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  1. Article ; Online: Teach the Mentor: A Six-Session Program Universally Improves Mentorship Skills Among a Diverse Group of Radiology Faculty.

    Milch, Hannah S / Luhar, Aarti / Manning, Brian / Aberle, Denise R / Sayre, James / Moriarty, John M

    Journal of the American College of Radiology : JACR

    2024  

    Language English
    Publishing date 2024-01-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2024.01.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer.

    Smedley, Nova F / Aberle, Denise R / Hsu, William

    Journal of medical imaging (Bellingham, Wash.)

    2021  Volume 8, Issue 3, Page(s) 31906

    Abstract: ... ...

    Abstract Purpose
    Language English
    Publishing date 2021-05-08
    Publishing country United States
    Document type Journal Article
    ISSN 2329-4302
    ISSN 2329-4302
    DOI 10.1117/1.JMI.8.3.031906
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: MILD trial, strong confirmation of lung cancer screening efficacy.

    Schabath, Matthew B / Aberle, Denise R

    Nature reviews. Clinical oncology

    2019  Volume 16, Issue 9, Page(s) 529–530

    MeSH term(s) Early Detection of Cancer ; Humans ; Lung Neoplasms ; Mass Screening ; Tomography, X-Ray Computed
    Language English
    Publishing date 2019-05-19
    Publishing country England
    Document type Journal Article ; Comment
    ZDB-ID 2491410-1
    ISSN 1759-4782 ; 1759-4774
    ISSN (online) 1759-4782
    ISSN 1759-4774
    DOI 10.1038/s41571-019-0231-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Capturing Demographic, Health-Related, and Psychosocial Variables in a Standardized Manner: Towards Improving Cancer Screening Adherence.

    Lin, Yannan / Ding, Ruiwen / Prosper, Ashley E / Aberle, Denise R / Bui, Alex A T / Hsu, William

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2023  Volume 2022, Page(s) 709–718

    Abstract: Determining factors influencing patient participation in and adherence to cancer screening recommendations is key to successful cancer screening programs. However, the collection of variables necessary to anticipate patient behavior in cancer screening ... ...

    Abstract Determining factors influencing patient participation in and adherence to cancer screening recommendations is key to successful cancer screening programs. However, the collection of variables necessary to anticipate patient behavior in cancer screening has not been systematically examined. Using lung cancer screening as a representative example, we conducted an exploratory analysis to characterize the current representations of 18 demographic, health-related, and psychosocial variables collected as part of a conceptual model to understand factors for lung cancer screening participation and adherence. Our analysis revealed a lack of standardization in controlled terminologies and common data elements for these variables. For example, only eight (44%) demographic and health-related variables were recorded consistently in the electronic health record. Multiple survey instruments could collect the remaining variables but were highly inconsistent in how variables were represented. This analysis suggests opportunities to establish standardized data formats for psychological, cognitive, social, and environmental variables to improve data collection.
    MeSH term(s) Humans ; Early Detection of Cancer ; Lung Neoplasms ; Data Collection ; Patient Participation ; Demography
    Language English
    Publishing date 2023-04-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.

    Prosper, Ashley Elizabeth / Kammer, Michael N / Maldonado, Fabien / Aberle, Denise R / Hsu, William

    Radiology

    2023  Volume 309, Issue 1, Page(s) e222904

    Abstract: The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United ... ...

    Abstract The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
    MeSH term(s) Humans ; Lung Neoplasms/diagnostic imaging ; Multiple Pulmonary Nodules/diagnostic imaging ; Image Processing, Computer-Assisted ; Tomography, X-Ray Computed
    Language English
    Publishing date 2023-10-12
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.222904
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  6. Article ; Online: Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

    Robertson, Sarah E / Joyce, Nina R / Steingrimsson, Jon A / Stuart, Elizabeth A / Aberle, Denise R / Gatsonis, Constantine A / Dahabreh, Issa J

    JAMA network open

    2024  Volume 7, Issue 1, Page(s) e2346295

    Abstract: Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results ... ...

