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Sulforaphane Improves Ischemia-Induced Detrusor Overactivity by Downregulating the Enhancement of Associated Endoplasmic Reticulum Stress, Autophagy, and Apoptosis in Rat Bladder

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Scientific reports 2016年第1期6卷 36110页

作者： Huai-Ching Tai Shiu-Dong Chung Chiang-Ting Chien Hong-Jeng Yu Department of Urology National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan. Graduate Institution of Clinical Medicine National Taiwan University College of Medicine Taipei Taiwan. Department of Urology Far East Memory Hospital New Taipei City Taiwan. Graduate Program in Biomedical Informatics College of Informatics Yuan-Ze University Chung-Li Tao-Yuan Taiwan. Department of Life Science National Taiwan Normal University Taipei Taiwan.

Atherosclerosis-associated pelvic ischemia has been reported to be a risk factor for bladder dysfunction and subsequent lower urinary tract symptoms (LUTS) in the elderly population. However, the molecular mechanisms of this association remain unclear. We hypothesized that stress-induced cellular responses might play a role in the pathogenesis of ischemia-induced bladder dysfunction. In the present study, the animal model of bladder ischemia was induced by bilateral partial arterial occlusion (BPAO) in rats. We found that BPAO significantly induced the presence of detrusor overactivity (DO) and upregulated the expression of several molecular reactions, including biomarkers in endoplasmic reticulum stress (78 kDa glucose-regulated protein, GRP78 and C/EBP-homologous protein, CHOP), autophagy (Beclin-1, p62 and LC3 II) and apoptosis (caspase 3). BPAO also disturbed the Kelch-like ECH-associated protein 1-nuclear factor erythroid-2-related factor 2 (Keap1-Nrf2) pathways. These responses might collectively alter muscarinic and purinergic signaling and contribute to the presence of DO in the ischemic bladder. Therapeutically, treatment with neither a muscarinic nor purinergic receptor antagonist restored bladder function. Interestingly, sulforaphane effectively attenuated ischemia-enhanced endoplasmic reticulum stress, autophagy and apoptosis in the bladder, subsequently ameliorated ischemia-induced bladder dysfunction and might emerge as a novel strategy to protect the bladder against ischemia-induced oxidative damage.

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： Computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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A web-based, hospital-wide health care-associated bloodstream infection surveillance and classification system: Development and evaluation

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JMIR Medical informatics 2015年第3期3卷 e31页

作者： Tseng, Yi-Ju Wu, Jung-Hsuan Lin, Hui-Chi Chen, Ming-Yuan Ping, Xiao-Ou Sun, Chun-Chuan Shang, Rung-Ji Sheng, Wang-Huei Chen, Yee-Chun Lai, Feipei Chang, Shan-Chwen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Computational Health Informatics Program Boston Children's Hospital Boston MA United States Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Infection Control National Taiwan University Hospital Taipei Taiwan National Taiwan University Hospital Information Systems Office Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan National Health Research Institutes National Institute of Infectious Diseases and Vaccinology Miaoli Taiwan

Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20;r=.999 P<.001) and by time (n=14;r=.941;P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. © 2020 JMIR Publications Inc. All rights reserved.

关键词： Health care-associated infection Infection control Information systems Surveillance Web-based services

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Geographic and age variations in mutational processes in colorectal cancer

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Nature 2025年 1-11页

作者： Marcos Díaz-Gay Mariya Kazachkova Ammal Abbasi Christopher D. Steele Raviteja Vangara Erik N. Bergstrom Azhar Khandekar Burçak Otlu Ludmil B. Alexandrov Wellington dos Santos Sergey Senkin Behnoush Abedi-Ardekani Ana Carolina de Carvalho Thomas Cattiaux Ricardo Cortez Cardoso Penha Valérie Gaborieau Priscilia Chopard Sandra Perdomo Paul Brennan Sarah Moody Jingwei Wang Stephen Fitzgerald Saamin Cheema Calli Latimer Jon W. Teague Laura Humphreys Michael R. Stratton Christine Carreira Anush Mukeriya David Zaridze Riley Cox Monique Albert Larry Phouthavongsy Steven Gallinger Reza Malekzadeh Ahmadreza Niavarani Marko Miladinov Katarina Erić Sasa Milosavljevic Suleeporn Sangrajrang Maria Paula Curado Samuel Aguiar Rui Manuel Reis Monise Tadin Reis Luis Gustavo Romagnolo Denise Peixoto Guimarães Ivana Holcatova Jaroslav Kalvach Carlos Alberto Vaccaro Tamara Alejandra Piñero Beata Świątkowska Jolanta Lissowska Katarzyna Roszkowska-Purska Antonio Huertas-Salgado Tatsuhiro Shibata Satoshi Shiba Surasak Sangkhathat Taned Chitapanarux Gholamreza Roshandel Patricia Ashton-Prolla Daniel C. Damin Francine Hehn de Oliveira Trevor D. Lawley Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Moores Cancer Center University of California San Diego La Jolla CA USA Digital Genomics Group Structural Biology Program Spanish National Cancer Research Center (CNIO) Madrid Spain Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Health Informatics Graduate School of Informatics Middle East Technical University Ankara Turkey Sanford Stem Cell Institute University of California San Diego La Jolla CA USA Genomic Epidemiology Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Cancer Ageing and Somatic Mutation Wellcome Sanger Institute Cambridge UK Evidence Synthesis and Classification Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Clinical Epidemiology N. N. Blokhin National Medical Research Centre of Oncology Moscow Russia Ontario Tumour Bank Ontario Institute for Cancer Research Toronto Ontario Canada Centre for Biodiversity Genomics University of Guelph Guelph Ontario Canada Lunenfeld-Tanenbaum Research Institute Sinai Health System Toronto Ontario Canada Digestive Oncology Research Center Digestive Disease Research Institute Tehran University of Medical Sciences Tehran Iran Clinic for Digestive Surgery—First Surgical Clinic University Clinical Centre of Serbia Belgrade Serbia Department of Pathology University Clinical Centre of Serbia Belgrade Serbia International Organization for Cancer Prevention and Research Belgrade Serbia National Cancer Institute Bangkok Thailand Department of Epidemiology A. C. Camargo Cancer Center Sao Paulo Brazil Colon Cancer Reference Center A. C. Camargo Cancer Center Sao Paulo Brazil Molecular Oncology Research Center Barretos Cancer Hospital

