eosin-stained tissue sections of adult-type diffuse gliomas. The majority of studies (52/83) are based on the publicly available glioblastoma and low-grade glioma datasets from The Cancer Genome Atlas (TCGA) and only a few studies employed other datasets in isolation (16/83) or in addition to the TCGA datasets (15/83). Current approaches mostly rely on convolutional neural networks (63/83) for analyzing tissue at 20x magnification (35/83). A new field of research is the integration of clinical data, omics data, or magnetic resonance imaging (29/83). So far, AI-based methods have achieved promising results, but are not yet used in real clinical settings. Future work should focus on the independent validation of methods on larger, multi-site datasets with high-quality and up-to-date clinical and molecular pathology annotations to demonstrate routine applicability.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00020-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-28DOI: 10.1038/s44303-024-00021-7
Qiang Wang, Ahsan R. Akram, David A. Dorward, Sophie Talas, Basil Monks, Chee Thum, James R. Hopgood, Malihe Javidi, Marta Vallejo
Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.
{"title":"Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images","authors":"Qiang Wang, Ahsan R. Akram, David A. Dorward, Sophie Talas, Basil Monks, Chee Thum, James R. Hopgood, Malihe Javidi, Marta Vallejo","doi":"10.1038/s44303-024-00021-7","DOIUrl":"10.1038/s44303-024-00021-7","url":null,"abstract":"Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-28DOI: 10.1038/s44303-024-00019-1
Barbara Sarri, Véronique Chevrier, Flora Poizat, Sandro Heuke, Florence Franchi, Louis De Franqueville, Eddy Traversari, Jean-Philippe Ratone, Fabrice Caillol, Yanis Dahel, Solène Hoibian, Marc Giovannini, Cécile de Chaisemartin, Romain Appay, Géraldine Guasch, Hervé Rigneault
Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the conventional histological investigation of these organoids necessitates their devitalization through fixation and slicing, limiting their utility to a single-time analysis. Here, we use stimulated Raman histology (SRH) to demonstrate non-destructive, label-free virtual staining of 3D organoids, while preserving their viability and growth. This novel approach provides contrast similar to conventional staining methods, allowing for the continuous monitoring of organoids over time. Our results demonstrate that SRH transforms organoids from one-time use products into repeatable models, facilitating the efficient selection of effective drug combinations. This advancement holds promise for personalized cancer treatment, allowing for the dynamic assessment and optimization of chemotherapy treatments in patient-specific contexts.
{"title":"In vivo organoid growth monitoring by stimulated Raman histology","authors":"Barbara Sarri, Véronique Chevrier, Flora Poizat, Sandro Heuke, Florence Franchi, Louis De Franqueville, Eddy Traversari, Jean-Philippe Ratone, Fabrice Caillol, Yanis Dahel, Solène Hoibian, Marc Giovannini, Cécile de Chaisemartin, Romain Appay, Géraldine Guasch, Hervé Rigneault","doi":"10.1038/s44303-024-00019-1","DOIUrl":"10.1038/s44303-024-00019-1","url":null,"abstract":"Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the conventional histological investigation of these organoids necessitates their devitalization through fixation and slicing, limiting their utility to a single-time analysis. Here, we use stimulated Raman histology (SRH) to demonstrate non-destructive, label-free virtual staining of 3D organoids, while preserving their viability and growth. This novel approach provides contrast similar to conventional staining methods, allowing for the continuous monitoring of organoids over time. Our results demonstrate that SRH transforms organoids from one-time use products into repeatable models, facilitating the efficient selection of effective drug combinations. This advancement holds promise for personalized cancer treatment, allowing for the dynamic assessment and optimization of chemotherapy treatments in patient-specific contexts.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-19DOI: 10.1038/s44303-024-00017-3
Neeladrisingha Das, Hieu T. M. Nguyen, Wan-Jin Lu, Arutselvan Natarajan, Syamantak Khan, Guillem Pratx
Positron emission tomography (PET), a cornerstone in cancer diagnosis and treatment monitoring, relies on the enhanced uptake of fluorodeoxyglucose ([18F]FDG) by cancer cells to highlight tumors and other malignancies. While instrumental in the clinical setting, the accuracy of [18F]FDG-PET is susceptible to metabolic changes introduced by radiation therapy. Specifically, radiation induces the formation of giant cells, whose metabolic characteristics and [18F]FDG uptake patterns are not fully understood. Through a novel single-cell gamma counting methodology, we characterized the [18F]FDG uptake of giant A549 and H1299 lung cancer cells that were induced by radiation, and found it to be considerably higher than that of their non-giant counterparts. This observation was further validated in tumor-bearing mice, which similarly demonstrated increased [18F]FDG uptake in radiation-induced giant cells. These findings underscore the metabolic implications of radiation-induced giant cells, as their enhanced [18F]FDG uptake could potentially obfuscate the interpretation of [18F]FDG-PET scans in patients who have recently undergone radiation therapy.
