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14th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2023

作者： Gouthamchand, Varsha Choudhury, Ananya Hoebers, Frank Wesseling, Frederik Welch, Mattea Kim, Sejin Haibe-Kains, Benjamin Kazmierska, Joanna Dekker, Andre van Soest, Johan Wee, Leonard GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht Netherlands Clinical Data Science Faculty of Health Medicine and Life Sciences Maastricht University Maastricht Netherlands Radiation Medicine Program Princess Margaret Cancer Centre University Health Network TorontoON Canada Dept of Radiation Oncology Greater Poland Cancer Centre II Poznan Poland Brightlands Institute for Smart Society Faculty of Science and Engineering Maastricht University Heerlen Netherlands

Research in modern healthcare requires vast volumes of data from various healthcare centers across the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated Learning approach, governed in a set of agreements such as the Personal Health Train (PHT) manages to tackle these concerns by distributing models to the data centers instead of the traditional approach of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles help in building interoperable and reusable data by adding knowledge representation and providing descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward. Our main objective is to disentangle this process by using domain and technical expertise and get data prepared for federated learning. This paper introduces applications that are easily deployable as Docker containers, which will automate parts of the aforementioned process and significantly simplify the task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to explore data from multiple centers without breaching privacy. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge representation

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Outcomes of Patients with Hematologic Malignancies and COVID-19 Infection: A Report from the ASH Research Collaborative data Hub

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BLOOD 2020年 136卷 7-8页

作者： Wood, William A. Neuberg, Donna S. Thompson, John Colton Tallman, Martin S. Sekeres, Mikkael A. Sehn, Laurie H. Anderson, Kenneth Goldberg, Aaron D. Pennell, Nathan A. Niemeyer, Charlotte M. Tucker, Emily Hewitt, Kathleen Plovnick, Robert M. Hicks, Lisa K. Division of Hematology Department of Medicine University of North Carolina Chapel Hill NC Department of Data Science Dana-Farber Cancer Institute Boston MA ASH Research Collaborative Washington DC Memorial Sloan-Kettering Cancer Center New York NY Leukemia Program Department of Hematology and Medical Oncology Cleveland Clinic Taussig Cancer Institute Cleveland OH British Columbia Cancer Agency Vancouver Canada Jerome Lipper Multiple Myeloma Center LeBow Institute for Myeloma Therapeutics Dana-Farber Cancer Institute Harvard Medical School Boston MA Department of Medicine Leukemia Service Memorial Sloan-Kettering Cancer Center New York NY Lung Program Medical Oncology Cleveland Clinic Taussig Cancer Institute Cleveland OH University Children's Hospital Freiburg Germany ASH Research Collaborative Washington DC DC American Society of Hematology Washington DC DC Division of Hematology/Oncology St. Michael's Hospital Toronto Canada

Introduction: The coronavirus disease 2019 (COVID-19) is an illness resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019. Many patients with blood cancer have underlying immune dysfunction, and many are treated with chemotherapies and immunotherapies that are themselves profoundly immunosuppressive. Additionally, patients with blood cancer are often older, may have comorbid illness including hypertension and diabetes, and may be especially susceptible to complications of COVID-19 include hypercoagulability and thrombosis. For patients with hematologic malignancies, overall risk of morbidity and mortality from COVID-19 infection, and how this risk varies as a function of age, disease status, type of malignancy, and cancer therapy, has not yet been well defined. Methods: The ASH Research Collaborative COVID-19 Registry for Hematology was developed to study features and outcomes of COVID-19 infection in patients with underlying blood disorders, such as hematologic malignancies. The Registry opened for data collection on April 1, 2020. The Registry is a global effort and is housed on a secure data platform hosted by Prometheus Research, an IQVIA company. The Registry collects data from patients of all ages with a current or history of hematological disease, and either a laboratory-confirmed or presumptive diagnosis of SARS-CoV-2 infection. data are made available and regularly updated on the ASH Research Collaborative website to guide the provider and patient communities. data presented here are limited to malignant hematologic diseases only. Contributors are individual providers or designees submitting data on behalf of providers. Results: At the time of this analysis, data from 250 patients with blood cancers from 74 sites around the world had been entered into the Registry. The most commonly represented malignancies were acute leukemia (33%), non-Hodgkin lymphoma (27%), and myeloma or amyloidosis (16%). Patients presen