    Abstract Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population.
    Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults.
    Design, setting, and participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023.
    Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years.
    Main outcomes and measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality.
    Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%).
    Conclusions and relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.
    MeSH term(s) Adult ; Humans ; Middle Aged ; Early Detection of Cancer ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/epidemiology ; Cross-Sectional Studies ; Tomography, X-Ray Computed ; Heart Diseases
    Language English
    Publishing date 2024-01-02
    Publishing country United States
    Document type Journal Article
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2023.46295
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  7. Article ; Online: Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points.

    Lin, Yannan / Liang, Li-Jung / Ding, Ruiwen / Prosper, Ashley Elizabeth / Aberle, Denise R / Hsu, William

    JAMA network open

    2023  Volume 6, Issue 5, Page(s) e2315250

    Abstract: Importance: Screening with low-dose computed tomography (CT) has been shown to reduce mortality from lung cancer in randomized clinical trials in which the rate of adherence to follow-up recommendations was over 90%; however, adherence to Lung Computed ... ...

    Abstract Importance: Screening with low-dose computed tomography (CT) has been shown to reduce mortality from lung cancer in randomized clinical trials in which the rate of adherence to follow-up recommendations was over 90%; however, adherence to Lung Computed Tomography Screening Reporting & Data System (Lung-RADS) recommendations has been low in practice. Identifying patients who are at risk of being nonadherent to screening recommendations may enable personalized outreach to improve overall screening adherence.
    Objective: To identify factors associated with patient nonadherence to Lung-RADS recommendations across multiple screening time points.
    Design, setting, and participants: This cohort study was conducted at a single US academic medical center across 10 geographically distributed sites where lung cancer screening is offered. The study enrolled individuals who underwent low-dose CT screening for lung cancer between July 31, 2013, and November 30, 2021.
    Exposures: Low-dose CT screening for lung cancer.
    Main outcomes and measures: The main outcome was nonadherence to follow-up recommendations for lung cancer screening, defined as failing to complete a recommended or more invasive follow-up examination (ie, diagnostic dose CT, positron emission tomography-CT, or tissue sampling vs low-dose CT) within 15 months (Lung-RADS score, 1 or 2), 9 months (Lung-RADS score, 3), 5 months (Lung-RADS score, 4A), or 3 months (Lung-RADS score, 4B/X). Multivariable logistic regression was used to identify factors associated with patient nonadherence to baseline Lung-RADS recommendations. A generalized estimating equations model was used to assess whether the pattern of longitudinal Lung-RADS scores was associated with patient nonadherence over time.
    Results: Among 1979 included patients, 1111 (56.1%) were aged 65 years or older at baseline screening (mean [SD] age, 65.3 [6.6] years), and 1176 (59.4%) were male. The odds of being nonadherent were lower among patients with a baseline Lung-RADS score of 1 or 2 vs 3 (adjusted odds ratio [AOR], 0.35; 95% CI, 0.25-0.50), 4A (AOR, 0.21; 95% CI, 0.13-0.33), or 4B/X, (AOR, 0.10; 95% CI, 0.05-0.19); with a postgraduate vs college degree (AOR, 0.70; 95% CI, 0.53-0.92); with a family history of lung cancer vs no family history (AOR, 0.74; 95% CI, 0.59-0.93); with a high age-adjusted Charlson Comorbidity Index score (≥4) vs a low score (0 or 1) (AOR, 0.67; 95% CI, 0.46-0.98); in the high vs low income category (AOR, 0.79; 95% CI, 0.65-0.98); and referred by physicians from pulmonary or thoracic-related departments vs another department (AOR, 0.56; 95% CI, 0.44-0.73). Among 830 eligible patients who had completed at least 2 screening examinations, the adjusted odds of being nonadherent to Lung-RADS recommendations at the following screening were increased in patients with consecutive Lung-RADS scores of 1 to 2 (AOR, 1.38; 95% CI, 1.12-1.69).
    Conclusions and relevance: In this retrospective cohort study, patients with consecutive negative lung cancer screening results were more likely to be nonadherent with follow-up recommendations. These individuals are potential candidates for tailored outreach to improve adherence to recommended annual lung cancer screening.
    MeSH term(s) Humans ; Male ; Aged ; Female ; Lung Neoplasms/diagnostic imaging ; Cohort Studies ; Early Detection of Cancer/methods ; Retrospective Studies ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2023-05-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2023.15250
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  8. Article ; Online: ACR Lung-RADS v2022: Assessment Categories and Management Recommendations.