{"title":"Increased [18F]FDG uptake of radiation-induced giant cells: a single-cell study in lung cancer models","authors":"Neeladrisingha Das, Hieu T. M. Nguyen, Wan-Jin Lu, Arutselvan Natarajan, Syamantak Khan, Guillem Pratx","doi":"10.1038/s44303-024-00017-3","DOIUrl":"10.1038/s44303-024-00017-3","url":null,"abstract":"Positron emission tomography (PET), a cornerstone in cancer diagnosis and treatment monitoring, relies on the enhanced uptake of fluorodeoxyglucose ([18F]FDG) by cancer cells to highlight tumors and other malignancies. While instrumental in the clinical setting, the accuracy of [18F]FDG-PET is susceptible to metabolic changes introduced by radiation therapy. Specifically, radiation induces the formation of giant cells, whose metabolic characteristics and [18F]FDG uptake patterns are not fully understood. Through a novel single-cell gamma counting methodology, we characterized the [18F]FDG uptake of giant A549 and H1299 lung cancer cells that were induced by radiation, and found it to be considerably higher than that of their non-giant counterparts. This observation was further validated in tumor-bearing mice, which similarly demonstrated increased [18F]FDG uptake in radiation-induced giant cells. These findings underscore the metabolic implications of radiation-induced giant cells, as their enhanced [18F]FDG uptake could potentially obfuscate the interpretation of [18F]FDG-PET scans in patients who have recently undergone radiation therapy.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00017-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1038/s44303-024-00012-8
Nazish Khalid, Muhammad Zubair, Muhammad Qasim Mehmood, Yehia Massoud
In recent years, microwave imaging (MWI) has emerged as a non-ionizing and cost-effective modality in healthcare, specifically within medical imaging. Concurrently, advances in artificial intelligence (AI) have significantly augmented the capabilities of medical imaging tools. This paper explores the intersection of these two domains, focusing on the integration of AI algorithms into MWI techniques to elevate accuracy and overall performance. Within the scope of existing literature, representative prior works are compared concerning the application of AI in both the “MWI for Healthcare Applications" and “Artificial Intelligence Assistance In MWI" sections. This comparative analysis sheds light on the diverse approaches employed to enhance the synergy between AI and MWI. While highlighting the state-of-the-art technology in MWI and its historical context, this paper delves into the historical taxonomy of AI-assisted MWI, elucidating the evolution of intelligent systems within this domain. Moreover, it critically examines prominent works, providing a nuanced understanding of the advancements and challenges encountered. Addressing the limitations and challenges inherent in developing AI-assisted MWI systems like Generalization to different conditions, Generalization to different conditions, etc the paper offers a brief synopsis of these obstacles, emphasizing the importance of overcoming them for robust and reliable results in actual clinical environments. Finally, the paper not only underscores the current advancements but also anticipates future innovations and developments in utilizing AI for MWI applications in healthcare.
