Literature DB >> 29777062

Brock malignancy risk calculator for pulmonary nodules: validation outside a lung cancer screening population.

Kaman Chung1, Onno M Mets2, Paul K Gerke1, Colin Jacobs1, Annemarie M den Harder2, Ernst T Scholten1, Mathias Prokop1, Pim A de Jong2, Bram van Ginneken1, Cornelia M Schaefer-Prokop1,3.   

Abstract

OBJECTIVE: To assess the performance of the Brock malignancy risk model for pulmonary nodules detected in routine clinical setting.
METHODS: In two academic centres in the Netherlands, we established a list of patients aged ≥40 years who received a chest CT scan between 2004 and 2012, resulting in 16 850 and 23 454 eligible subjects. Subsequent diagnosis of lung cancer until the end of 2014 was established through linking with the National Cancer Registry. A nested case-control study was performed (ratio 1:3). Two observers used semiautomated software to annotate the nodules. The Brock model was separately validated on each data set using ROC analysis and compared with a solely size-based model.
RESULTS: After the annotation process the final analysis included 177 malignant and 695 benign nodules for centre A, and 264 malignant and 710 benign nodules for centre B. The full Brock model resulted in areas under the curve (AUCs) of 0.90 and 0.91, while the size-only model yielded significantly lower AUCs of 0.88 and 0.87, respectively (p<0.001). At 10% malignancy risk, the threshold suggested by the British Thoracic Society, sensitivity of the full model was 75% and 81%, specificity was 85% and 84%, positive predictive values were 14% and 10% at negative predictive value (NPV) of 99%. The optimal threshold was 6% for centre A and 8% for centre B, with NPVs >99%. DISCUSSION: The Brock model shows high predictive discrimination of potentially malignant and benign nodules when validated in an unselected, heterogeneous clinical population. The high NPV may be used to decrease the number of nodule follow-up examinations. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  lung cancer

Mesh:

Year:  2018        PMID: 29777062     DOI: 10.1136/thoraxjnl-2017-211372

Source DB:  PubMed          Journal:  Thorax        ISSN: 0040-6376            Impact factor:   9.139


  11 in total

1.  Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

Authors:  Johanna Uthoff; Nicholas Koehn; Jared Larson; Samantha K N Dilger; Emily Hammond; Ann Schwartz; Brian Mullan; Rolando Sanchez; Richard M Hoffman; Jessica C Sieren
Journal:  Eur Radiol       Date:  2019-04-01       Impact factor: 5.315

Review 2.  Implementation planning for lung cancer screening in China.

Authors:  Yue I Cheng; Michael P A Davies; Dan Liu; Weimin Li; John K Field
Journal:  Precis Clin Med       Date:  2019-03-14

3.  A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data.

Authors:  Ruiling Zu; Lin Wu; Rong Zhou; Xiaoxia Wen; Bangrong Cao; Shan Liu; Guishu Yang; Ping Leng; Yan Li; Li Zhang; Xiaoyu Song; Yao Deng; Kaijiong Zhang; Chang Liu; Yuping Li; Jian Huang; Dongsheng Wang; Guiquan Zhu; Huaichao Luo
Journal:  J Cancer       Date:  2022-05-09       Impact factor: 4.478

4.  A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT.

Authors:  Andrew V Kossenkov; Rehman Qureshi; Noor B Dawany; Jayamanna Wickramasinghe; Qin Liu; R Sonali Majumdar; Celia Chang; Sandy Widura; Trisha Kumar; Wen-Hwai Horng; Eric Konnisto; Gerard Criner; Jun-Chieh J Tsay; Harvey Pass; Sai Yendamuri; Anil Vachani; Thomas Bauer; Brian Nam; William N Rom; Michael K Showe; Louise C Showe
Journal:  Cancer Res       Date:  2018-11-28       Impact factor: 12.701

5.  Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial.

Authors:  Robert Haase; Jonathan D Dodd; Hans-Ulrich Kauczor; Ella A Kazerooni; Marc Dewey
Journal:  Eur J Radiol Open       Date:  2020-06-25

Review 6.  Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening.

Authors:  Spencer C Dyer; Brian J Bartholmai; Chi Wan Koo
Journal:  J Thorac Dis       Date:  2020-11       Impact factor: 2.895

7.  The determinants of lung cancer after detecting a solitary pulmonary nodule are different in men and women, for both chest radiograph and CT.

Authors:  Elisa Chilet-Rosell; Lucy A Parker; Ildefonso Hernández-Aguado; María Pastor-Valero; José Vilar; Isabel González-Álvarez; José María Salinas-Serrano; Fermina Lorente-Fernández; M Luisa Domingo; Blanca Lumbreras
Journal:  PLoS One       Date:  2019-09-11       Impact factor: 3.240

8.  Clinical characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital.

Authors:  Xiaonan Cui; Daiwei Han; Marjolein A Heuvelmans; Yihui Du; Yingru Zhao; Lei Zhang; Harry J M Groen; Geertruida H de Bock; Monique D Dorrius; Matthijs Oudkerk; Rozemarijn Vliegenthart; Zhaoxiang Ye
Journal:  Cancer Biol Med       Date:  2020-02-15       Impact factor: 4.248

9.  Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels.

Authors:  Hongjun Hou; Shui Yu; Zushan Xu; Hongsheng Zhang; Jie Liu; Wenjun Zhang
Journal:  Eur J Cancer Prev       Date:  2021-09-01       Impact factor: 2.164

10.  Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review.

Authors:  Marina Senent-Valero; Julián Librero; María Pastor-Valero
Journal:  Syst Rev       Date:  2021-12-06
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