    Christensen, Jared / Prosper, Ashley Elizabeth / Wu, Carol C / Chung, Jonathan / Lee, Elizabeth / Elicker, Brett / Hunsaker, Andetta R / Petranovic, Milena / Sandler, Kim L / Stiles, Brendon / Mazzone, Peter / Yankelevitz, David / Aberle, Denise / Chiles, Caroline / Kazerooni, Ella

    Chest

    2024  Volume 165, Issue 3, Page(s) 738–753

    Abstract: The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size ... ...

    Abstract The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
    MeSH term(s) Humans ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/therapy ; Tomography, X-Ray Computed ; Multiple Pulmonary Nodules ; Consensus ; Lung/diagnostic imaging ; Thyroid Nodule ; Retrospective Studies ; Ultrasonography
    Language English
    Publishing date 2024-01-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1032552-9
    ISSN 1931-3543 ; 0012-3692
    ISSN (online) 1931-3543
    ISSN 0012-3692
    DOI 10.1016/j.chest.2023.10.028
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  9. Article ; Online: External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data.

    Winter, Audrey / Aberle, Denise R / Hsu, William

    Thorax

    2019  Volume 74, Issue 6, Page(s) 551–563

    Abstract: Introduction: We performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis ...

    Abstract Introduction: We performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating.
    Materials and methods: We assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams
    Results: While the area under the curve (AUC) of the model was good, 0.905 (95% CI 0.882 to 0.928), a histogram plot showed that the model poorly differentiated between benign and malignant cases. The calibration plot showed that the model overestimated the probability of cancer. In recalibrating the model, the coefficients for emphysema, spiculation and nodule count were updated. The updated model had an improved calibration and achieved an optimism-corrected AUC of 0.912 (95% CI 0.891 to 0.932). Only pack-year history was found to be significant (p<0.01) among the new covariates evaluated.
    Conclusion: While the Brock model achieved a high AUC when validated on the NLST data set, the model benefited from updating and recalibration. Nevertheless, covariates used in the model appear to be insufficient to adequately discriminate malignant cases.
    MeSH term(s) Aged ; Calibration ; Datasets as Topic ; Early Detection of Cancer ; Female ; Guideline Adherence ; Humans ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/pathology ; Male ; Mass Screening ; Middle Aged ; Models, Statistical ; Multiple Pulmonary Nodules/diagnostic imaging ; Multiple Pulmonary Nodules/pathology ; Predictive Value of Tests ; Probability ; Prognosis ; Randomized Controlled Trials as Topic ; Risk Assessment/methods ; Solitary Pulmonary Nodule/diagnostic imaging ; Solitary Pulmonary Nodule/pathology ; Tomography, X-Ray Computed
    Language English
    Publishing date 2019-03-21
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Validation Study
    ZDB-ID 204353-1
    ISSN 1468-3296 ; 0040-6376
    ISSN (online) 1468-3296
    ISSN 0040-6376
    DOI 10.1136/thoraxjnl-2018-212413
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  10. Article ; Online: ACR Lung-RADS v2022: Assessment Categories and Management Recommendations.

    Christensen, Jared / Prosper, Ashley Elizabeth / Wu, Carol C / Chung, Jonathan / Lee, Elizabeth / Elicker, Brett / Hunsaker, Andetta R / Petranovic, Milena / Sandler, Kim L / Stiles, Brendon / Mazzone, Peter / Yankelevitz, David / Aberle, Denise / Chiles, Caroline / Kazerooni, Ella

    Journal of the American College of Radiology : JACR

    2023  Volume 21, Issue 3, Page(s) 473–488

    Abstract: The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid ... ...

    Abstract The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
    MeSH term(s) Humans ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/therapy ; Tomography, X-Ray Computed ; Consensus ; Cysts ; Lung/diagnostic imaging
    Language English
    Publishing date 2023-10-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2023.09.009
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