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Phase I Study of Ixazomib with Conventional Chemotherapy in the Treatment of Acute Myeloid Leukemia in Older Adults

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BLOOD 2020年 136卷 7-8页

作者： Amrein, Philip C. Attar, Eyal C. Fell, Geoffrey Blonquist, Traci M. Brunner, Andrew M. Hobbs, Gabriela S. Hock, Hanno R. McAfee, Steven L. Narayan, Rupa Moran, Jenna A. Bergeron, Meghan Foster, Julia E. Bertoli, Christina McGregor, Kristin Macrae, Molly Burke, Meghan Behnan, Tanya T. Som, Tina T. Ramos, Aura Y. Vartanian, Megan K. Story, Jennifer Lombardi Neuberg, Donna S. Fathi, Amir T. Leukemia Center/MGH Cancer Center Massachusetts General Hospital Belmont MA Agios Boston MA Data Science Dana-Farber Cancer Institute Boston MA Center for Leukemia Massachusetts General Hospital Boston MA Leukemia Center Massachusetts General Hospital Boston MA Leukemia Center Massachusetts General Hospital / Harvard Medical School Boston MA Massachusetts General Hospital Blood and Marrow Transplant Program Boston MA Leukemia Center Massachusetts General Hospital Center for Leukemia Cambridge MA

Introduction: Outcomes for acute myeloid leukemia (AML) among older patients has remained largely unchanged for decades. Long-term survival for patients aged >60 years is poor (median survival 10.5 months). Targeting the proteasome in AML is attractive, since leukemia stem cells have demonstrated sensitivity to proteasome inhibition in preclinical models, perhaps through down regulation of nuclear NF-KB (Guzman, Blood 2001). AML cell lines are susceptible to synergistic cytotoxicity when bortezomib, a proteasome inhibitor, is combined with daunorubicin and cytarabine. We have shown that adding bortezomib to standard treatment in AML results in a high remission rate, although grade 2 sensory neurotoxicity was noted in approximately 12% of treated patients. A newer generation proteasome inhibitor, ixazomib, is less frequently associated with neurotoxicity, and, therefore, was selected for combination with conventional chemotherapy in this phase I trial. The primary objective of this study was to determine the maximum tolerated dose (MTD) of ixazomib in combination with conventional induction and consolidation chemotherapy for AML. Herein are the initial results of this trial. Methods: Adults >60 years of age with newly diagnosed AML were screened for eligibility. Patients with secondary AML were eligible, including those with prior hypomethylating agent therapy for myelodysplastic syndromes (MDS). We excluded those with promyelocytic leukemia. There were 2 phases in this study. In the first phase (A), the induction treatment consisted of the following: cytarabine 100 mg/m2/day by continuous IV infusion, Days 1-7; daunorubicin 60 mg/m2/day IV, Days 1, 2, 3, and ixazomib was provided orally at the cohort dose, Days 2, 5, 9, and 12. Consolidaton or transplant was at the discretion of the treating physician in phase A. In the second phase (B), induction was the same as that with the determined MTD of ixazomib. All patients were to be treated with the following consolidati

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Frequency of Deleterious Pathogenic/Likely Pathogenic Germline Variants in Related and Unrelated Allogeneic Hematopoietic Cell Transplant Donors for Patients with Myelodysplastic Syndrome

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Transplantation and Cellular Therapy 2025年第2期31卷 S113-S113页