Incidence rates of colorectal cancer vary geographically and have changed over time1. Notably, in the past two decades, the incidence of early-onset colorectal cancer, which affects individuals below 50 years of age, has doubled in many countries2,3,4,5. The reasons for this increase are unknown. Here we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite-unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown aetiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin6,7, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals who were diagnosed before 40 years of age than in those over 70 years of age, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked toAPCdriver mutations, with ID18 being responsible for about 25% ofAPCdriver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that mutagenic exposure to colibactin-producing bacteria in early life may contribute to the increasing incidence of early-onset colorectal cancer.

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Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning

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Journal of cellular and molecular medicine 2024年第12期28卷 e18404页

作者： Li-Jen Liao Chien-Chen Tsai Po-Yu Li Cheng-Yun Lee Shian-Ren Lin Wan-Yu Lai I-Yu Chen Chiung-Fang Chang Jan-Mou Lee Yen-Ling Chiu Department of Otolaryngology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Anatomical Pathology Far Eastern Memorial Hospital New Taipei City Taiwan. FullHope Biomedical Co. Ltd New Taipei City Taiwan. Division of Nephrology Department of Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Medical Research Far Eastern Memorial Hospital New Taipei City Taiwan. Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei Taiwan.

In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral blood remains unclear. In this study, we established an immune cell profile for patients with NPC and used flow cytometry and machine learning (ML) to identify the characteristics of this profile. After isolation of circulating leukocytes, the proportions of 104 immune cell subsets were compared between NPC group and the healthy control group (HC). Data obtained from the immune cell profile were subjected to ML training to differentiate between the immune cell profiles of the NPC and HC groups. We observed that subjects in the NPC group presented higher proportions of T cells, memory B cells, short-lived plasma cells, IgG-positive B cells, regulatory T cells, MHC II T cells, CTLA4 T cells and PD-1 T cells than subjects in the HC group, indicating weaker and compromised cellular and humoral immune responses. ML revealed that monocytes, PD-1 CD4 T cells, memory B cells, CTLA4 CD4 T cells and PD-1 CD8 T cells were strongly contributed to the difference in immune cell profiles between the NPC and HC groups. This alteration can be fundamental in developing novel immunotherapies for NPC.

关键词： flow cytometry immune cell profile machine learning nasopharyngeal carcinoma (NPC)

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Personal health records - Potential one-stop approach in polypharmacy

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Computer Methods and programs in Biomedicine Update 2022年 2卷

作者： Chen, Ya-Lin Satria, Fauzi Budi Jamshed, Shazia Babar, Zaheer-Ud-Din Mukherjee, Nilesh Yang, Hsuan-Chia Li, Yu-Chuan Iqbal, Usman Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan Department of Pharmacy Practice Faculty of Pharmacy UniSZA Kuala Terengganu Malaysia Qualitative Research-Methodological Application in Health Sciences Research Group Kulliyyah of Pharmacy International Islamic University Malaysia Kuantan Malaysia Centre for Pharmaceutical Policy and Practice Research Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Master of Science in Systems Biology Manipal School of Life Sciences Manipal Academy of Higher Education Manipal India International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer’s disease

