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    EX04 - Mini Oral Abstract Session - MA08.06, MA18.02, MA19.02, MA20.11 (ID 1006)

    • Event: WCLC 2018
    • Type: Exhibit Showcase
    • Track: Advanced NSCLC
    • Presentations: 1
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      EX04.04 - Automatic Nodule Size Measurements Can Improve Prediction Accuracy Within a Brock Risk Model (ID 14018)

      10:10 - 10:15  |  Author(s): Nick Dowson

      • Abstract
      • Slides

      Background

      The intrinsic variance of manually measured nodule diameters may limit the predictive accuracy of the Brock University Cancer Prediction Model, especially given the relative weight of its coefficient within the model. Size measurements that are automatically derived may improve this prediction performance. This study aims to examine whether automatic nodule segmentation can improve the predictive efficacy of the Brock model.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      A retrospective analysis was performed on all images from the third annual screening study (T2) of the US National Lung Cancer Screening Trial (NLST). Following BTS guidelines, all nodules >=5mm were selected with no other exclusions whether on type, attenuation margin, or otherwise. This resulted in 7551 benign and 314 malignant nodules from 5373 patients with mean age 62±5 years and 3180 males. An automatic segmentation method, based on Deep Learning, was used to segment the nodule volume using a single point placed within the nodule on the CT image as an initialization. The nodule volume, V, was calculated from the segmentation and then converted to an equivalent spherical diameter, Dsph=∛(6/π V).

      We evaluated four implementations: 1) the original Brock model using as input the manual nodule sizes provided in the NLST dataset (BaselineManual); 2) the original Brock model using as input automatically calculated equivalent spherical diameter, Dsph, (BaselineAuto); 3) a Brock model re-fitted to the NLST dataset, using the manual nodule sizes (OptimManual); 4) a Brock model re-fitted to the NLST dataset, using the automatic nodule sizes (OptimAuto).

      Statistics were computed by bootstrapping across 1000 draws without replacement with 70%/30% training/testing per-patient splits. The performance of each combination was measured using Area-Under-the-Receiver-Operating-Curve (AUC-ROC) of predicting nodule malignancy. The relative weighting of size coefficients was also compared.

      4c3880bb027f159e801041b1021e88e8 Result

      The AUC-ROC was 86.5% (95% confidence interval (CI): 83.2, 89.7) for BaselineManual, 87.4% for OptManual (84.3, 90.5), 88.5% for BaselineAuto (85.7, 91.4), and 89.0% for OptAuto (86.2, 91.8). The relative absolute weight of the size coefficient is 0.46 in OptManual (95% CI: 0.42,0.49) increasing to 0.53 (0.49, 0.57) in OptAuto, an increase of 0.07 (0.06, 0.09)

      8eea62084ca7e541d918e823422bd82e Conclusion

      Automatic segmentation appears to improve the prediction accuracy of both the original Brock model and the NLST optimized version. The benefits of repeatable measurements from automatic segmentations are apparent even as direct replacements within the original model, i.e. without re-fitting. However, re-fitting the parameters increases the relative weight of the nodule size coefficient and further improves performance.

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