作者： Stubbins, Ryan J. Hattig, Zachary S. Trottier, Amy M. Rodgers, Courtnee V. Haribabu, Yoga Crone, Lara Govaerts, Inge Smith, William, MD Juckett, Mark Grunwald, Michael R. Oran, Betul, MD, MS Elmariah, Hany Villa, Kennia Auer, Paul Kim, Soyoung Dong, Jing Chen, Zhongyuan Parrilla, Charimar Santiago Zhang, Tao Bolon, Yung-Tsi Spellman, Stephen R., MBS Saber, Wael Godley, Lucy A., MD, PhD Leukemia/ Bone Marrow Transplant Program of British Columbia Vancouver BC Canada Robert H. Lurie Comprehensive Cancer Center and Northwestern University Chicago IL Division of Hematology Department of Medicine Dalhousie University Halifax NS Canada Department of Pathology Dalhousie University Halifax NS Canada Department of Pathology Wayne State University School of Medicine Detroit MI Division of Hematology Oncology and Transplantation University of Minnesota Minneapolis MN Department of Hematologic Oncology and Blood Disorders Levine Cancer Institute Atrium Health NC Levine Cancer Institute Charlotte NC Department of Stem Cell Transplantation and Cellular Therapy The University of Texas MD Anderson Cancer Center Houston TX Blood and Marrow Transplantation and Cellular Immunotherapy H. Lee Moffitt Cancer Center and Research Institute Tampa FL CIBMTR® (Center for International Blood and Marrow Transplant Research) NMDP Minneapolis MN Division of Biostatistics Data Science Institute and Cancer Center Medical College of Wisconsin Milwaukee WI CIBMTR® (Center for International Blood and Marrow Transplant Research) Department of Medicine Medical College of Wisconsin Milwaukee WI Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine Medical College of Wisconsin Milwaukee WI Division of Biostatistics Institute for Health and Equity and Cancer Center Medical College of Wisconsin Milwaukee WI Cancer Center Biostatistics Shared Resource Medical College of Wisconsin Milwaukee WI Robert H. Lurie Comprehensive Cancer Center Northwestern University Chicago IL

IntroductionUp to 15% of myelodysplastic syndrome (MDS) patients have pathogenic (P) or likely pathogenic (LP) germline variants (PGVs) underlying their disease. PGVs in allogeneic hematopoietic cell transplant (allo-HCT) donor cells may impact outcomes. Around 5% of related donors (RDs) share PGVs with recipients, the rate of PGVs in unrelated donors (UDs) is unknown. ObjectiveWe quantified PGVs in recipient-donor pairs for patients undergoing RD or UD allo-HCT for MDS. MethodsPaired peripheral blood samples from MDS patients undergoing allo-HCT and RDs or UDs underwent 30X whole genome sequencing (WGS) in prior CIBMTR studies. Variants were called in 40 hematologic cancer predisposition genes. Quality filtered variants were culled by a 0.005 gnomAD allele frequency (AF) cutoff, adjusted to 0.05 for genes with high AF P/LP variants ( CHEK2, DDX41). Intronic variants were removed, except in genes with known P/LP intronic variants ( DKC1, FANCI, GATA2, NF1, TERT). Variants with >25 somatic occurrences in COSMIC were removed. P/LP variants were manually curated for pathogenicity per established guidelines. Shared PGVs in RD and recipients were confirmed germline. PGVs in UD and recipients were considered likely germline if variants known to have minimal somatic variation. ResultsTotal 494 patients with paired recipient-donor WGS were included, 97 patients had RDs and 397 patients had UDs as their stem cell source. PGVs were found in 15% (75/494) of recipients with MDS, most commonly in DDX41 (20%, 15/75), RUNX1 (17%, 13/75), and CHEK2 (8%, 6/75). In RDs, 5% (5/97) of donors had PGVs. The PGV was shared between the donor and recipient in 80% (4/5) of cases and in 20% (1/5), the recipient lacked the donor PGV. Among UDs, 4% (16/397) had PGVs identified. In 19% (3/16) of UDs with a PGV the recipient had a separate, unshared PGV (Table 1). ConclusionWe identified PGVs in a significant number of allo-HCT donors for MDS patients, with similar rates in RDs as prior literatur

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Identification of Frailty Clusters Using Cross-Sectional Frailty and Frailty Trajectory: Cohort of Heart Failure Veterans

