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Disparities in stage at diagnosis for liver cancer in China

Journal of the National cancer Center 2023年第1期3卷 7-13页

作者： Tianhao Shan Xianhui Ran Huizhang Li Guoshuang Feng Siwei Zhang Xuehong Zhang Lei Zhang Lingeng Lu Lan An Ruiying Fu Kexin Sun Shaoming Wang Ru Chen Li Li Wanqing Chen Wenqiang Wei Hongmei Zeng Jie He National Central Cancer Registry National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Cancer Hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital Department of Cancer PreventionInstitute of Cancer Research and Basic Medical SciencesChinese Academy of SciencesHangzhouChina Big Data Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Channing Division of Network Medicine Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA Department of Nutrition T.H.Chan School of Public HealthHarvard UniversityBostonUSA China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’anChina Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health CentreAlfred HealthMelbourneAustralia Central Clinical School Faculty of MedicineMonash UniversityMelbourneAustralia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhouChina Department of Chronic Disease Epidemiology Yale School of Public HealthYale Cancer CenterNew HavenUSA Department of Thoracic Surgery National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

Background:The stage at diagnosis is a major factor in making treatment strategies and cancer control ***,the stage distribution for liver cancer in China was not well *** this multi-center hospital-based study,we aimed to identify the distribution and factors associated with stage at diagnosis for liver cancer in ***:We included patients diagnosed with primary liver cancer in 13 hospitals of 10 provinces covering various geographic and socioeconomic populations during 2016-2017 in *** stage distribution overall,and by sex and age at diagnosis were *** used logistic regression to identify the factors associated with stage Ⅲ-Ⅳ *** further compared these estimates with data from the ***:We included 2,991 patients with known stage at diagnosis in *** proportion of patients diagnosed with stageⅠ,Ⅱ,Ⅲ,and Ⅳ was 17.5%,25.6%,29.3%,and 27.6%,*** proportion of stage Ⅲ-Ⅳ cases was higher in women[65.1%vs 54.9%,adjusted odds ratio(OR)=1.5,95%CI:1.2,1.8]and those≥60 years(61.6%vs 52.8%,OR=1.4,95%CI:1.2,1.6).We found an increased risk of stage Ⅲ-Ⅳ among drinkers and those without a family history of *** to the USA,our study population had a substantially higher proportion of stage Ⅲ-Ⅳ cases(56.9%vs 45.6%).Conclusion:The disparities in liver cancer stage at diagnosis among different populations within China,and between China and the USA,imply the necessity for improving cancer awareness and early detection for liver cancer in China.

关键词： Liver cancer Stage at diagnosis Disparity China USA

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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

ISBN: (纸本)9781665428132

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 slide images (WSIs). Despite recent progress in formulating WSIs as bags for multiple instance learning (MIL), representation learning of entire WSIs remains an open and challenging problem, especially in overcoming: 1) the computational complexity of feature aggregation in large bags, and 2) the data heterogeneity gap in incorporating biological priors such as genomic measurements. In this work, we present a Multimodal Co-Attention Transformer (MCAT) framework that learns an interpretable, dense co-attention mapping between WSIs and genomic features formulated in an embedding space. Inspired by approaches in Visual Question Answering (VQA) that can attribute how word embed-dings attend to salient objects in an image when answering a question, MCAT learns how histology patches attend to genes when predicting patient survival. In addition to visualizing multimodal interactions, our co-attention trans-formation also reduces the space complexity of WSI bags, which enables the adaptation of Transformer layers as a general encoder backbone in MIL. We apply our proposed method on five different cancer datasets (4,730 WSIs, 67 million patches). Our experimental results demonstrate that the proposed method consistently achieves superior performance compared to the state-of-the-art methods.

关键词： Representation learning Visualization Histopathology Genomics Predictive models Transformers Biological information theory

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Population simulation modeling of disparities in US breast cancer mortality

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Journal of the National cancer Institute - Monographs 2023年第62期2023卷 178-187页

作者： Mandelblatt, Jeanne S. Schechter, Clyde B. Stout, Natasha K. Huang, Hui Stein, Sarah Hunter Chapman, Christina Trentham-Dietz, Amy Jayasekera, Jinani Gangnon, Ronald E. Hampton, John M. Abraham, Linn O’Meara, Ellen S. Sheppard, Vanessa B. Lee, Sandra J. Department of Oncology Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center WA United States Departments of Family and Social Medicine and of Epidemiology and Population Health Albert Einstein College of Medicine Bronx NY United States Department of Population Sciences Harvard Medical School and Harvard Pilgrim Health Care Institute Boston MA United States Department of Data Science Dana-Farber Cancer Institute and Harvard Medical School Boston MA United States Department of Radiation Oncology Section of Health Services Research Baylor College of Medicine and Health Policy Quality and Informatics Program at the Center for Innovations in Quality Effectiveness and Safety Michael E. DeBakey VA Medical Center Houston TX United States Department of Population Health Sciences and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Health Equity and Decision Sciences Research Lab National Institute on Minority Health and Health Disparities Intramural Research Program National Institutes of Health Bethesda MD United States Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Kaiser Permanente Washington Health Research Institute Seattle WA United States Department of Health Behavior and Policy and Massey Cancer Center Virginia Commonwealth University Richmond VA United States

