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K. Desai
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P1.06 - Poster Session/ Screening and Early Detection (ID 218)
- Event: WCLC 2015
- Type: Poster
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/07/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P1.06-023 - Addition of Low Dose Computed Tomography Image-Features Improves Diagnostic Accuracy for Indeterminate Pulmonary Nodules (ID 1019)
09:30 - 09:30 | Author(s): K. Desai
- Abstract
Background:
Lung cancer is the leading cause of cancer related deaths world-wide. While low dose computed tomography (LDCT) screening of the high risk patient population was recently shown to decrease deaths from lung cancer by 20%, LDCT also resulted in 18% over-diagnosis [c.f. Patz-E.-F.-JAMA-2003] with a positive predictive value of only 52.9% when a suspicious LDCT finding led to a biopsy [c.f. Church-T.-NEJM-368-2003]. We tested whether combining novel image-features (IF) with routinely collected baseline-features (BF) can improve the accuracy of diagnosing suspicious findings on baseline LDCT.
Methods:
This exploratory case-control study included N=123 (66-cancer, 57-no-cancer) high risk subjects with at least one suspicious finding (nodule >= 8mm [c.f. Lung-RADS-ACR-2014]) on baseline LDCT screening at Vanderbilt University on a VCT Discovery (GE-Healthcare, UK) or a Brilliance iCT 128 SP (Philips, Amsterdam) system. The cohort was randomly divided into a separate training-set (N=55, 32-cancer, 23-no-cancer) and a test-set (N=68, 34-cancer, 34-no-cancer). All model training and leave-one-out cross-validation were strictly restricted to the training-set. Performance was evaluated on the unseen test-set. Definitive lung cancer or no-cancer diagnosis, smoking history and at least 6 baseline-features (BF6) viz. age, family-history, pack-smoking-years, body-mass-index, nodule-location, nodule-size were recorded for all subjects. Baseline lung cancer predictions were generated by (a) using the Gould-model [c.f. Gould,M.-Chest-2007] and (b) fitting an Elastic-Net Regularized Generalized Linear Model (GLMnet [c.f. Zou-H.-Journ-Royal-Stats-Soc-B-2005]) to BF6. The final baseline model (“GLMnet:BF”) effectively utilized 4 baseline-features with the coefficients for age and body-mass-index shrunk to zero. New LDCT specific information was extracted by computing 589 intensity, shape, surface and texture features (IF589) [c.f. Aerts-H.-Nat-Comm-S2014, Way-T.-Med-Phys-2009] from a 3D volume-of-interest (VOI) encompassing a rough Graph-cuts [c.f. Li-K.-IEEE-PAMI-2006] segmentation for each suspicious nodule. A GLMnet was fit to all 595 features (BF6 and IF589) yielding a final enhanced model (“GLMnet:BF+IF”), which contained 12 features after GLMnet shrinkage : 10 IF related to VOI energy, nodule shape and surface statistics and image intensity variability and 2 BF (family-history, nodule-location).
Results:
Baseline AUC increase by 7.4% from 0.81 (Gould-model) and 0.80 (GLMnet:BF) to 0.87 (GLMnet:BF+IF). At 88% sensitivity, false positive rate reduced by 60% from 56% (Gould-model) and 44% (GLMnet:BF) to 18% (GLMnet:BF+IF); accuracy improved from 65% (Gould-model) and 71% (GLMnet:BF) to 84% (GLMnet:BF+IF). Fig.1 below shows more details: Figure 1
Conclusion:
This initial exploratory analysis showed that image-features extracted from suspicious LDCT findings may help reduce the number of unnecessary biopsies. Additional validation studies are warranted to determine the value of this structural imaging-based approach.
