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Is Immediate Breast Reconstruction an Option for Elderly Women? A Comparative Study Between Elderly and Younger Population

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Clinical Breast Cancer 2025年第4期25卷 e440-e449页

作者： Marcadet, Julie Bouche, Caroline Arellano, Carlo Gauroy, Elodie Ung, Mony Jouve, Eva Selmes, Gabrielle Soule-Tholy, Marc Meresse, Thomas Massabeau, Carole Cavillon, Ana Vaysse, Charlotte Department of Oncological Surgery CHU Toulouse Toulouse France Department of Medical Oncology Claudius Regaud Institute Toulouse France Department of Oncological Surgery Claudius Regaud Institute Toulouse France Department of Radiotherapy Claudius Regaud Institute Toulouse France Biostatistics & Health Data Science Unit Claudius Regaud Institute Toulouse France

Objective: To evaluate the incidence of postoperative complications (POC) in elderly patients (EP) compared to younger patients (YP) following immediate breast reconstruction (IBR) after total mastectomy (TM). Methods: This retrospective study included patients treated at the Institut Universitaire of Cancer of Toulouse-Oncopole (IUCT-O) between January 2014 and May 2022. The primary outcome was the incidence of POC within 30 days postoperatively. Secondary outcomes included the delay before initiation of adjuvant treatments and re-hospitalization rates. Results: Elderly patients had a significantly higher rate of POC compared to younger patients, affecting 27.9% of EP and only 14.8% of YP. However, the severity of complications does not differ significantly between YP and EP (69.1% of major POC for YP and 64.7% for EP, P = .6680). Rates of re-hospitalization within 30 days between the 2 groups are similar (67.3% for YP and 61.8% for EP, P = .5962). Most importantly, these complications are not responsible for a delay in initiating adjuvant treatment compared with the younger population. Age ≥ 70 years and obesity (BMI ≥ 30) were identified as independent risk factors for POC. Conclusion: Despite a higher rate of POC, immediate breast reconstruction can be considered for elderly patients, but these patients should be carefully selected and assessed preoperatively to limit the risk of POC. © 2025 The Authors

关键词： Adjuvant treatment Breast cancer Elderly patients Postoperative complications Quality of life

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Validation of a revised instrument to assess implementation readiness of skin-to-skin care in acute or intensive care for neonates born with complex congenital heart disease

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Journal of Neonatal Nursing 2025年第1期31卷 129-134页

作者： Lisanti, Amy Jo Baxelbaum, Keith Kneeland, Tondi Bettencourt, Amanda Vittner, Dorothy University of Pennsylvania School of Nursing Department of Family and Community Health 418 Curie Blvd Philadelphia 19104 PA United States Children's Hospital of Philadelphia Research Institute 734 Schuylkill Ave Philadelphia 19146 PA United States Department of Biomedical and Health Informatics Data Science and Biostatistics Unit The Children's Hospital of Philadelphia Philadelphia PA United States The Ohio State University College of Nursing United States Fairfield University Egan School of Nursing and Health Studies Fairfield CT United States Connecticut Children's Hartford CT United States

An instrument measuring implementation readiness of skin-to-skin care for critically ill premature infants has been previously validated. The aim of this study was to adapt the instrument to the neonatal complex congenital heart disease population and perform initial validation for the revised instrument. A group of experts reviewed the original instrument and adapted items based on existing research and face validity. The revised instrument was distributed to nurses and healthcare professionals caring for neonates with complex congenital heart disease at an international conference and word of mouth from August to December 2023. A total of 158 nurses and 65 healthcare professionals completed the survey. Cronbach's alpha demonstrated strong internal consistency (α = 0.96, 95%CI = 0.94–0.97). Exploratory factor analysis revealed a seven-factor solution provided the strongest fit. This instrument may serve as a useful tool for nurses aiming to enhance the uptake of skin-to-skin care for neonates with complex congenital heart disease. © 2024 Neonatal Nurses Association

