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检索条件"机构=Cancer Data Science Program"
252 条 记 录,以下是21-30 订阅
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Suboptimal Engraftment and Toxicity after Post-Transplant Cyclophosphamide
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Transplantation and Cellular Therapy 2025年 第2期31卷 S141-S142页
作者: Scarpetti, Lauren, MD Kim, Haesook T., PhD Ikeda, Daniel Collier, Kerry Chung, Jooho DeFilipp, Zachariah, MD El-Jawahri, Areej R., MD Frigault, Matthew J., MD McAfee, Steven L., MD Newcomb, Richard Andrew, MD Spitzer, Thomas R, MD Westervelt, Peter O'Donnell, Paul Chen, Yi-Bin, MD Hematopoetic Cell Transplant & Cell Therapy Program Massachusetts General Hospital Boston MA Department of Data Science Dana-Farber Cancer Institute Harvard School of Public Health Boston MA Massachusetts General Hospital Boston MA Hematopoietic Cell Transplant & Cell Therapy Program Massachusetts General Hospital Boston MA Hematopoietic Cell Transplant and Cellular Therapy Program Massachusetts General Hospital Boston MA Medicine Division of Hematology & Oncology Massachusetts General Hospital Boston MA Blood and Marrow Transplant Program Massachusetts General Hospital Boston MA Hematopoietic Cell Transplant and Cell Therapy Program Massachusetts General Hospital Boston MA
BackgroundPost-transplant cyclophosphamide (PTCy) after hematopoietic cell transplantation (HCT) to prevent graft-versus-host disease (GVHD) is increasingly considered standard. We conducted a single-center retrospect...
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A randomized phase 2 study of neratinib with or without fulvestrant for patients with HER2-positive, estrogen receptor-positive metastatic breast cancer
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Clinical Breast cancer 2025年
作者: Stefania Morganti Xiangying Chu Tarah J. Ballinger Nisha Unni Sarah Sinclair Robert Wesolowski Alyssa M. Pereslete Paulina Lange Nabihah Tayob Nancy U. Lin Jose P. Leone Ian E. Krop Sara M. Tolaney Heather A. Parsons Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Breast Oncology Program Dana-Farber Brigham Cancer Center Boston MA Harvard Medical School Boston MA Broad Institute of MIT and Harvard Cambridge MA Data Science Dana-Farber Cancer Institute Boston MA Indiana University School of Medicine Indianapolis IN University of Texas Southwestern Medical Center Dallas TX Northern Light Cancer Care Brewer ME Department of Medicine The Ohio State University College of Medicine Columbus OH Florida International University Herbert Wertheim College of Medicine Miami FL Boston Medical Center Boston MA Yale Cancer Center New Haven CT
Background Most HER2-positive breast cancers co-express estrogen receptor (ER). Given crosstalk between HER2 and ER signaling pathways, dual blockade may be beneficial. Methods In this randomized, open-label, phase 2 ...
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Feasibility of Distress Thermometer Screening for Caregivers of Patients Undergoing Inpatient or Outpatient Hematopoietic Stem Cell Transplantation (HSCT)
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Transplantation and Cellular Therapy 2025年 第2期31卷 S493-S494页
作者: Gray, Tamryn Fowler, RN, PhD, MPH Heiling, Hillary, PhD Mazzola, Emanuele, PhD Amonoo, Hermioni Lokko, MD, MPP Sullivan, Lauren, MSN, RN, AGCNS-BC, OCN, BMTCN Kelkar, Amar H., MD, MPH, FACP Pirl, William, MD, MPH Hammer, Marilyn J. Tulsky, James A., MD El-Jawahri, Areej, M.D. Cutler, Corey S., MD MPH Partridge, Ann H., MD, MPH School of Nursing UNC Chapel Hill Chapel Hill NC Data Science Dana-Farber Cancer Institute Boston MA Dana-Farber Cancer Institute Boston MA Harvard Medical School Boston MA Commercial AbbVie North Chicago IL Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Psychosocial Oncology and Palliative Care Dana-Farber Cancer Institute Boston MA Supportive Oncology Dana-Farber Cancer Institute Boston MA Medicine Division of Hematology & Oncology Massachusetts General Hospital Boston MA Department of Medical Oncology Dana-Farber Cancer Institute and Harvard Medical School Boston MA Division of Transplantation and Cellular Therapy Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Stem Cell/Bone Marrow Transplantation Program Dana-Farber Cancer Institute Boston MA Division of Hematological Malignancies Department of Medical Oncology Dana Farber Cancer Institute Boston MA
Topic Significance & Study Purpose/Background/RationaleCaregivers of adults undergoing HSCT face considerable emotional challenges, yet few are screened for distress and limited data exists on whether caregiver di...
