The utility of a convolutional neural network (CNN) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to British Thoracic Society guidelines.

Published on Jan 14, 2021in European Journal of Radiology2.687
· DOI :10.1016/J.EJRAD.2021.109553
Maria Tsakok7
Estimated H-index: 7
(John Radcliffe Hospital),
Maria Tsakok2
Estimated H-index: 2
(John Radcliffe Hospital)
+ 3 AuthorsFergus Gleeson6
Estimated H-index: 6
(John Radcliffe Hospital)
Abstract Purpose To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum. Method An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group. We hypothesized that the 5% low risk threshold group requires only one follow-up, the 0.56% very low risk threshold group requires no follow-up and the 80% high risk threshold group warrants expedited intervention. Results The 158 benign nodules had an LCP-CNN score between 0.1 and 70.8%, median 5.5% (IQR 1.4-18.0), whilst the 32 cancer nodules had an LCP-CNN score between 10.1 and 98.7%, median 59.0% (IQR 37.1-83.9). 24/61 CT scans in the 0.56-5% group (n = 37) and 21/21 CT scans Conclusion We show the potential of artificial intelligence to reduce the need for follow-up scans and intervention in low-scoring benign nodules, whilst potentially accelerating the investigation and treatment of high-scoring cancer nodules.
#1Pierre P. Massion (Vandy: Vanderbilt University)H-Index: 65
#2Sanja L. Antic (Vandy: Vanderbilt University)H-Index: 4
Last. Fergus V. Gleeson (University of Oxford)H-Index: 58
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Rationale: The management indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unne...
15 CitationsSource
#1David R Baldwin (University of Nottingham)H-Index: 43
#2Jennifer Gustafson (Churchill Hospital)H-Index: 2
Last. Fergus V. Gleeson (Churchill Hospital)H-Index: 58
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Background Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines. Methods A dataset of incidentally detected pulmonary nodules measuring 5–15 mm was collected retrospe...
21 CitationsSource
#1Sandra González Maldonado (DKFZ: German Cancer Research Center)H-Index: 5
#2Stefan Delorme (DKFZ: German Cancer Research Center)H-Index: 63
Last. Rudolf Kaaks (DKFZ: German Cancer Research Center)H-Index: 151
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Importance Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening. Objective To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the Ge...
4 CitationsSource
#1Audrey Winter (UC: University of California)H-Index: 1
#2Denise R. Aberle (UC: University of California)H-Index: 64
Last. William Hsu (UC: University of California)H-Index: 14
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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. Adheri...
8 CitationsSource
#1Irsk Anderson (U of C: University of Chicago)H-Index: 3
#2Andrew M. Davis (U of C: University of Chicago)H-Index: 18
Last. Andrew A. Davis (U of C: University of Chicago)H-Index: 26
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#1Andrew Murphy (USYD: University of Sydney)H-Index: 4
#2Matthew R. Skalski (Southern California University of Health Sciences)H-Index: 12
Last. Frank Gaillard (University of Melbourne)H-Index: 13
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Lung cancer is one of the leading causes of cancer-related fatality in the world. Patients display few or even no signs or symptoms in the early stages, resulting in up to 75% of patients diagnosed in the later stages of the disease. Consequently, there has been a call for lung cancer screening amongst at-risk populations. The early detection of malignant pulmonary nodules in CT is one of the suggested methods proposed to diagnose early-stage lung cancer; however, the reported sensitivity of rad...
22 CitationsSource
Machine learning based lung cancer prediction models have been proposed to assist clinicians in managing incidental or screen detected indeterminate pulmonary nodules. Such systems may be able to reduce variability in nodule classification, improve decision making and ultimately reduce the number of benign nodules that are needlessly followed or worked-up. In this article, we provide an overview of the main lung cancer prediction approaches proposed to date and highlight some of their relative s...
25 CitationsSource
#1Ryan Clay (Mayo Clinic)H-Index: 6
#2Srinivasan Rajagopalan (Mayo Clinic)H-Index: 19
Last. Brian J. Bartholmai (Mayo Clinic)H-Index: 33
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The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment...
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#1Kaman Chung (Radboud University Nijmegen)H-Index: 9
#2Onno M. Mets (UU: Utrecht University)H-Index: 18
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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...
16 CitationsSource
#1David R Baldwin (University of Nottingham)H-Index: 43
Last. I Woolhouse (University Hospitals Birmingham NHS Foundation Trust)H-Index: 6
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Introduction The purpose of the quality standards document is to provide healthcare professionals, commissioners, service providers and patients with a guide to standards of care that should be met for the investigation and management of pulmonary nodules in the UK, together with measurable markers of good practice. Methods Development of British Thoracic Society (BTS) Quality Standards follows the BTS process of quality standard production based on the National Institute for Health and Care Exc...
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