关键词： Congenital heart disease Infants Instrument Skin-to-skin care

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Resource Utilization in Adolescent and Adult Cardiomyopathy and Heart Failure

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The Journal of Heart and Lung Transplantation 2025年第4期44卷 S637-S637页

作者： A.B. Ludomirsky W. Russell K. Lin M. O'Connor C. Wittlieb-Weber H. Ahmed N. Reza T. Bittermann A. Dewitt H. Griffis X. Zhang Y. Huang J. Rossano J. Edelson Department of Pediatrics Children's Hospital of Philadelphia Philadelphia PA Department of Internal Medicine Hospital of the University of Pennsylvania Philadelphia PA Department of Anesthesiology and Critical Care Medicine Children's Hospital of Philadelphia Philadelphia PA Department of Biomedical Health Informatics Data Science and Biostatistics Unit University of Pennsylvania Philadelphia PA Department of Biomedical Health Informatics Data Science and Biostatistics Unit Children's Hospital of Philadelphia Philadelphia PA

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A Workflow for Causal Discovery Under Evolving Mechanisms

SSRN

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SSRN 2025年

作者： Rocchi, Niccolò Zanga, Alessio Bernasconi, Alice Gronchi, Alessandro Callegaro, Dario Borghi, Alessandra Casali, Paolo Giovanni Provenzano, Salvatore Miceli, Rosalba Trama, Annalisa Stella, Fabio University of Milano-Bicocca Viale Sarca 336 Milan20126 Italy Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Grenzacherstrasse 124 Basel4070 Switzerland Department of Surgery Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy Department of Cancer Medicine Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy Biostatistics for Clinical Research Unit Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy

Objective: Causal networks are gaining significant attention in healthcare. They sup-port physicians by offering a mechanistic view of the phenomena under investigation, thusenabling personalized, explainable, and optimal decisions. However, developing causalnetworks is complex, and the problem becomes even more evident when dealing withrare diseases. Observational data and events are scarce here, medical knowledge aloneis often insufficient to obtain an effective model, and disease mechanisms may evolveover time. In this paper, we design, develop, and evaluate a collaborative procedure forlearning causal relationships in constrained-resources ***: We devise a workflow that iteratively learns and refines causal networks’structures over different time windows. The workflow leverages a novel pre-processingalgorithm for survival data, characterized by longitudinal data measuring specific events’occurrence and timing. Our methodology takes advantage of the continuous interactionwith domain experts, who can provide insights into the temporal evolution of causalmechanisms. We evaluate the proposed framework on soft tissue sarcoma data, a classof rare ***: We obtain three different networks, related to three overlapping time *** our knowledge, this is the first cause-effect representation of the natural history ofsoft tissue sarcoma. We assessed the results with clinical experts, collecting key insightsinto the disease’s ***: The proposed methodology paves the way to standardized, replicable, andagile causal discovery in all situations affected by sparse data, longitudinal dynamics,and heterogeneous expert knowledge. Results on soft tissue sarcoma may enable moreeffective and personalized clinical decisions. © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Randomization-Based Inference for MCP-Mod

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Statistics in Medicine 2025年第1-12期44卷

作者： Lukas Pin Oleksandr Sverdlov Frank Bretz Björn Bornkamp MRC Biostatistics Unit University of Cambridge Cambridge UK Early Development Analytics Novartis Pharmaceuticals Corporation East Hanover NJ USA Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland Center for Medical Data Science Institute for Medical Statistics Medical University of Vienna Vienna Austria

Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient's safety of a drug. The Generalized Multiple Comparison Procedures and Modeling approach is commonly used in Phase II trials for testing and estimation of dose-response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non existence of estimates. Motivated by an actual clinical trial using the MCP-Mod approach, this paper introduces penalized maximum likelihood estimation (MLE) and randomization-based inference techniques to address these challenges. Randomization-based inference allows for exact finite sample inference, while population-based inference for MCP-Mod typically relies on asymptotic approximations. Simulation studies demonstrate that randomization-based tests can enhance statistical power in small to medium-sized samples while maintaining control over type-I error rates, even in the presence of time trends. Our results show that residual-based randomization tests using penalized MLEs not only improve computational efficiency but also outperform standard randomization-based methods, making them an adequate choice for dose-finding analyses within the MCP-Mod framework. Additionally, we apply these methods to pharmacometric settings, demonstrating their effectiveness in such scenarios. The results in this paper underscore the potential of randomization-based inference for the analysis of dose-finding trials, particularly in small sample contexts.