BACKGROUND: Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. METHODS: Three cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. RESULTS: Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. CONCLUSION: Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***

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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-supervised deep learning, many approaches are not context-aware and are unable to model important morphological feature interactions between cell identities and tissue types that are prognostic for patient survival. In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five different cancer types from the cancer Genome Atlas (TCGA), and demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%. Our code and corresponding models are publicly available at https://***/mahmoodlab/Patch-GCN. Copyright © 2021, The Authors. All rights reserved.

关键词： Pathology

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Low rates of chronic graft-versus-host disease with ruxolitinib maintenance following allogeneic HCT

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Blood 2025年第20期145卷 2312-2316页

作者： DeFilipp, Zachariah Kim, Haesook T. Knight, Laura W. Dhaver, Shilton E. White, Meghan Dholaria, Bhagirathbhai Schroeder, Mark A. Vasu, Sumithira Abedin, Sameem Chung, Jooho El-Jawahri, Areej Frigault, Matthew J. McAfee, Steven Newcomb, Richard A. Spitzer, Thomas R. Chen, Yi-Bin Hobbs, Gabriela S. Hematopoietic Cell Transplant and Cellular Therapy Program Massachusetts General Hospital Boston MA United States Department of Data Science Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health Boston MA United States Cancer Center Protocol Office Massachusetts General Hospital Boston MA United States Department of Hematology-Oncology and Stem Cell Transplantation Vanderbilt University Medical Center Nashville TN United States Bone Marrow Transplantation and Leukemia Section Division of Oncology Washington University School of Medicine in Saint Louis Saint Louis MO United States Division of Hematology Department of Internal Medicine The Ohio State University Columbus OH United States Division of Hematology/Oncology Department of Medicine Medical College of Wisconsin Milwaukee WI United States Center for Leukemia Massachusetts General Hospital Boston MA United States

Despite recent advances in graft-versus-host disease (GVHD) prophylaxis, novel approaches to effective prevention of chronic GVHD (cGVHD) remain of high importance. In this prospective, multicenter, phase 2 trial, ruxolitinib, an oral inhibitor of Janus kinase (JAK) 1 and 2, was administered as maintenance therapy after reduced-intensity allogeneic hematopoietic cell transplantation (HCT). GVHD prophylaxis consisted of tacrolimus and methotrexate. Ruxolitinib began between day +30 to 100 and was administered continuously in 28-day cycles for up to 24 cycles. Seventy-eight participants were enrolled before HCT;63 participants received the intervention. The median start date of ruxolitinib after HCT was day +45. The most common grade ≥3 adverse events were neutropenia, thrombocytopenia, and anemia. Seven participants experienced grade ≥3 infectious events. GVHD-free, relapse-free survival at 1 year after HCT, the primary end point, was 70%. Grade 3 to 4 acute GVHD at 6 months was 4.8%, and moderate-severe cGVHD at 2 years was 16%. cGVHD requiring systemic therapy was 9.5% at 1 year and 13% at 2 years. Overall survival and progression-free survival at 2 years were 76% and 68%, respectively. Prolonged administration of ruxolitinib following HCT is associated with low rates of clinically significant cGVHD. The incorporation of JAK inhibition into GVHD prevention approaches warrants further investigation. This trial was registered at *** as #NCT03286530. © 2025 American Society of Hematology

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data gaps and opportunities for modeling cancer health equity

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Journal of the National cancer Institute - Monographs 2023年第62期2023卷 246-254页

作者： Trentham-Dietz, Amy Corley, Douglas A. Del Vecchio, Natalie J. Greenlee, Robert T. Haas, Jennifer S. Hubbard, Rebecca A. Hughes, Amy E. Kim, Jane J. Kobrin, Sarah Li, Christopher I. Meza, Rafael Neslund-Dudas, Christine M. Tiro, Jasmin A. Department of Population Health Sciences and Carbone Cancer Center School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States Division of Research Kaiser Permanente Northern California Oakland CA United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA United States Marshfield Clinic Research Institute Marshfield WI United States Division of General Internal Medicine Massachusetts General Hospital Boston MA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Population and Data Sciences University of Texas Southwestern Medical Center Dallas TX United States Department of Health Policy and Management Center for Health Decision Science Harvard T.H. Chan School of Public Health Boston MA United States Healthcare Delivery Research Program Division of Cancer Control & Population Sciences National Cancer Institute National Institutes of Health Rockville MD United States Department of Integrative Oncology British Columbia (BC) Cancer Research Institute Vancouver BC Canada Department of Public Health Sciences and Henry Ford Cancer Henry Ford Health Detroit MI United States Department of Public Health Sciences University of Chicago Biological Sciences Division University of Chicago Medicine Comprehensive Cancer Center Chicago IL United States

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***@***.

