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M. Tammemägi
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MINI 36 - Imaging and Diagnostic Workup (ID 163)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Screening and Early Detection
- Presentations: 2
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MINI36.04 - Automated Measurement of Malignancy Risk of Lung Nodule Detected by Screening Computed Tomography (ID 1737)
18:45 - 18:50 | Author(s): M. Tammemägi
- Abstract
- Presentation
Background:
We have previously reported a practical predictive tool that accurately estimates the probability of malignancy for lung nodules detected at baseline screening LDCT (New Engl J Med. 2013;369:908-17). Manual measurement of nodule dimensions and generation of malignancy risk scores is time consuming and subjected to intra- and inter-observer variability. The goal of this study is to prepare a nodule malignancy risk prediction model based on automated computer generated nodule data and compare it to an established model based on radiologists’ generated data.
Methods:
Using the same published PanCan dataset (New Engl J Med. 2013;369:908-17) with the number of lung cancers updated, we prepared a logistic regression model predicting lung cancer using computer-generated imaging data from the CIRRUS Lung Screening software (Diagnostic Imaging Analysis Group, Nijmegen, The Netherlands). Ninety-one of the 2,537 baseline (first) scans were not available or could not be processed by CIRRUS. The remaining 2,446 scans were first annotated by the CIRRUS software. A human non-radiologist reader then accepted/rejected the annotated marks and manually searched the LDCT for nodules missed by CIRRUS or the study radiologist. New nodules found that were not recorded by the study radiologist were reviewed by a subspecialty trained chest radiologist with 14 years experience in lung cancer screening (JM). Nodule morphometric measurements (maximum and mean diameter, volume, mass, density) and total nodule count per scan irrespective of size were automatically generated by the CIRRUS software. The nodule type (nonsolid, part-solid, or solid), nodule description (lobulated, spiculated or well defined) and nodule location (upper versus middle or lower lobe) were manually entered. The variables were evaluated in models as untransformed and natural log transformed variables. Nonlinear relationships with lung cancer were also evaluated. Socio-demographic and clinical history predictors were not included in the model.
Results:
Radiologists evaluation identified 8,570 pulmonary nodules of any size in 2063 individuals - 124 nodules in 119 individuals were diagnosed as cancer in follow-up. Based on CIRRUS software annotated marks that were accepted by a human reader, computer analysis identified 11,520 pulmonary nodules in 2174 individuals - 121 nodules in 115 individuals were diagnosed as cancer in follow-up. Thirty-six percent of new nodules found by CIRRUS and/or second human reader were ≥4 mm (mean±SD, 5.9± 3.5 mm). Both the computer generated imaging data model (Model-CIRRUS) and the radiologist generated data model (Model-RAD) demonstrated excellent discrimination and calibration. Their predictive performances were also similar. Comparing Model-CAD to Model-RAD, the AUCs were 0.9537 versus 0.9541, the 90[th] percentile absolute errors were 0.0008 versus 0.0007, and the Brier scores were 0.0093 versus 0.0137. Mean nodule diameter is a better risk predictor than maximum nodule diameter, nodule density or mass.
Conclusion:
The predictive performances of computer and radiologist generated data models were similar. The model can be integrated to the CIRRUS Lung Screening software to automatically generate a nodule malignancy risk score to facilitate nodule management recommendation. Supported by the Terry Fox Research Institute, The Canadian Partnership Against Cancer and the BC Cancer Foundation on behalf of the Pan-Canadian Early Detection of Lung Cancer Study Group.
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MINI36.05 - Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT (ID 1702)
18:50 - 18:55 | Author(s): M. Tammemägi
- Abstract
- Presentation
Background:
The recommendation by the US Preventive Services Task Force to screen high-risk smokers with low-dose computed tomography (LDCT) and the recent decision by the Centers for Medicare and Medicaid Services to fund LDCT screening under the Medicare program mean that LDCT screening will be implemented at the population level in the US and likely in other countries. With the large volume of scans that will be generated, accurate and efficient interpretation of LDCT images is key to providing a cost-effective implementation of LDCT screening to the large at risk population. Objective To evaluate an alternative workflow to identify and triage abnormal LDCT scans in which a technician assisted by Computer Vision (CV) software acts as first reader with the aim to reduce workload, improve speed, consistency and quality of interpretation of screening LDCT scans.