关键词： dose finding finite sample inference multiple testing penalized maximum likelihood estimation randomization test time trends

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Histo–blood group ABO system transferase plasma levels and risk of future venous thromboembolism: the HUNT study

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Blood 2025年第22期145卷 2656.0-2665.0页

作者： Onsaker, Asbjørn L. Arntzen, Anna Y. Trégouët, David-Alexandre Nøst, Therese H. Tang, Weihong Guan, Weihua Jonasson, Christian Morange, Pierre-Emmanuel Hindberg, Kristian D. Folsom, Aaron R. Hveem, Kristian Morelli, Vânia M. Hansen, John-Bjarne Thrombosis Research Group Department of Clinical Medicine UiT–the Arctic University of Norway Tromsø Norway University of Bordeaux INSERM Bordeaux Population Health Research Center Unité Mixte de Recherche 1219 ELEANOR Bordeaux France HUNT Center for Molecular and Clinical Epidemiology Norwegian University of Science and Technology Trondheim Norway Levanger Hospital Nord-Trøndelag Hospital Trust Levanger Norway Department of Community Medicine UiT–the Arctic University of Norway Tromsø Norway HUNT Research Center Levanger Norway Division of Epidemiology and Community Health University of Minnesota School of Public Health Minneapolis MN United States Division of Biostatistics and Health Data Science University of Minnesota School of Public Health Minneapolis MN United States Aix-Marseille Univ INSERM Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement Centre de Recherche en CardioVasculaire et Nutrition Laboratory of Haematology Centre de Ressources Biologiques Assistance Publique–Hôpitaux de Marseille HemoVasc Marseille France Thrombosis Research Center Division of Internal Medicine University Hospital of North Norway Tromsø Norway

The non-O blood group is a well-established risk factor for venous thromboembolism (VTE). However, the association between plasma levels of the histo–blood group ABO system transferase (BGAT), the gene product of the ABO locus, and VTE risk remains unclear. We aimed to investigate the association between plasma BGAT levels and risk of future VTE, and whether this relationship was mediated by plasma von Willebrand factor (VWF) or coagulation factor VIII (FVIII), as VWF is glycosylated by BGAT. Incident VTE-cases (n = 294) and a randomly sampled age- and-sex-weighted subcohort (n = 1066) were derived from the third survey of the Trøndelag Health Study. Baseline plasma samples (2006-2008) were subjected to the SomaScan aptamer-based-7K platform for protein measurements. Weighted Cox regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) across BGAT quartiles. We found that ABO haplotypes (A1/A2/B/O1/O2) explained ≈80% of the BGAT plasma variability. Participants with BGAT levels in the highest quartile had 2-fold higher VTE risk (HR, 2.12;95% CI, 1.39-3.22) compared with those with BGAT in the lowest quartile in age-, sex-, and sample batch–adjusted models. The associations were particularly pronounced for unprovoked VTE (HR, 3.71;95% CI, 1.79-7.67) and deep vein thrombosis (HR, 3.28;95% CI, 1.63-6.59). The HRs were similar after further adjustment for body mass index, C-reactive protein, and estimated glomerular filtration rate, and moderately attenuated when adding VWF or FVIII plasma levels to the models. Our findings indicate that elevated BGAT plasma levels are associated with increased risk of future VTE beyond what is explained by VWF and FVIII. © 2025 American Society of Hematology

关键词： ABO blood group histo-blood group ABO system transferase venous thromboembolism von Willebrand factor coagulation factor VIII

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Context is key: New insights on barriers and facilitators influencing the implementation of skin-to-skin care in the pediatric cardiac intensive care unit