JACC: Advances

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JACC: Advances 2025年第5期4卷

作者： Razjouyan, Javad Tofighi, Saeed Orkaby, Ariela R. Bozkurt, Biykem Sharafkhaneh, Amir Horstman, Molly J. Goyal, Parag Amos, Christopher I. Intrator, Orna Naik, Aanand D. VA HSR&D Center for Innovations in Quality Effectiveness and Safety Michael E. DeBakey VA Medical Center Houston TX United States Department of Medicine Baylor College of Medicine Houston TX United States Big Data Scientist Training Enhancement Program VA Office of Research and Development Washington DC United States Tehran Heart Center Cardiovascular Diseases Research Institute Tehran University of Medical Sciences Tehran Iran New England Geriatrics Research Education and Clinical Center VA Boston Health Care System Boston MA United States Brigham & Women's Hospital Harvard Medical School Boston MA United States Division of General Internal Medicine Department of Medicine Weill Medical College of Cornell University New York NY United States Division of Cardiology Department of Medicine Weill Medical College of Cornell University New York NY United States Department of Internal Medicine UNM Comprehensive Cancer Center University of New Mexico Albuquerque NM United States Brookdale Department of Geriatrics & Palliative Medicine Icahn School of Medicine at Mount Sinai New York NY United States Geriatrics & Extended Care Data Analysis Center James J. Peters VA Medical Center Bronx NY United States Department of Management Policy and Community Health The University of Texas Health Science Center at Houston (UTHealth) Houston TX United States UTHealth Institute on Aging University of Texas Health Science Center Houston TX United States

Background: Frailty is a syndrome associated with increased vulnerability and diminished physiological reserves. Three-quarters (78%) of heart failure (HF) patients are frail. Traditional frailty indices (FIs) assess cross-sectional deficits, while frailty trajectories (FTs) measure changes over time. Objectives: This study aims to examine the interaction between FI and FT to enhance risk stratification in hospitalized adults with HF. Methods: This retrospective cohort study utilized data from the Veterans Health Administration, including 143,687 veterans aged >50 admitted for HF from 2005 to 2019. FT measurements were derived from FI calculations for each of the 3 years before index hospitalization. Unsupervised clustering identified 4 clusters based on FI and FT interactions: low-low, low-high, high-low, and high-high. Associations between these clusters and clinical outcomes (ie, 1-year mortality, prolonged hospital stays, emergency department visits, and readmissions) were analyzed. Results: The study cohort was mostly older (mean age 74 ± 10 years), male (98%), and diverse (55% non-Hispanic White). Survival analysis showed distinct mortality risks across clusters;while the 2 clusters with low FI had the longest survival, the high-high group had the lowest survival probability. Adjusted logistic regression indicated that the high-high cluster had over twice the odds of 1-year mortality compared to the low-low cluster (OR: 2.29;95% CI: 2.15-2.44). The high-high cluster also had significantly higher rates of prolonged hospital stays, emergency department visits, and readmissions at 30 and 90 days postdischarge. Conclusions: Integrating cross-sectional FI and longitudinal FT offers a comprehensive assessment of frailty in HF patients, improving risk stratification and disease management. © 2025

关键词： cluster analysis frailty heart failure patient outcome risk assessment

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ASS1 metabolically contributes to the nuclear and cytosolic p53-mediated DNA damage response (04 July, 10.1038/s42255-024-01090-z)

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NATURE METABOLISM 2024年第7期6卷 1417-1417页

