Literature DB >> 25293951

Inter-reader variability when applying the 2013 Fleischner guidelines for potential solitary subsolid lung nodules.

Alex Penn1, Mingming Ma2, Benjamin B Chou2, Jeffrey R Tseng2, Peter Phan2.   

Abstract

BACKGROUND: In 2013, the Fleischner Society published recommendations for managing subsolid pulmonary nodules. Inter-reader variability has not yet been defined and has potential implications for the ease and reproducibility of applying the guidelines to clinical practice.
PURPOSE: To evaluate inter-reader variability when applying the 2013 Fleischner guidelines for potential solitary subsolid lung nodules.
MATERIAL AND METHODS: Potential nodules were identified through a systematic retrospective review of CT studies that reported a ground-glass lesion. Three radiologists decided whether these lesions fit criteria of a subsolid nodule and thus merit application of the Fleischner Society guidelines, determined if a solid component was present, and measured each component in two dimensions. Final management recommendations were based on these intermediate decisions. Inter-reader variability for management was calculated and Fleiss' kappa was used to determine significance. Logistic regression and Fisher's exact test determined whether management was contingent on each intermediate decision.
RESULTS: Forty-four nodules with mean diameter of 9.4 mm were evaluated by three radiologists. Final management recommendations were in agreement for 93 out of 132 cases (70.4%, kappa = 0.56). Inter-reader variability in management recommendation was contingent on disagreement over whether a pulmonary lesion fit criteria of a subsolid nodule for 24 cases (P < 0.01), whether there was a solid component for 10 cases (P = 0.01), and whether the measurement met the threshold of 5 mm for five cases (P = 0.12).
CONCLUSION: There is moderate inter-reader variability when applying the 2013 Fleischner Society management recommendations. Significant contributors of variability include whether the potential lesions fit subsolid nodule criteria and whether a solid component is present. Measurement variability does not significantly affect the final management decisions. © The Foundation Acta Radiologica 2014.

Entities:  

Keywords:  CT – spiral; decision analysis; lung; thorax

Mesh:

Year:  2014        PMID: 25293951     DOI: 10.1177/0284185114551975

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  16 in total

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Authors:  Julien G Cohen; Hyungjin Kim; Su Bin Park; Bram van Ginneken; Gilbert R Ferretti; Chang Hyun Lee; Jin Mo Goo; Chang Min Park
Journal:  Eur Radiol       Date:  2017-01-05       Impact factor: 5.315

2.  Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network.

Authors:  Xiaoguang Tu; Mei Xie; Jingjing Gao; Zheng Ma; Daiqiang Chen; Qingfeng Wang; Samuel G Finlayson; Yangming Ou; Jie-Zhi Cheng
Journal:  Sci Rep       Date:  2017-09-01       Impact factor: 4.379

3.  Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT.

Authors:  Roger Y Kim; Jason L Oke; Lyndsey C Pickup; Reginald F Munden; Travis L Dotson; Christina R Bellinger; Avi Cohen; Michael J Simoff; Pierre P Massion; Claire Filippini; Fergus V Gleeson; Anil Vachani
Journal:  Radiology       Date:  2022-05-24       Impact factor: 29.146

4.  Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement.

Authors:  Hyungjin Kim; Chang Min Park; Eui Jin Hwang; Su Yeon Ahn; Jin Mo Goo
Journal:  Eur Radiol       Date:  2017-12-01       Impact factor: 5.315

Review 5.  Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology.

Authors:  Douglas E Wood; Ella A Kazerooni; Scott L Baum; George A Eapen; David S Ettinger; Lifang Hou; David M Jackman; Donald Klippenstein; Rohit Kumar; Rudy P Lackner; Lorriana E Leard; Inga T Lennes; Ann N C Leung; Samir S Makani; Pierre P Massion; Peter Mazzone; Robert E Merritt; Bryan F Meyers; David E Midthun; Sudhakar Pipavath; Christie Pratt; Chakravarthy Reddy; Mary E Reid; Arnold J Rotter; Peter B Sachs; Matthew B Schabath; Mark L Schiebler; Betty C Tong; William D Travis; Benjamin Wei; Stephen C Yang; Kristina M Gregory; Miranda Hughes
Journal:  J Natl Compr Canc Netw       Date:  2018-04       Impact factor: 11.908

6.  Measurement Variability of Persistent Pulmonary Subsolid Nodules on Same-Day Repeat CT: What Is the Threshold to Determine True Nodule Growth during Follow-Up?

Authors:  Hyungjin Kim; Chang Min Park; Yong Sub Song; Leonard Sunwoo; Ye Ra Choi; Jung Im Kim; Jae Hyun Kim; Jae Seok Bae; Jong Hyuk Lee; Jin Mo Goo
Journal:  PLoS One       Date:  2016-02-09       Impact factor: 3.240

7.  Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

Authors:  Pierre P Massion; Sanja Antic; Sarim Ather; Carlos Arteta; Jan Brabec; Heidi Chen; Jerome Declerck; David Dufek; William Hickes; Timor Kadir; Jonas Kunst; Bennett A Landman; Reginald F Munden; Petr Novotny; Heiko Peschl; Lyndsey C Pickup; Catarina Santos; Gary T Smith; Ambika Talwar; Fergus Gleeson
Journal:  Am J Respir Crit Care Med       Date:  2020-07-15       Impact factor: 21.405

8.  Determining malignancy in CT guided fine needle aspirate biopsy of subsolid lung nodules: Is core biopsy necessary?

Authors:  Nantaka Kiranantawat; Shaunagh McDermott; Milena Petranovic; Mari Mino-Kenudson; Ashok Muniappan; Amita Sharma; Jo-Anne O Shepard; Subba R Digumarthy
Journal:  Eur J Radiol Open       Date:  2019-05-04

9.  Semiquantative Visual Assessment of Sub-solid Pulmonary Nodules ≦3 cm in Differentiation of Lung Adenocarcinoma Spectrum.

Authors:  Fu-Zong Wu; Po-An Chen; Carol C Wu; Pei-Lun Kuo; Shu-Ping Tsao; Chu-Chun Chien; En-Kuei Tang; Ming-Ting Wu
Journal:  Sci Rep       Date:  2017-11-17       Impact factor: 4.379

10.  Development and clinical application of deep learning model for lung nodules screening on CT images.

Authors:  Sijia Cui; Shuai Ming; Yi Lin; Fanghong Chen; Qiang Shen; Hui Li; Gen Chen; Xiangyang Gong; Haochu Wang
Journal:  Sci Rep       Date:  2020-08-12       Impact factor: 4.379

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