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  P2.11 - Screening and Early Detection (ID 178)

  • Event: WCLC 2019
  • Type: Poster Viewing in the Exhibit Hall
  • Track: Screening and Early Detection
  • Presentations: 1
  • Now Available
  • Moderators:
  • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall

  • 31337

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    P2.11-13 - What Is the Impact of Localised Data When Training Deep Neural Networks for Lung Cancer Prediction?  (Now Available) (ID 2485)

    10:15 - 18:15  |  Author(s): Ilaria Pulzato
    • Abstract
    • Slides

    Background
    

    Deep neural networks (DNN) have been shown to offer a viable alternative for risk cancer prediction of indeterminate pulmonary nodules (IPNs). While the type of data used for training is known to impact performance, this issue has not been extensively studied. We present, for the first time, a study of the effect of including training data that matches the clinical pathway of the independent validation dataset, a nodule clinic of incidental findings.

    Method
    

    Two identical DNNs were trained on the task of diagnosis prediction of pulmonary nodules from CT images. The first one (DNNnlst) used purely screening data from the US National Lung Screening Trial (922 cancer and 14733 benign nodules), while the second one (DNNnlst+incidental) included data of incidentally detected nodules from European hospitals (1064 cancer and 7207 benign nodules). Both models were evaluated in an independent validation set of nodules coming from a referral center in the UK (Royal Brompton and Harefield Hospital, London) consisting of baseline scans of 406 cancer and 325 benign nodules. The models were compared in terms of AUC, as well as their ability to reclassify cancer patients with intermediate risk nodules. The Intermediate risk sub-population was defined by selecting patients with nodules in the size range of 8 to 15mm, and who were followed-up within a year with CT, referred to PET-CT, or referred to biopsy. Within this sub-population, a cancer prevalence of 30% was assumed. The operating points of the cancer prediction models were chosen by setting a cancer risk of 70%, corresponding to high-risk nodules in the guidelines of the British Thoracic Society.

    Result
    

    The DNNnlst and DNNnlst+incidental models achieved an AUC of 84.33 (95%CI: 81.49, 87.15) and 87.43 (95%CI: 84.79, 89.82) respectively on the entire validation set, showing an improvement in the discrimination capabilities (p < 0.01). For reference, using the nodule’s maximum axial diameter as a predictor led to an AUC of 79.07 (95%CI: 75.73, 82.73). Additionally, considering only the intermediate risk population of the data, all of which would require workup according to guidelines, the DNNnlst+incidental model correctly classified as high risk 34.34% more cases than the DNNnlst model (sensitivity 59.39% (95%CI: 47.68, 75.17) vs. 44.21% (95%CI: 20.98, 64.42)), an improvement significant at p < 0.05.

    Conclusion
    

    Although a DNN trained only on the US lung cancer screening data could have clinical utility in an incidental setting, exposing it to further incidental data can not only increase its discriminability, as expected, but also make it a potentially more effective tool for speeding up the diagnosis of cancer patients with intermediate risk nodules and reducing unnecessary workups.

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