作者： Lim, Lisha Qiu Jin Adler, Lital Hajaj, Emma Soria, Leandro R. Perry, Rotem Ben-Tov Darzi, Naama Brody, Ruchama Furth, Noa Lichtenstein, Michal Bab-Dinitz, Elizabeta Porat, Ziv Melman, Tevie Brandis, Alexander Malitsky, Sergey Itkin, Maxim Aylon, Yael Ben-Dor, Shifra Orr, Irit Pri-Or, Amir Seger, Rony Shaul, Yoav Ruppin, Eytan Oren, Moshe Perez, Minervo Meier, Jordan Brunetti-Pierri, Nicola Shema, Efrat Ulitsky, Igor Erez, Ayelet Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Department of Medicine D Beilinson Hospital Petah Tikva Israel Telethon Institute of Genetics and Medicine Pozzuoli Italy Department of Translational Medicine Medical Genetics University of Naples Federico II Naples Italy Scuola Superiore Meridionale (SSM School of Advanced Studies) Genomics and Experimental Medicine Program University of Naples Federico II Naples Italy Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot Israel Department of Molecular Neuroscience Weizmann Institute of Science Rehovot Israel Department of Biochemistry and Molecular Biology The Institute for Medical Research Israel-Canada Faculty of Medicine Hebrew University of Jerusalem Jerusalem Israel Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot Israel The De Botton Protein Profiling Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot Israel Cancer Data Science Lab Center for Cancer Research National Cancer Institute National Institute of Health Bethesda MD USA

Downregulation of the urea cycle enzyme argininosuccinate synthase (ASS1) in multiple tumors is associated with a poor prognosis partly because of the metabolic diversion of cytosolic aspartate for pyrimidine synthesis, supporting proliferation and mutagenesis owing to nucleotide imbalance. Here, we find that prolonged loss of ASS1 promotes DNA damage in colon cancer cells and fibroblasts from subjects with citrullinemia type I. Following acute induction of DNA damage with doxorubicin, ASS1 expression is elevated in the cytosol and the nucleus with at least a partial dependency on p53; ASS1 metabolically restrains cell cycle progression in the cytosol by restricting nucleotide synthesis. In the nucleus, ASS1 and ASL generate fumarate for the succination of SMARCC1, destabilizing the chromatin-remodeling complex SMARCC1–SNF5 to decrease gene transcription, specifically in a subset of the p53-regulated cell cycle genes. Thus, following DNA damage, ASS1 is part of the p53 network that pauses cell cycle progression, enabling genome maintenance and survival. Loss of ASS1 contributes to DNA damage and promotes cell cycle progression, likely contributing to cancer mutagenesis and, hence, adaptability potential.

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Identify Non-mutational p53 Functional Deficiency in Human cancers

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Genomics, Proteomics & Bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： cancer Composite expression DNA mutation Machine learning p53 deficiency

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis (vol 53, pg 403, 2021)

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NATURE GENETICS 2021年第6期53卷 935-935页

作者： Granja, Jeffrey M. Corces, M. Ryan Pierce, Sarah E. Bagdatli, S. Tansu Choudhry, Hani Chang, Howard Y. Greenleaf, William J. Department of Genetics Stanford University School of Medicine Stanford CA USA Program in Biophysics Stanford University Stanford CA USA Center for Personal Dynamic Regulomes Stanford University Stanford CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Gladstone Institute of Neurological Disease Gladstone Institute of Data Science and Biotechnology San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Program in Cancer Biology Stanford University School of Medicine Stanford CA USA Department of Biochemistry Faculty of Science Cancer and Mutagenesis Unit King Fahd Center for Medical Research King Abdulaziz University Jeddah Saudi Arabia Howard Hughes Medical Institute Stanford University Stanford CA USA Department of Applied Physics Stanford University Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://***/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

关键词： Epigenetics Epigenomics Gene regulation

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Fast and scalable image search for histology

arXiv

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arXiv 2021年

作者： Chen, Chengkuan Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Schaumberg, Andrew J. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambridgeMA United States Cancer Data Science Dana-Farber Cancer Institute BostonMA United States

The expanding adoption of digital pathology has enabled the curation of large repositories of histology whole slide images (WSIs), which contain a wealth of information. Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes and potential clinical trial success. A critical challenge in developing a WSI search and retrieval system is scalability, which is uniquely challenging given the need to search a growing number of slides that each can consist of billions of pixels and are several gigabytes in size. Such systems are typically slow and retrieval speed often scales with the size of the repository they search through, making their clinical adoption tedious and are not feasible for repositories that are constantly growing. Here we present Fast Image Search for Histopathology (FISH), a histology image search pipeline that is infinitely scalable and achieves constant search speed that is independent of the image database size, while being interpretable and without requiring detailed annotations. FISH uses self-supervised deep learning to encode meaningful representations from WSIs and a Van Emde Boas tree for fast search, followed by an uncertainty-based ranking algorithm to retrieve similar WSIs. We evaluated FISH on multiple tasks and datasets with over 22,000 patient cases spanning 56 disease subtypes. We additionally demonstrate that FISH can be used to assist with the diagnosis of rare cancer types where sufficient cases may not be available to train traditional supervised deep models. FISH is available as an easy-to-use, open source software package (https://***/mahmoodlab/FISH). © 2021, CC BY-NC-ND.