{"title":"Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence","authors":"Nazish Khalid, Muhammad Zubair, Muhammad Qasim Mehmood, Yehia Massoud","doi":"10.1038/s44303-024-00012-8","DOIUrl":"10.1038/s44303-024-00012-8","url":null,"abstract":"In recent years, microwave imaging (MWI) has emerged as a non-ionizing and cost-effective modality in healthcare, specifically within medical imaging. Concurrently, advances in artificial intelligence (AI) have significantly augmented the capabilities of medical imaging tools. This paper explores the intersection of these two domains, focusing on the integration of AI algorithms into MWI techniques to elevate accuracy and overall performance. Within the scope of existing literature, representative prior works are compared concerning the application of AI in both the “MWI for Healthcare Applications\" and “Artificial Intelligence Assistance In MWI\" sections. This comparative analysis sheds light on the diverse approaches employed to enhance the synergy between AI and MWI. While highlighting the state-of-the-art technology in MWI and its historical context, this paper delves into the historical taxonomy of AI-assisted MWI, elucidating the evolution of intelligent systems within this domain. Moreover, it critically examines prominent works, providing a nuanced understanding of the advancements and challenges encountered. Addressing the limitations and challenges inherent in developing AI-assisted MWI systems like Generalization to different conditions, Generalization to different conditions, etc the paper offers a brief synopsis of these obstacles, emphasizing the importance of overcoming them for robust and reliable results in actual clinical environments. Finally, the paper not only underscores the current advancements but also anticipates future innovations and developments in utilizing AI for MWI applications in healthcare.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00012-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.1038/s44303-024-00009-3
Yohana C. Toner, Geoffrey Prévot, Mandy M. T. van Leent, Jazz Munitz, Roderick Oosterwijk, Anna Vera D. Verschuur, Yuri van Elsas, Vedran Peric, Rianne J. F. Maas, Anna Ranzenigo, Judit Morla-Folch, William Wang, Martin Umali, Anne de Dreu, Jessica Chimene Fernandes, Nathaniel A. T. Sullivan, Alexander Maier, Christian Mason, Thomas Reiner, Zahi A. Fayad, Willem J. M. Mulder, Abraham J. P. Teunissen, Carlos Pérez-Medina
Macrophages are key inflammatory mediators in many pathological conditions, including cardiovascular disease (CVD) and cancer, the leading causes of morbidity and mortality worldwide. This makes macrophage burden a valuable diagnostic marker and several strategies to monitor these cells have been reported. However, such strategies are often high-priced, non-specific, invasive, and/or not quantitative. Here, we developed a positron emission tomography (PET) radiotracer based on apolipoprotein A1 (ApoA1), the main protein component of high-density lipoprotein (HDL), which has an inherent affinity for macrophages. We radiolabeled an ApoA1-mimetic peptide (mA1) with zirconium-89 (89Zr) to generate a lipoprotein-avid PET probe (89Zr-mA1). We first characterized 89Zr-mA1’s affinity for lipoproteins in vitro by size exclusion chromatography. To study 89Zr-mA1’s in vivo behavior and interaction with endogenous lipoproteins, we performed extensive studies in wildtype C57BL/6 and Apoe-/- hypercholesterolemic mice. Subsequently, we used in vivo PET imaging to study macrophages in melanoma and myocardial infarction using mouse models. The tracer’s cell specificity was assessed by histology and mass cytometry (CyTOF). Our data show that 89Zr-mA1 associates with lipoproteins in vitro. This is in line with our in vivo experiments, in which we observed longer 89Zr-mA1 circulation times in hypercholesterolemic mice compared to C57BL/6 controls. 89Zr-mA1 displayed a tissue distribution profile similar to ApoA1 and HDL, with high kidney and liver uptake as well as substantial signal in the bone marrow and spleen. The tracer also accumulated in tumors of melanoma-bearing mice and in the ischemic myocardium of infarcted animals. In these sites, CyTOF analyses revealed that natZr-mA1 was predominantly taken up by macrophages. Our results demonstrate that 89Zr-mA1 associates with lipoproteins and hence accumulates in macrophages in vivo. 89Zr-mA1’s high uptake in these cells makes it a promising radiotracer for non-invasively and quantitatively studying conditions characterized by marked changes in macrophage burden.