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Journal of Neonatal Nursing 2025年第3期31卷

作者： Lisanti, Amy Jo Bettencourt, Amanda Kneeland, Tondi Vittner, Dorothy Baxelbaum, Keith University of Pennsylvania School of Nursing Department of Family and Community Health 418 Curie Blvd Philadelphia 19104 PA United States Children's Hospital of Philadelphia Research Institute 734 Schuylkill Ave Philadelphia 19146 PA United States The Ohio State University College of Nursing United States Fairfield University Egan School of Nursing and Health Studies Fairfield CT United States Connecticut Children's Hartford CT United States Department of Biomedical and Health Informatics Data Science and Biostatistics Unit The Children's Hospital of Philadelphia Philadelphia PA United States

Skin-to-skin care (SSC) is an underutilized intervention for neonates born with congenital heart disease (CHD) in the acute/critical care setting. The aim of this study was to explore contextual factors influencing the implementation of SSC for neonates with CHD. This study was a secondary analysis of complete survey data from 223 interdisciplinary healthcare professionals collected via convenience sampling. The survey included valid and reliable measures of implementation outcomes. Scores for all outcomes were compared across pre-identified variables of interest. Comparisons included t-tests for predictors with binary values and simple linear regressions when the predictor was continuous. Relevant recent experience appears to be a strong facilitator for SSC implementation. Both general education level and lack of specific knowledge on unit-based policies for SSC may be factors that could be addressed by education. Future research could target factors identified in this study to improve the implementation of SSC for neonates with CHD. © 2025

关键词： Congenital heart disease Implementation Infants Skin-to-skin care

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Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood

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American Journal of Human Genetics 2025年第2期112卷 276-290页

作者： Jakubek, Yasminka A. Ma, Xiaolong Stilp, Adrienne M. Yu, Fulong Bacon, Jason Wong, Justin W. Aguet, Francois Ardlie, Kristin Arnett, Donna K. Barnes, Kathleen Bis, Joshua C. Blackwell, Tom Becker, Lewis C. Boerwinkle, Eric Bowler, Russell P. Budoff, Matthew J. Carson, April P. Chen, Jiawen Cho, Michael H. Coresh, Josef Cox, Nancy J. de Vries, Paul S. DeMeo, Dawn L. Fardo, David W. Fornage, Myriam Guo, Xiuqing Hall, Michael E. Heard-Costa, Nancy Hidalgo, Bertha Irvin, Marguerite Ryan Johnson, Andrew D. Jorgenson, Eric Kenny, Eimear E. Kessler, Michael D. Levy, Daniel Li, Yun Lima, Joao A.C. Liu, Yongmei Locke, Adam E. Loos, Ruth J.F. Machiela, Mitchell J. Mathias, Rasika A. Mitchell, Braxton D. Murabito, Joanne M. Mychaleckyj, Josyf C. North, Kari E. Orchard, Peter Parker, Stephen C.J. Pershad, Yash Peyser, Patricia A. Pratte, Katherine A. Psaty, Bruce M. Raffield, Laura M. Redline, Susan Rich, Stephen S. Rotter, Jerome I. Shah, Sanjiv J. Smith, Jennifer A. Smith, Aaron P. Smith, Albert Taub, Margaret A. Tiwari, Hemant K. Tracy, Russell Tuftin, Bjoernar Bick, Alexander G. Sankaran, Vijay G. Reiner, Alexander P. Scheet, Paul Auer, Paul L. Department of Internal Medicine University of Kentucky Lexington KY United States Division of Biostatistics Data Science Institute Medical College of Wisconsin Milwaukee WI United States Department of Biostatistics University of Washington Seattle WA United States Division of Hematology/Oncology Boston Children's Hospital Harvard Medical School Boston MA United States Department of Pediatric Oncology Dana-Farber Cancer Institute Harvard Medical School Boston MA United States Broad Institute of MIT and Harvard Cambridge MA United States GMU-GIBH Joint School of Life Sciences The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases Guangzhou National Laboratory Guangzhou Medical University Guangzhou China Department of Computer Science Department of Biological Sciences University of Wisconsin Milwaukee Milwaukee WI United States Department of Epidemiology University of Texas MD Anderson Cancer Center Houston TX United States Broad Institute Cambridge MA United States University of South Carolina Columbia SC United States Division of Biomedical Informatics and Personalized Medicine School of Medicine School of Medicine University of Colorado Anschutz Medical Campus Aurora CO United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States Department of Biostatistics University of Michigan Ann Arbor MI United States Department of Medicine Division of Cardiology Johns Hopkins Hospital Johns Hopkins University of Medicine Baltimore MD United States Human Genetics Center Department of Epidemiology School of Public Health The University of Texas Health Science Center at Houston Houston TX United States Department of Medicine National Jewish Health Denver CO United States Department of Medicine Division of Cardiology The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center Torrance CA United States Department of Medicine University of

