Predicting Malignancy Risk of Screen-Detected Lung Nodules–Mean Diameter or Volume

Tammemägi, Martin C.; Ritchie, A.; Atkar-Khattra, Sukhinder; Dougherty, Brendan; Sanghera, Calvin; Mayo, John R.; Ren, Yuan; Manos, Daria; McWilliams, Annette; Schmidt, Heidi; Gingras, Michel J. P.; Pasian, Sergio; Stewart, Lori; Tsai, Scott S. H.; Seely, Jean M.; Burrowes, Paul; Bhatia, Rick; Haider, Ehsan; Boylan, Colm; Jacobs, Colin; Ginneken, Bram van; Tsao, Ming‐Sound; Lam, Stephen

doi:10.1016/j.jtho.2018.10.006

Cited by 40 publications

(47 citation statements)

References 30 publications

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“…15 However, the recent PanCan-VOL and PanCan-MD updates in PanCan showed higher sensitivity and lower specificity in our study but similar PPV. 16 External validation of PanCan-2b in the NLST also showed higher sensitivity, lower specificity, and comparable PPV 18 compared with estimates in LUSI ( To our knowledge, these analyses are the first to provide external validation of the recent UKLS model 17 and a complete external validation for PanCan-MD and PanCan-VOL models. 16 Our analyses showed no clear superiority for the PanCan-VOL or PanCan-MD models over the older PanCan model versions, confirming observations in PanCan 16 but not those from Horeweg et al 30 In LUSI, Downloaded From: https://jamanetwork.com/ on 10/06/2020 diameters were retrieved by software, thus based on a 3-dimensional estimation, which possibly led to a more accurate estimation of nodule diameter compared with manual diameter measurements used in other studies.…”

Section: Study Participants and Pulmonary Nodule Findingsmentioning

confidence: 84%

“…16 External validation of PanCan-2b in the NLST also showed higher sensitivity, lower specificity, and comparable PPV 18 compared with estimates in LUSI ( To our knowledge, these analyses are the first to provide external validation of the recent UKLS model 17 and a complete external validation for PanCan-MD and PanCan-VOL models. 16 Our analyses showed no clear superiority for the PanCan-VOL or PanCan-MD models over the older PanCan model versions, confirming observations in PanCan 16 but not those from Horeweg et al 30 In LUSI, Downloaded From: https://jamanetwork.com/ on 10/06/2020 diameters were retrieved by software, thus based on a 3-dimensional estimation, which possibly led to a more accurate estimation of nodule diameter compared with manual diameter measurements used in other studies. 31 Overall, our observations are in line with those from other validation studies, in context of incidentally or symptomatically detected pulmonary nodules 13,[32][33][34][35][36][37][38] or in LDCT screening settings, 14,[17][18][19]39,40 which mostly have also shown discrimination indices of AUCs of approximately 0.80 and higher, and similar ranking by discrimination capacity when comparing performance of the PanCan, VA, Mayo Clinic, and PKUPH models.…”

Section: Study Participants and Pulmonary Nodule Findingsmentioning

confidence: 84%

“…The more comprehensive model including spiculation, PanCan-2b, was recently updated by replacing nodule diameter with multidimensional measurements: mean diameter (PanCan-MD) or volume (PanCan-VOL). 16 Independently of PanCan, a new model was developed recently in the context of the UK Lung Cancer Screening (UKLS) trial 17 using nodule volume.…”

Section: Introductionmentioning

confidence: 99%

“…The PanCan models have been externally validated for discrimination in the NLST 16,18 and Danish Lung Cancer Screening Trial, 19 but they have not been externally validated for calibration. The recent UKLS model 17 has not been externally validated, to our knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography

et al. 2020

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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 German Lung Cancer Screening Intervention trial. DESIGN, SETTING, AND PARTICIPANTS In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from