Nature Aging

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Nature Aging 2025年 1-18页

作者： Heo, Gyujin Xu, Ying Wang, Erming Ali, Muhammad Oh, Hamilton Se-Hwee Moran-Losada, Patricia Anastasi, Federica González Escalante, Armand Puerta, Raquel Song, Soomin Timsina, Jigyasha Liu, Menghan Western, Daniel Gong, Katherine Chen, Yike Kohlfeld, Pat Flynn, Allison Thomas, Alvin G. Lowery, Joseph Morris, John C. Holtzman, David M. Perlmutter, Joel S. Schindler, Suzanne E. Vilor-Tejedor, Natalia Suárez-Calvet, Marc García-González, Pablo Marquié, Marta Fernández, Maria Victoria Boada, Mercè Cano, Amanda Ruiz, Agustín Zhang, Bin Bennett, David A. Benzinger, Tammie Wyss-Coray, Tony Ibanez, Laura Sung, Yun Ju Cruchaga, Carlos Department of Psychiatry Washington University School of Medicine St. Louis MO United States NeuroGenomics and Informatics Center Washington University St. Louis MO United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY United States Mount Sinai Center for Transformative Disease Modeling Icahn School of Medicine at Mount Sinai New York NY United States Graduate Program in Stem Cell and Regenerative Medicine Stanford University Stanford CA United States The Phil and Penny Knight Initiative for Brain Resilience Stanford University Stanford CA United States Wu Tsai Neurosciences Institute Stanford University Stanford CA United States Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford CA United States Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain Hospital del Mar Research Institute Barcelona Spain Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology (BIST) Barcelona Spain Department of Medicine and Life Sciences Universitat Pompeu Fabra Barcelona Spain Ace Alzheimer Center Barcelona Universitat Internacional de Catalunya Barcelona Spain PhD Program in Biotechnology Faculty of Pharmacy and Food Sciences University of Barcelona Barcelona Spain Department of Neurology Washington University School of Medicine St. Louis MO United States Hope Center for Neurologic Diseases Washington University St. Louis MO United States Knight Alzheimer’s Disease Research Center Washington University St. Louis MO United States Department of Radiology Washington University School of Medicine St. Louis MO United States Department of Genetics Radboud UMC Nijmegen Netherlands Servei de Neurologia Hospital del Mar Barcelona Spain Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED) National Institute of Health Carlos III Madrid Spain Glenn Biggs Institute for Alzheimer’s & Ne

Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer’s disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more than 3,300 well-characterized individuals to identify new proteins, pathways and predictive models for AD. We identified 416 proteins (294 new) associated with clinical AD status and validated the findings in two external datasets representing more than 7,000 samples. AD-related proteins reflected blood–brain barrier disruption and other processes implicated in AD, such as lipid dysregulation or immune responses. A machine learning model was used to identify a set of seven proteins that were highly predictive of both clinical AD (area under the curve (AUC) of >0.72) and biomarker-defined AD status (AUC of >0.88), which were replicated in multiple external cohorts and orthogonal platforms. These findings underscore the potential of using plasma proteins as biomarkers for the early detection and monitoring of AD and for guiding treatment decisions. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation

arXiv

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arXiv 2024年

作者： Lee, Junhyeok Oh, Yujin Lee, Dahyoun Joh, Hyon Keun Sohn, Chul-Ho Baik, Sung Hyun Jung, Cheol Kyu Park, Jung Hyun Choi, Kyu Sung Kim, Byung-Hoon Ye, Jong Chul Interdisciplinary Program in Cancer Biology Seoul National University College of Medicine Seoul Korea Republic of Harvard Medical School BostonMA United States Department of Biomedical Systems Informatics Yonsei University College of Medicine Seoul Korea Republic of Department of Medicine The Graduate School of Yonsei University Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Department of Radiology Seoul National University Hospital Seoul Korea Republic of Department of Radiology Seoul National University Bundang Hospital Seongnam Korea Republic of Department of Radiology Seoul Metropolitan Government Seoul National University Boramae Medical Center Seoul Korea Republic of Department of Psychiatry Yonsei University College of Medicine Seoul Korea Republic of Institute of Behavioral Sciences in Medicine Yonsei University College of Medicine Seoul Korea Republic of Daejeon Korea Republic of

Acute ischemic stroke (AIS) requires time-critical management, with hours of delayed intervention leading to an irreversible disability of the patient. Since diffusion weighted imaging (DWI) using the magnetic resonance image (MRI) plays a crucial role in the detection of AIS, automated prediction of AIS from DWI has been a research topic of clinical importance. While text radiology reports contain the most relevant clinical information from the image findings, the difficulty of mapping across different modalities has limited the factuality of conventional direct DWI-to-report generation methods. Here, we propose paired image-domain retrieval and text-domain augmentation (PIRTA), a cross-modal retrieval-augmented generation (RAG) framework for providing clinician-interpretative AIS radiology reports with improved factuality. PIRTA mitigates the need for learning cross-modal mapping, which poses difficulty in image-to-text generation, by casting the cross-modal mapping problem as an in-domain retrieval of similar DWI images that have paired ground-truth text radiology reports. By exploiting the retrieved radiology reports to augment the report generation process of the query image, we show by experiments with extensive in-house and public datasets that PIRTA can accurately retrieve relevant reports from 3D DWI images. This approach enables the generation of radiology reports with significantly higher accuracy compared to direct image-to-text generation using state-of-the-art multimodal language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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