关键词： Diseases

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Factors Associated with Clinical Trial Participation in Adult Hematopoietic Stem Cell Transplantation (HSCT) Recipients: A Center for International Blood and Marrow Transplant Research (CIBMTR) Analysis

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Transplantation and Cellular Therapy 2025年第2期31卷 S493-S493页

作者： Gray, Tamryn Fowler, RN, PhD, MPH Brazauskas, Ruta, PhD Patel, Dr. Jinalben, MPH Rangarajan, Hemalatha, MD Michelis, Fotios V., MD, PhD Battiwalla, Minoo, MD, MS Lehmann, Leslie E., MD Stefanski, Dr. Heather E., MD, PhD El-Jawahri, Areej R., MD Saber, Wael School of Nursing UNC Chapel Hill Chapel Hill NC Division of Biostatistics Data Science Institute Medical College of Wisconsin Milwaukee WI CIBMTR® (Center for International Blood and Marrow Transplant Research) Department of Medicine Medical College of Wisconsin Milwaukee WI Department of Pediatric Hematology Oncology Blood and Marrow Transplantation Nationwide Children's Hospital Columbus OH Princess Margaret Cancer Centre Toronto ON Canada Sarah Cannon Transplant & Cellular Therapy Program at TriStar Centennial/HCA Healthcare Nashville TN Center for Stem Cell Transplantation Division of Hematologic Malignancy Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School Boston MA Department of Pediatrics DIvision of BMT and CT University of Minnesota Minneapolis MN Massachusetts General Hospital Boston MA

Topic Significance & Study Purpose/Background/RationaleOnly 3-5% of adults with cancer enroll in clinical trials nationwide. While clinical trial participation in solid tumors is well-studied, little is known about clinical trial participation in the context of HSCT. Methods, Intervention, & AnalysisRetrospective cohort study to: (1) describe clinical trial participation rates by HSCT type; (2) examine associations between patient, disease, and transplant-related characteristics and trial participation; and (3) explore the association between trial participation and overall survival (OS). Multivariable logistic and Cox regression models were used to examine associations and were stratified by transplant center and HSCT year to adjust for trial availability. Findings & InterpretationWe analyzed CIBMTR data collected between 2013 and 2019 that included 84,151 adult HSCT recipients (autologous: n=51,133; allogeneic: n=33,018). The proportion of patients who participated in a clinical trial was 16.2% in allogeneic HSCT and 2.8% in autologous HSCT. In allogeneic HSCT, older age (ages 60-69: OR = 1.92, p=0.0003), and age ≥ 70 (OR = 2.19, p<0.0001), Black race (OR = 1.35, p=0.0002), cord blood transplant (OR = 3.19, p<0.0001), MMUD (OR = 2.04, p=0.0017), intermediate disease risk (OR = 1.23, p = 0.007), a diagnosis of sickle cell disease (OR = 2.64, p <0.0001) were associated with higher odds of participation. In contrast, a diagnosis of severe aplastic anemia (OR = 0.32, p <0.0001), receipt of peripheral blood stem cells (OR = 0.46, P<0.0001) and haploidentical donors (OR= 0.49, P<0.0001) were associated with lower odds of participation. In autologous HCT, ≥3 comorbidities were associated with lower odds of participation (3: OR = 0.65, p = 0.0003; 4: OR = 0.54, p < 0.0001; 5≥: OR = 0.53, p <0.0001). Compared to Hodgkin disease, plasma cell disorders/MM (OR 1.74, p = 0.0009) had higher odds of participation. Clinical trial participation was not associated with OS for allog

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