Chrome Extension
WeChat Mini Program
Use on ChatGLM
VIPCreated with Sketch.

    • Lung Cancer Screening and Incidental Findings: A Research Agenda: an Official American Thoracic Society Research Statement.

    Louise M Henderson, Roger Y Kim,Nichole T Tanner,Emily B Tsai,Abbie Begnaud,Farouk Dako,Michael Gieske,Kimberly Kallianos,Ilana Richman,Lori C Sakoda, Ronald G Schwartz,Joseph Yeboah,Kwun M Fong,Stephen Lam,Pyng Lee,Mary Pasquinelli,Robert A Smith,Matthew Triplette,Lynn T Tanoue,M Patricia Rivera

    American journal of respiratory and critical care medicine（2025）

    Medical University of South Carolina | Stanford University | University of Pennsylvania | St Elizabeth Healthcare | University of California San Francisco | Yale University | Kaiser Permanente Northern California | University of Rochester Medical Center | Wake Forest Baptist Medical Center | Institute of Heart and Lung Health | National University Health System | University of Illinois Chicago | American Cancer Society | Yale University Medical School | URMC

    Cited 0|Views2
    Abstract
    Background: Lung cancer screening with low-dose computed tomography (LDCT) may uncover incidental findings (IFs) unrelated to lung cancer. There may be potential benefits from identifying clinically significant IFs that warrant intervention and potential harms related to identifying IFs that are not clinically significant but may result in additional evaluation, clinician effort, patient anxiety, complications, and excess cost. Objectives: To identify knowledge and research gaps and develop and prioritize research questions to address the approach to and management of IFs. Methods: We convened a multidisciplinary panel to review the available literature on IFs detected in lung cancer screening LDCT examinations, focusing on variability and standardizing reporting, management of IFs, and evaluation of the benefits and harms of IFs, particularly cardiovascular-related IFs. We used a three-round modified Delphi process to prioritize research questions. Results: This statement identifies knowledge gaps in 1) reporting of IFs, 2) management of IFs, and 3) identifying and reporting coronary artery calcification found on lung cancer screening LDCT. Finally, we present the panel's initial 36 research questions and the final 20 prioritized questions. Conclusions: This statement provides a prioritized research agenda to further efforts focused on evaluating, managing, and increasing awareness of IFs in lung cancer screening.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    【要点】：本研究旨在识别并优先排序关于肺癌筛查中意外发现（IFs）的知识和研究空白，提出管理这些发现的研究问题。
    

    【方法】：通过召集跨学科小组回顾现有关于肺癌筛查LDCT检查中IFs的文献，并采用三阶段的修改版德尔菲过程来优先排序研究问题。
    

    【实验】：无具体实验描述，使用的数据集未提及。研究结果是提出了36个初始研究问题和最终20个优先级研究问题。