Literature DB >> 28339311

Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?

Kaman Chung1, Colin Jacobs1, Ernst T Scholten1, Jin Mo Goo1, Helmut Prosch1, Nicola Sverzellati1, Francesco Ciompi1, Onno M Mets1, Paul K Gerke1, Mathias Prokop1, Bram van Ginneken1, Cornelia M Schaefer-Prokop1.   

Abstract

Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and Methods SSNs on all baseline computed tomographic (CT) scans from the National Lung Cancer Trial that would have been classified as Lung-RADS category 3 or higher were identified, resulting in 374 SSNs for analysis. An experienced screening radiologist volumetrically segmented all solid cores and located all malignant SSNs visible on baseline scans. Six experienced chest radiologists independently determined which nodules to upgrade to category 4X, a recently introduced category for lesions that demonstrate additional features or imaging findings that increase the suspicion of malignancy. Malignancy rates of purely size-based categories and category 4X were compared. Furthermore, the false-positive rates of category 4X lesions were calculated and observer variability was assessed by using Fleiss κ statistics. Results The observers upgraded 15%-24% of the SSNs to category 4X. The malignancy rate for 4X nodules varied from 46% to 57% per observer and was substantially higher than the malignancy rates of categories 3, 4A, and 4B SSNs without observer intervention (9%, 19%, and 23%, respectively). On average, the false-positive rate for category 4X nodules was 7% for category 3 SSNs, 7% for category 4A SSNs, and 19% for category 4B SSNs. Of the falsely upgraded benign lesions, on average 27% were transient. The agreement among the observers was moderate, with an average κ value of 0.535 (95% confidence interval: 0.509, 0.561). Conclusion The inclusion of a 4X assessment category for lesions suspicious for malignancy in a nodule management tool is of added value and results in high malignancy rates in the hands of experienced radiologists. Proof of the transient character of category 4X lesions at short-term follow-up could avoid unnecessary invasive management. © RSNA, 2017.

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Year:  2017        PMID: 28339311     DOI: 10.1148/radiol.2017161624

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


  14 in total

Review 1.  Management of incidental lung nodules <8 mm in diameter.

Authors:  Marcelo Sánchez; Mariana Benegas; Ivan Vollmer
Journal:  J Thorac Dis       Date:  2018-08       Impact factor: 2.895

2.  Adenocarcinoma in pure ground glass nodules: histological evidence of invasion and open debate on optimal management.

Authors:  Gianluca Milanese; Nicola Sverzellati; Ugo Pastorino; Mario Silva
Journal:  J Thorac Dis       Date:  2017-09       Impact factor: 2.895

Review 3.  Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study.

Authors:  David S Gierada; William C Black; Caroline Chiles; Paul F Pinsky; David F Yankelevitz
Journal:  Radiol Imaging Cancer       Date:  2020-03-27

4.  Cancer Risk in Subsolid Nodules in the National Lung Screening Trial.

Authors:  Mark M Hammer; Lauren L Palazzo; Chung Yin Kong; Andetta R Hunsaker
Journal:  Radiology       Date:  2019-09-17       Impact factor: 11.105

Review 5.  Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology.

Authors:  Annemie Snoeckx; Pieter Reyntiens; Damien Desbuquoit; Maarten J Spinhoven; Paul E Van Schil; Jan P van Meerbeeck; Paul M Parizel
Journal:  Insights Imaging       Date:  2017-11-15

6.  Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers.

Authors:  Sarah J van Riel; Francesco Ciompi; Mathilde M Winkler Wille; Asger Dirksen; Stephen Lam; Ernst Th Scholten; Santiago E Rossi; Nicola Sverzellati; Matiullah Naqibullah; Rianne Wittenberg; Marieke C Hovinga-de Boer; Miranda Snoeren; Liesbeth Peters-Bax; Onno Mets; Monique Brink; Mathias Prokop; Cornelia Schaefer-Prokop; Bram van Ginneken
Journal:  PLoS One       Date:  2017-11-09       Impact factor: 3.240

7.  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 8.  Does aggressive management of solitary pulmonary nodules pay off?

Authors:  Stefano Elia; Serafina Loprete; Alessandro De Stefano; Georgia Hardavella
Journal:  Breathe (Sheff)       Date:  2019-03

9.  Malignancy Prediction Capacity and Possible Prediction Model of Circulating Tumor Cells for Suspicious Pulmonary Lesions.

Authors:  Ching-Yang Wu; Jui-Ying Fu; Ching-Feng Wu; Ming-Ju Hsieh; Yun-Hen Liu; Hui-Ping Liu; Jason Chia-Hsun Hsieh; Yang-Teng Peng
Journal:  J Pers Med       Date:  2021-05-21

10.  A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.

Authors:  Ayşegül Gürsoy Çoruh; Bülent Yenigün; Çağlar Uzun; Yusuf Kahya; Emre Utkan Büyükceran; Atilla Elhan; Kaan Orhan; Ayten Kayı Cangır
Journal:  Br J Radiol       Date:  2021-06-11       Impact factor: 3.629

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