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole-genome sequencing (WGS) of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. We show that haplotype-based calling methods can be used with WGS data to successfully identify mLOY events. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European (EUR) ancestry group compared to other ancestries. We identify multiple loci associated with mLOY susceptibility and show that subsets of human hematopoietic stem cells are enriched for the activity of mLOY susceptibility variants. Finally, we found that certain alleles on chromosome Y are more likely to be lost than others in detectable mLOY clones. © 2024 The Author(s)

关键词： GWAS mosaic loss of chromosome Y multi-ancestry

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Impact and safety of pregnancy on desmoid fibromatosis management in the era of active surveillance. An international multicenter retrospective observational study

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European Journal of Cancer 2025年 222卷 115474-115474页

作者： Fiore, Marco Ljevar, Silva Raut, Chandraijt Premanand Personeni, Giulia Rabih, Mikhael Gladdy, Rebecca Mercier, Kelly Sulciner, Megan Rossi, Enrica Tzanis, Dimitri Suraweera, Harini Colombo, Chiara Coppola, Marianna Bonvalot, Sylvie Iadecola, Sara Sarre-Lazcano, Catherine Figura, Costanza Salvatore, Daniela Miceli, Rosalba Gronchi, Alessandro Sarcoma Service Department of Surgery Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Unit of Biostatistics for Clinical Research Department Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Brigham and Women's Hospital Boston MA United States Dana-Farber Cancer Institute Boston MA United States Harvard Medical School Boston MA United States Desmoid Foundation Italy Milan Italy Institut Curie Paris France Division of Surgical Oncology Mount Sinai and Princess Margaret Cancer Center University of Toronto Canada Desmoid Tumor Research Foundation Woodcliff Lake NJ United States Duke University Durham NC United States

Aim: To evaluate desmoid fibromatosis (DF) progression and obstetric outcomes during and after pregnancy, providing evidence to inform clinical decision-making and counseling. Methods: This international, multicenter, retrospective observational study included data from 157 women with DF, contributing 177 pregnancies. Women were classified into three groups: DF diagnosed during pregnancy (Group A), pregnancy occurring after the diagnosis of DF (Group B: DF in situ during pregnancy), or pregnancy in previously resected DF (Group C). Logistic regression and tumor size trend analyses were conducted. Results: Among 177 pregnancies, Group A exhibited the highest rates of DF progression (68.7 % during pregnancy, 40.6 % postpartum), while Groups B and C had lower progression rates (9.5 % during pregnancy, 8.3 % postpartum). Active treatment was required in 5.6 % of cases. Spontaneous regression occurred in 23.7 % of pregnancies, particularly following progression. Obstetric complications were comparable to those in the general population. Conclusion: Pregnancy is generally safe for women with DF, particularly after prolonged disease stability. Individualized counseling is essential for managing risks of progression and recurrence, supporting informed fertility decisions. Referral to specialized centers is recommended to optimize DF management during family planning and pregnancy. © 2025 Elsevier Ltd

关键词： Aggressive fibromatosis Desmoid tumor Desmoid-type fibromatosis Pregnancy Soft tissue sarcoma

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