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Aneri B Balar
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OA06 - Refining Lung Cancer Screening (ID 131)
- Event: WCLC 2019
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Tomasz Grodzki, Lluis Esteban Tejero
- Coordinates: 9/09/2019, 11:00 - 12:30, Hilton Head (1978)
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OA06.06 - Independent Validation of a Novel High-Resolution Computed Tomography-Based Radiomic Classifier for Indeterminate Lung Nodules (Now Available) (ID 2862)
11:00 - 12:30 | Author(s): Aneri B Balar
- Abstract
- Presentation
Background
Optimization of the clinical management of incidentally- and screen-identified lung nodules is urgently needed to limit the number of unnecessary invasive diagnostic interventions, and therefore morbidity, mortality and healthcare costs. We recently developed and internally validated a novel radiomics-based approach for the classification of screen-detected indeterminate nodules, and present herein validation of this algorithm in an independent cohort.
Method
In a previous study, we developed a multivariate prediction model evaluating independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature. Nodules between 7 and 30 mm of largest diameter were selected from the National Lung Screening Trial (n=726 indeterminate nodules, benign (n = 318) and malignant (n = 408)) were used to derive this model using least absolute shrinkage and selection operator (LASSO) method with bootstrapping method applied for the internal validation. Eight variables capturing vertical location, size, shape, density and surface characteristics were included with an optimism-correct area under the curve (AUC) of 0.94. For this study, an independent dataset of 203 incidentally-identified lung nodules obtained from the indeterminate pulmonary nodule registry at Vanderbilt University was identified. CT datasets were transferred to Mayo Clinic (Rochester, MN) for analysis. Nodules were segmented manually using the ANALYZE software (Biomedical Imaging Resource, Mayo Clinic, Rochester, MN), and radiomic analysis was performed using the 8-variable radiomic diagnostic algorithm derived from the NLST. The Brock model was also used to calculate probability of malignancy for all NLST and Vanderbilt nodules.
Result
Brock scores were calculated for 685 NLST nodules (excluded: interval cancers, n=12; missing values needed for Brock score, n=29). The AUC for the Brock score (AUC Brock) for NLST nodules was 0.83 which was inferior to the AUC for the radiomic model (AUC Radiomic =0.94, P<0.001). When the subset of intermediate pre-test probability of lung cancer was considered (Brock score > 10 but <= 60), the AUC Brock was 0.61 (95% CI: 0.54-0.68) whereas the AUC Radiomic was 0.88 (95% CI: 0.84-0.93). A total of 203 incidentally found pulmonary nodules with available clinical information and biopsy or surgery-proven histology identified in the Vanderbilt indeterminate pulmonary nodule registry, and all histology data and corresponding CT images were reviewed. CT images were transferred to Mayo Clinic for analysis. After exclusion of duplicate CT datasets, unanalyzable CT images and not identifiable nodules (n=27 cases), 176 nodules were segmented and analyzed, including 84 benign and 92 malignant nodules. The AUC was 0.89 (95% CI: 0.85-0.94). For comparison, the AUC Brock was 0.88 (95% CI: 0.83-0.94). When the subset of intermediate pre-test probability of lung cancer was considered (Brock score > 10 but <= 60), the AUC Brock was 0.76 (95% CI: 0.63-0.89) whereas the AUC Radiomic was 0.85 (95% CI: 0.74-0.95).
Conclusion
Our radiomic classifier demonstrates good performance characteristics on an independent retrospective validation dataset. If prospectively validated, integration into clinical decision making algorithm could significantly impact patient care.
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P1.11 - Screening and Early Detection (ID 177)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.11-02 - Acceleration of Lung Cancer Diagnosis: Utility Study for AI-Based Stratification of Pulmonary Nodules (ID 2752)
09:45 - 18:00 | Author(s): Aneri B Balar
- Abstract
Background
Lung cancer diagnostic pathway guidelines promote the use of risk stratification models. Artificial Intelligence (AI)-based risk models have been shown to achieve better diagnostic accuracy than clinical models like Mayo Clinic (Mayo) for particular clinical populations. The aim of this study is to examine whether this could translate into faster diagnosis for high-risk cancer patients.
Method
116 patients (116 nodules) have been collected from a retrospective consecutive cohort acquired at Vanderbilt University Hospital. Time to diagnosis (TTD) was defined as the number of days between the CT scan and diagnosis date. Mean TTD was calculated on the cohort on which TTD could be defined, and on a reduced group comprising of TTD >31 days only.
Risk scores for each nodule were found using the Mayo model and an AI-based Lung Cancer Prediction model (LCP) based on CT images alone. A 65% risk of cancer was taken to be the threshold at which surgical intervention is indicated (according to ACCP guidelines).
Result
Seven patients were dropped due to negative TTD, and six for having no definitive diagnosis date. The eventual cohort contained 61 cancer patients and 42 controls. Mean TTD is 140 days (Interquartile Range – IQR 1-77 days). 25 patients have TTD=0, 60 are within 31 days (28 cancers, 32 controls) and 43 (33 cancers, 10 controls) are above 32 days.
On the full cohort: Mayo risk score is >=65% for 15 cancers and 4 controls (sensitivity, 24.6%, specificity 90.5%), with a mean cancer TTD of 75 days. The LCP scores >=65% in 43 cancers and 10 controls (sensitivity, 70.5%, specificity 76.2%), mean cancer TTD 81 days.
On the reduced group: Mayo >=65% for 7 cancers and 2 controls (sensitivity, 21.2%, specificity 80.0%) with mean cancer TTD 150 days. The LCP scores >=65% in 21 cancers and 4 controls (sensitivity, 63.6%, specificity 60.0%), with mean cancer TTD 156 days.
Conclusion
The LCP risk model could potentially accelerate the diagnosis in 40% more cancer patients who were not worked up fully in the month following a scan (the jump in sensitivity going from Mayo to LCP risk >=65% is 42.4%). For these patients, time to a cancer diagnosis and treatment could be shortened by up to 156 days compared to recommendations if applying the Mayo risk model.