Methods:
A test dataset of baseline Pan-Canadian Early Detection of Lung Cancer Study LDCT scans (New Engl J Med. 2013;369:908-17) was used. This included: 136 scans with lung cancers, 556 scans with benign nodules and 136 scans without nodules. The scans were randomly assigned for analysis by the CV software (CIRRUS Lung Screening, Diagnostic Imaging Analysis Group, Nijmegen, The Netherlands). The annotated scans were then reviewed by a technician without knowledge of the diagnosis. The scans were classified by the technician as either normal (no nodules or benign nodules only, potentially not requiring radiologist review) or abnormal (suspicious of malignancy or other abnormality requiring radiologist review). The results were compared with the Pan-Can Study radiologists. Nodules found by CIRRUS but not by the radiologist were reviewed by a subspecialty trained chest radiologist with 14 years experience in lung cancer screening (JM).
Results:
The overall sensitivity and specificity of the technician to identify an abnormal scan were: 97.7% (95% CI: 96.3 - 98.7) and 98.0% (95% CI: 89.5 - 99.7) respectively. The technician correctly identified all the scans with malignant nodules. The time taken by the technician to read a scan was 208±120 sec.
Conclusion:
A technician assisted by CV software can categorize accurately abnormal scans for review by a radiologist. Pre-screening by a technician and CV software is a promising strategy for reducing workload, improving the speed, consistency and quality of scan interpretation of screening chest CTs. Supported by the Terry Fox Research Institute, The Canadian Partnership Against Cancer and the BC Cancer Foundation on behalf of the Pan-Canadian Early Detection of Lung Cancer Study Group.
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MS 10 - Management of Screening Detected Lung Cancer (ID 28)
- Event: WCLC 2015
- Type: Mini Symposium
- Track: Treatment of Localized Disease - NSCLC
- Presentations: 1
- Moderators:N. Ikeda, B. Park, P. Goldstraw, L.E. Raez
- Coordinates: 9/07/2015, 14:15 - 15:45, Mile High Ballroom 2c-3c
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MS10.02 - Positive Lung Cancer Screens - Which Ones are Lung Cancer? (ID 1890)
14:30 - 14:40 | Author(s): M. Tammemägi
- Abstract
- Presentation
Abstract not provided
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ORAL 09 - CT Screening - New Data and Risk Assessment (ID 95)
- Event: WCLC 2015
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:J. Mulshine, J.K. Field
- Coordinates: 9/07/2015, 10:45 - 12:15, Mile High Ballroom 2a-3b
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ORAL09.07 - Economic Evidence for the Use of Risk-Selection and Risk-Stratification for Lung Cancer Screening Programs (ID 2928)
11:39 - 11:50 | Author(s): M. Tammemägi
- Abstract
- Presentation
Background:
Screening for lung cancer according to age and smoking history alone could cost billions of dollars of public health expenditure due to the high incidence of potential participants. Risk-adapted lung cancer screening strategies such as participant selection (based on published risk prediction models such as the PLCOm2012 model) and malignancy risk based screening protocols may reduce program costs while improving outcomes among current and former smokers at risk of developing the disease. The Pan-Canadian Early Detection of Lung Cancer Study (PanCan) was designed with the objective of providing economic evidence for an affordable lung cancer-screening program in Canada.
Methods:
Data for 2537 screening participants in the PanCan study (median follow-up time of 4 years) and 25,914 eligible participants from the NLST-CXR arm were included in the analysis. There was adequate power and follow-up to inform the transition probabilities in model and provide the distribution to test all model parameters simultaneously in a probabilistic sensitivity analysis. The cost and health utility inputs are from patient-level trial data with defined ranges of certainty.
Results:
Our results show that risk selection using the PanCan risk prediction model could reduce the need to screen 21,022 (81%) of the NLST population if risk prediction were applied. If risk prediction were applied to Canadians who met the NLST criteria, 2 year program costs could be reduced by 400 million dollars and nearly half a million people could be spared the potential harms from screening that is not likely to result in a Cancer diagnosis. With the economic evidence from the PanCan and NLST trials, we report our initial cost-effectiveness results and will show, for the first time, a definitive description of the uncertainty surrounding our cost-effectiveness ratios.
Conclusion:
Using a model loaded with patient-level screening data has enabled us to predict the likelihood that risk-adapted screening will fall below most commonly referenced thresholds of acceptability for cancer interventions. The initial results and characterization of the parameters affecting cost-effectiveness will be presented. [*]on behalf of the PanCan study team The panCan study is sponsored by the Terry Fox Research Institute and the Canadian Partnership against Cancer, ARCC is funded by the CCSRI The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI
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