{"title":"Macrophage PET imaging in mouse models of cardiovascular disease and cancer with an apolipoprotein-inspired radiotracer","authors":"Yohana C. Toner, Geoffrey Prévot, Mandy M. T. van Leent, Jazz Munitz, Roderick Oosterwijk, Anna Vera D. Verschuur, Yuri van Elsas, Vedran Peric, Rianne J. F. Maas, Anna Ranzenigo, Judit Morla-Folch, William Wang, Martin Umali, Anne de Dreu, Jessica Chimene Fernandes, Nathaniel A. T. Sullivan, Alexander Maier, Christian Mason, Thomas Reiner, Zahi A. Fayad, Willem J. M. Mulder, Abraham J. P. Teunissen, Carlos Pérez-Medina","doi":"10.1038/s44303-024-00009-3","DOIUrl":"10.1038/s44303-024-00009-3","url":null,"abstract":"Macrophages are key inflammatory mediators in many pathological conditions, including cardiovascular disease (CVD) and cancer, the leading causes of morbidity and mortality worldwide. This makes macrophage burden a valuable diagnostic marker and several strategies to monitor these cells have been reported. However, such strategies are often high-priced, non-specific, invasive, and/or not quantitative. Here, we developed a positron emission tomography (PET) radiotracer based on apolipoprotein A1 (ApoA1), the main protein component of high-density lipoprotein (HDL), which has an inherent affinity for macrophages. We radiolabeled an ApoA1-mimetic peptide (mA1) with zirconium-89 (89Zr) to generate a lipoprotein-avid PET probe (89Zr-mA1). We first characterized 89Zr-mA1’s affinity for lipoproteins in vitro by size exclusion chromatography. To study 89Zr-mA1’s in vivo behavior and interaction with endogenous lipoproteins, we performed extensive studies in wildtype C57BL/6 and Apoe-/- hypercholesterolemic mice. Subsequently, we used in vivo PET imaging to study macrophages in melanoma and myocardial infarction using mouse models. The tracer’s cell specificity was assessed by histology and mass cytometry (CyTOF). Our data show that 89Zr-mA1 associates with lipoproteins in vitro. This is in line with our in vivo experiments, in which we observed longer 89Zr-mA1 circulation times in hypercholesterolemic mice compared to C57BL/6 controls. 89Zr-mA1 displayed a tissue distribution profile similar to ApoA1 and HDL, with high kidney and liver uptake as well as substantial signal in the bone marrow and spleen. The tracer also accumulated in tumors of melanoma-bearing mice and in the ischemic myocardium of infarcted animals. In these sites, CyTOF analyses revealed that natZr-mA1 was predominantly taken up by macrophages. Our results demonstrate that 89Zr-mA1 associates with lipoproteins and hence accumulates in macrophages in vivo. 89Zr-mA1’s high uptake in these cells makes it a promising radiotracer for non-invasively and quantitatively studying conditions characterized by marked changes in macrophage burden.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00009-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1038/s44303-024-00014-6
Karthik Menon, Muhammed Owais Khan, Zachary A. Sexton, Jakob Richter, Patricia K. Nguyen, Sachin B. Malik, Jack Boyd, Koen Nieman, Alison L. Marsden
Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges – incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.
{"title":"Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees","authors":"Karthik Menon, Muhammed Owais Khan, Zachary A. Sexton, Jakob Richter, Patricia K. Nguyen, Sachin B. Malik, Jack Boyd, Koen Nieman, Alison L. Marsden","doi":"10.1038/s44303-024-00014-6","DOIUrl":"10.1038/s44303-024-00014-6","url":null,"abstract":"Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges – incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00014-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140817289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.1038/s44303-024-00016-4
Safa Hameed, Navin Viswakarma, Greta Babakhanova, Carl G. Simon Jr., Boris Epel, Mrignayani Kotecha
{"title":"Author Correction: Nondestructive, longitudinal, 3D oxygen imaging of cells in a multi-well plate using pulse electron paramagnetic resonance imaging","authors":"Safa Hameed, Navin Viswakarma, Greta Babakhanova, Carl G. Simon Jr., Boris Epel, Mrignayani Kotecha","doi":"10.1038/s44303-024-00016-4","DOIUrl":"10.1038/s44303-024-00016-4","url":null,"abstract":"","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00016-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1038/s44303-024-00015-5
Timothy H. Witney
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