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Section: Study Participants and Pulmonary Nodule Findingsmentioning

confidence: 84%

Section: Study Participants and Pulmonary Nodule Findingsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography

et al. 2020

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“…Furthermore, subgroup analysis showed there was high efficacy for the detection of small (<10 mm) pulmonary nodules, similar to that for larger nodules (10–30 mm). Computer‐aided detection (CAD) tools, a well‐known system for nodules measurements and risk prediction, have been previously reported and validated through specific data sets . More comparable and even superior results were observed using the CNN model compared with the CAD‐based model (Table ).…”

Section: Discussionmentioning

confidence: 95%

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network

et al. 2019

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Background Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well‐trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. Materials and Methods Open‐source data sets and multicenter data sets have been used in this study. A three‐dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results. Results The sensitivity and specificity of this well‐trained model were found to be 84.4% (95% confidence interval [CI], 80.5%–88.3%) and 83.0% (95% CI, 79.5%–86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10–30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three‐dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment. Conclusion Under the companion diagnostics, the three‐dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices. Implications for Practice The three‐dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.

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Less Is More in Lung Cancer Risk Prediction Models

Heuvelmans

Oudkerk²

2020

JAMA Netw Open

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Screening of high-risk individuals by low-dose chest computed tomography (CT) reduces lung cancer mortality, as has been shown by 2 large randomized clinical trials. 1,2 Contrary to other cancer screening programs, such as breast and colorectal cancer screening, individuals eligible for screening are not selected only based on sex and age. Lung cancer is in most cases diagnosed in (former) smokers. To increase the efficacy of a screening program, and to minimize harms to individuals at low risk of the disease, it is most cost-effective to invite only those individuals who have the highest risk of developing lung cancer to undergo annual low-dose chest CT. Risk prediction models aim to assist in identifying these high-risk individuals, and most international lung cancer screening guidelines recommend using a model to optimize selection of the screening population. 3In addition to prediction models for the identification of the population at risk, in recent years, a variety of models have been published predicting lung nodule malignancy risk in screen-detected pulmonary nodules. These lung nodule malignancy risk prediction models aim to improve decisionmaking regarding nodule management and diagnosis. In every second lung cancer screening participant, at least 1 lung nodule is detected at the baseline screening round. A quarter of those who undergo screening present with 2 or more baseline nodules. 4 At nodule level, fewer than 1% are diagnosed as lung cancer; the others are likely to represent benign lesions such as scars or intrapulmonary lymph nodes. Lung nodule malignancy prediction models are based either on data collected in screening studies (ie, different versions of the model used in the Pan-Canadian Early Detection of Lung Cancer Study [also referred to as the Brock model] 5 and the model using data from the UK Lung Cancer Screening [UKLS] trial 6 ) or on clinically, mostly incidentally, detected nodules (ie, models from the Mayo Clinic, the US Department of Veteran Affairs clinics, and Peking University People's Hospital).In the study by González Maldonado et al, 7 performance of these lung nodule malignancy prediction models was externally tested using data from the interventional group of the German Lung Cancer Screening Intervention (LUSI) randomized clinical trial. In total, 1159 participants with 3903 noncalcified lung nodules in any of 5 annual low-dose CT screening rounds were selected for this study. During the active screening period, 54 of 1159 participants with nodules (5%) were diagnosed with lung cancer. Most lung nodules were detected at baseline (2883 nodules [73.9%]), whereas half of the lung cancers were diagnosed in 1 of the 1020 nodules newly detected after baseline. In the rounds following baseline screening, 80.6% of lung cancers had diameter of at least 8 mm at diagnosis. González Maldonado et al 7 have shown that performance of all 8 prediction models was better on prevalence (baseline) nodules compared with nodules newly detected during incidence screenings. Previous studies have ...

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Predicting Malignancy Risk of Screen-Detected Lung Nodules–Mean Diameter or Volume

Cited by 40 publications

References 30 publications

Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography

Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network

Less Is More in Lung Cancer Risk Prediction Models

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