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Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Embedding Space Augmentation for Weakly Supervised Learning ...

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IEEE International Symposium on Biomedical Imaging

作者： Imaad Zaffar Guillaume Jaume Nasir Rajpoot Faisal Mahmood Department of Computer Science University College London UK Department of Pathology Harvard Medical School Brigham and Women’s Hospital Boston MA USA Department of Pathology Harvard Medical School Massachusetts General Hospital Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Computer Science Tissue Image Analytics Centre University of Warwick Coventry UK Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry UK The Alan Turing Institute London UK

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL-based analytical pipelines for WSI-level analysis, the WSIs are divided into patches, and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs’ limited RAM. Because of this bottleneck, incorporating patch-level data augmentations during training adds an extra computational burden. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster.

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The CD8α hinge is intrinsically disordered with a dynamic exchange that includes proline cis-trans isomerization

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Journal of Magnetic Resonance 2022年 340卷 107234页

作者： Chen, Xiang Mirazee, Justin M. Skorupka, Katarzyna A. Matsuo, Hiroshi Youkharibache, Philippe Taylor, Naomi Walters, Kylie J. Center for Structural Biology Center for Cancer Research National Cancer Institute National Institutes of Health FrederickMD21702 United States Pediatric Oncology Branch Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20814 United States Laboratory of Integrative Cancer Immunology Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20894 United States Johns Hopkins University Department of Biology 3400 N. Charles Street BaltimoreMD21218 United States Basic Science Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20894 United States

T cells engineered to express artificial chimeric antigen receptors (CARs) that selectively target tumor-specific antigens or deleterious cell types offer transformative therapeutic possibilities. CARs contain an N-terminal extracellular antigen recognition domain, C-terminal intracellular signal transduction domains, and connecting hinge and transmembrane regions, each of which have been varied to optimize targeting and minimize toxicity. We find that a CD22-targeting CAR harboring a CD8α hinge (H) exhibits greater cytotoxicity against a low antigen density CD22+ leukemia as compared to an equivalent CAR with a CD28 H. We therefore studied the biophysical and dynamic properties of the CD8α H by nuclear magnetic resonance (NMR) spectroscopy. We find that a large region of the CD8α H undergoes dynamic chemical exchange between distinct and observable states. This exchanging region contains proline residues dispersed throughout the sequence that undergo cis-trans isomerization. Up to four signals of differing intensity are observed, with the most abundantly populated being intrinsically disordered and with all prolines in the trans isomerization state. The lesser populated states all contain cis prolines and evidence of local structural motifs. Altogether, our data suggest that the CD8α H lacks long-range structural order but has local structural motifs that transiently exchange with a dominant disordered state. We propose that structural plasticity and local structural motifs promoted by cis proline states within the CD8α H are important for relaying and amplifying antigen-binding effects to the transmembrane and signal transduction domains. © 2022

关键词： T-cells

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Predicting drug response through tumor deconvolution by cancer cell lines

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Patterns 2024年第4期5卷 100949页

作者： Hsu, Yu-Ching Chiu, Yu-Chiao Lu, Tzu-Pin Hsiao, Tzu-Hung Chen, Yidong Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei115 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei100 Taiwan Greehey Children's Cancer Research Institute University of Texas Health San Antonio San AntonioTX78229 United States Department of Medicine School of Medicine University of Pittsburgh PittsburghPA15261 United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA15232 United States Department of Medical Research Taichung Veterans General Hospital Taichung40705 Taiwan Department of Population Health Sciences University of Texas Health San Antonio San AntonioTX78229 United States

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing. © 2024 The Authors

关键词： Tumors

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A Foundational Multimodal Vision Language AI Assistant for Human Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Ikamura, Kenji Gerber, Georg Liang, Ivy Phi Le, Long Ding, Tong Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The field of computational pathology has witnessed remarkable progress in the development of both taskspecific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology using an in-house developed foundational vision encoder pretrained on 100 million histology images from over 100,000 patient cases and 1.18 million pathology image-caption pairs. The vision encoder is then combined with a pretrained large language model and the whole system is finetuned on over 250,000 diverse disease agnostic visual language instructions. We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4. When relevant clinical context is provided with the histology image, PathChat achieved a diagnostic accuracy of 87% on multiple-choice questions based on publicly available cases of diverse tissue origins and disease models. Additionally, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision language AI assistant that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making. © 2023, CC BY-NC-ND.

关键词： Pathology

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