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A T cell resilience model associated with response to immunotherapy in multiple tumor types (May, 10.1038/s41591-022-01799-y, 2022)
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NATURE MEDICINE 2022年 第10期28卷 2219-2219页
作者: Zhang, Yu Vu, Trang Palmer, Douglas C. Kishton, Rigel J. Gong, Lanqi Huang, Jiao Nguyen, Thanh Chen, Zuojia Smith, Cari Livak, Ferenc Paul, Rohit Day, Chi-Ping Wu, Chuan Merlino, Glenn Aldape, Kenneth Guan, Xin-yuan Jiang, Peng Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Department of Clinical Oncology The University of Hong Kong Hong Kong China Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China Surgery Branch National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory of Pathology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Experimental Immunology Branch Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory Animal Science Program Leidos Biomedical Research Inc Frederick MD USA Flow Cytometry Core Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Office of the Director Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA
Despite breakthroughs in cancer immunotherapy, most tumor-reactive T cells cannot persist in solid tumors due to an immunosuppressive environment. We developed Tres (tumor-resilient T cell, https://***/), a computatio... 详细信息
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Core variables for real-world clinicogenomic data collection in precision oncology
ESMO Real World Data and Digital Oncology
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ESMO Real World data and Digital Oncology 2025年 7卷 100117-100117页
作者: R. Dienstmann A. Hackshaw J.-Y. Blay C. Le Tourneau OC Precision Medicina Oncoclínicas & Co São Paulo Brazil PhD Program Doctoral School University of Vic—Central University of Catalonia Barcelona Spain Oncology Data Science Vall d’Hebron Institute of Oncology Barcelona Spain Cancer Research UK and UCL Cancer Trials Centre University College London London UK Centre Léon Bérard and Université Claude Bernard Lyon France Institut Curie Department of Drug Development and Innovation (D3i) Paris-Saclay University Paris France
Background Precision oncology evidence gaps may be bridged using real-world clinicogenomic data; however, current limitations compromise real-world data collection and evidence generation. We aimed to define a set of ... 详细信息
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Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: Reflections and lessons learned
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Journal of Medical Imaging 2015年 第2期2卷
作者: Armato, Samuel G. Hadjiiski, Lubomir Tourassi, Georgia D. Drukker, Karen Giger, Maryellen L. Li, Feng Redmond, George Farahani, Keyvan Kirby, Justin S. Clarke, Laurence P. University of Chicago Department of Radiology MC 2026 5841 S. Maryland Avenue ChicagoIL60637 United States University of Michigan Department of Radiology 1500 E. Medical Center Drive Ann ArborMI48109 United States Biomedical Science and Engineering Center Health Data Sciences Institute Oak Ridge National Laboratory Oak RidgeTN37831 United States National Cancer Institute Division of Cancer Treatment and Diagnosis Cancer Imaging Program 9609 Medical Center Drive BethesdaMD20892 United States Frederick National Laboratory for Cancer Research Leidos Biomedical Research Inc Cancer Imaging Program FrederickMD21702 United States
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Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
Multimodal Co-Attention Transformer for Survival Prediction ...
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International Conference on Computer Vision (ICCV)
作者: Richard J. Chen Ming Y. Lu Wei-Hung Weng Tiffany Y. Chen Drew FK. Williamson Trevor Manz Maha Shady Faisal Mahmood Brigham and Women’s Hospital Harvard Medical School Cancer Program Broad Institute of Harvard and MIT Cancer Data Science Program Dana-Farber Cancer Institute Computer Science and Artificial Intelligence Laboratory MIT
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole sl... 详细信息
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Phase I Study of Ixazomib Added to Chemotherapy in the Treatment of Acute Lymphoblastic Leukemia in Older Adults
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BLOOD 2020年 136卷 41-42页
作者: Amrein, Philip C. Ballen, Karen K. Stevenson, Kristen E. Blonquist, Traci M. Brunner, Andrew M. Hobbs, Gabriela S. Hock, Hanno R. McAfee, Steven L. 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 Connolly, Christine Graubert, Timothy A. Neuberg, Donna S. Fathi, Amir T. Leukemia Center/MGH Cancer Center Massachusetts General Hospital Belmont MA Division of Hematology/Oncology University of Virginia Health Center Charlottesville VA Department of Data Science Dana-Farber Cancer Institute 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: While progress has been made in the treatment of childhood leukemia, the outlook for patients >60 years of age with acute lymphoblastic leukemia (ALL) is poor with complete remission rates (CR) of app...
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Choosing between HLA-Mismatched Unrelated and Haploidentical Donors: Donor Age Considerations
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Transplantation and Cellular Therapy 2025年 第2期31卷 S258-S259页
作者: Mehta, Rohtesh S., MD Wang, Tao, PhD Schmidt, Gabrielle, MPH Gadalla, Shahinaz M., MD, PhD Betts, Brian Christopher, MD Benjamin, Cara L., PhD Arrieta-Bolaños, Esteban, MQC, PhD Bolon, Yung-Tsi Lee, Stephanie J, MD, MPH Department of Stem Cell Transplantation and Cellular Therapy The University of Texas MD Anderson Cancer Center Houston TX CIBMTR® (Center for International Blood and Marrow Transplant Research) Medical College of Wisconsin Milwaukee WI Division of Biostatistics Data Science Institute Medical College of Wisconsin Milwaukee WI Center for International Blood and Marrow Transplant Research (CIBMTR) National Marrow Donor Program/Be The Match Minneapolis MN Clinical Genetics Branch; Division of Cancer Epidemiology and Genetics National Cancer Institute NIH Bethesda MD Roswell Park Comprehensive Cancer Center Buffalo NY Department of Medicine Univeristy of Miami Miller School of Medicine Miami FL Institute for Experimental Cellular Therapy University Hospital Essen Essen Düsseldorf Germany CIBMTR® (Center for International Blood and Marrow Transplant Research) NMDP Minneapolis MN Clinical Research Division Fred Hutchinson Cancer Center Seattle WA
BACKGROUNDHLA-mismatched unrelated donor (mmURD) and haploidentical donor (haplo) are the two most common mismatched donor options for hematopoietic cell transplantation (HCT). Substantial evidence suggests that donor...
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Whole slide images are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks
arXiv
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arXiv 2021年
作者: Chen, Richard J. Lu, Ming Y. Shaban, Muhammad Chen, Chengkuan Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Department of Biomedical Informatics Harvard Medical School Cancer Data Science Program Dana-Farber Cancer Institute Cancer Program Broad Institute of Harvard MIT
cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Despite the advancements made in weakly-s... 详细信息
来源: 评论