Literature DB >> 26458208

Differentiating between Subsolid and Solid Pulmonary Nodules at CT: Inter- and Intraobserver Agreement between Experienced Thoracic Radiologists.

Carole A Ridge1, Afra Yildirim1, Phillip M Boiselle1, Tomas Franquet1, Cornelia M Schaefer-Prokop1, Denis Tack1, Pierre Alain Gevenois1, Alexander A Bankier1.   

Abstract

PURPOSE: To quantify the reproducibility and accuracy of experienced thoracic radiologists in differentiating between subsolid and solid pulmonary nodules at CT.
MATERIALS AND METHODS: The institutional review board of Beth Israel Deaconess Medical Center approved this multicenter study. Six thoracic radiologists, with a mean of 21 years of experience in thoracic radiology (range, 17-22 years), selected images of 10 solid and 10 subsolid nodules to create a database of 120 nodules; this selection served as the reference standard. Each radiologist then interpreted 120 randomly ordered nodules in two different sessions that were separated by a minimum of 3 weeks. The radiologists classified whether or not each nodule was subsolid. Inter- and intraobserver agreement was assessed with a κ statistic. The number of correct classifications was calculated and correlated with nodule size by using Bland-Altman plots. The relationship between disagreement and nodule morphologic characteristics was analyzed by calculating the intraclass correlation coefficient.
RESULTS: Interobserver agreement (κ) was 0.619 (range, 0.469-0.745; 95% confidence interval (CI): 0.576, 0.663) and 0.670 (range, 0.440-0.839; 95% CI: 0.608, 0.733) for interpretation sessions 1 and 2, respectively. Intraobserver agreement (κ) was 0.792 (95% CI: 0.750, 0.833). Averaged for interpretation sessions, correct classification was achieved by all radiologists for 58% (70 of 120) of nodules. Radiologists agreed with their initial determination (the reference standard) in 77% of cases (range, 45%-100%). Nodule size weakly correlated with correct classification (long axis: Spearman rank correlation coefficient, rs = 0.161 and P = .049; short axis: rs = 0.128 and P = .163).
CONCLUSION: The reproducibility and accuracy of thoracic radiologists in classifying whether or not a nodule is subsolid varied in the retrospective study. This inconsistency may affect surveillance recommendations and prognostic determinations.

Mesh:

Year:  2015        PMID: 26458208     DOI: 10.1148/radiol.2015150714

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  19 in total

1.  Lung cancer screening with ultra-low dose CT using full iterative reconstruction.

Authors:  Masayo Fujita; Toru Higaki; Yoshikazu Awaya; Toshio Nakanishi; Yuko Nakamura; Fuminari Tatsugami; Yasutaka Baba; Makoto Iida; Kazuo Awai
Journal:  Jpn J Radiol       Date:  2017-02-14       Impact factor: 2.374

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

Review 3.  Radiomics in immuno-oncology.

Authors:  Z Bodalal; I Wamelink; S Trebeschi; R G H Beets-Tan
Journal:  Immunooncol Technol       Date:  2021-04-16

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.  Improved repeatability of subsolid nodule measurement in low-dose lung screening with monoenergetic images: a phantom study.

Authors:  Jihang Kim; Kyung Hee Lee; Junghoon Kim; Yoon Joo Shin; Kyung Won Lee
Journal:  Quant Imaging Med Surg       Date:  2019-02

7.  Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study.

Authors:  Marios A Gavrielides; Benjamin P Berman; Mark Supanich; Kurt Schultz; Qin Li; Nicholas Petrick; Rongping Zeng; Jenifer Siegelman
Journal:  Quant Imaging Med Surg       Date:  2017-12

8.  Pathologic categorization of lung nodules: Radiomic descriptors of CT attenuation distribution patterns of solid and subsolid nodules in low-dose CT.

Authors:  Chuan Zhou; Heang-Ping Chan; Aamer Chughtai; Lubomir M Hadjiiski; Ella A Kazerooni; Jun Wei
Journal:  Eur J Radiol       Date:  2020-05-31       Impact factor: 3.528

9.  Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules.

Authors:  Jean-Paul Charbonnier; Kaman Chung; Ernst T Scholten; Eva M van Rikxoort; Colin Jacobs; Nicola Sverzellati; Mario Silva; Ugo Pastorino; Bram van Ginneken; Francesco Ciompi
Journal:  Sci Rep       Date:  2018-01-12       Impact factor: 4.379

10.  Malignancy estimation of Lung-RADS criteria for subsolid nodules on CT: accuracy of low and high risk spectrum when using NLST nodules.

Authors:  Kaman Chung; Colin Jacobs; Ernst T Scholten; Onno M Mets; Irma Dekker; Mathias Prokop; Bram van Ginneken; Cornelia M Schaefer-Prokop
Journal:  Eur Radiol       Date:  2017-04-24       Impact factor: 5.315

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.