Advertisement

Transfer Learning Approach to Predict Biopsy-Confirmed Malignancy of Lung Nodules from Imaging Data: A Pilot Study

  • William Lindsay
  • Jiancong Wang
  • Nicholas Sachs
  • Eduardo Barbosa
  • James GeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

The goal of this study is to train and assess the performance of a deep 3D convolutional network (3D-CNN) in classifying indeterminate lung nodules as either benign or malignant based solely on diagnostic-grade thoracic CT imaging. While prior studies have relied upon subjective ratings of malignancy by radiologists, our study relies only on data from subjects with biopsy-proven ground truth labels. Our dataset includes 796 patients who underwent CT-guided lung biopsy at one institution between 2012 and 2017. All patients have pathology-confirmed diagnosis (from CT-guided biopsy) and high-resolution CT imaging data acquired immediately prior to biopsy. Lesion location was manually determined using the biopsy guidance CT scan as a reference for a subset of 86 patients for this proof-of-concept study. Rather than training the network without a priori knowledge, which risks over fitting on small datasets, we employed transfer learning, taking the initial layers of our network from an existing neural network trained on a distinct but similar dataset. We then evaluated our network on a held out test set, achieving an area under the receiver operating characteristic curve (AUC) of 0.70 and a classification accuracy of 71%.

Keywords

Deep learning Lung cancer Machine learning 

References

  1. 1.
    Chon, A., et al.: Deep convolutional neural networks for lung cancer detection. Technical report, Stanford University (2017)Google Scholar
  2. 2.
    Deppen, S.A., et al.: Predicting lung cancer prior to surgical resection in patients with lung nodules. J. Thorac. Oncol. 9(10), 1477–1484 (2014)CrossRefGoogle Scholar
  3. 3.
    Foucard, L.: Github Repository (2017). https://github.com/LouisFoucard/DSB17
  4. 4.
    Gurney, J.W.: Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part II. Application. Radiology 186(2), 415–22 (1993)CrossRefGoogle Scholar
  5. 5.
    Gurney, J.W., Lyddon, D.M., McKay, J.A.: Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian Analysis. Part II. Application. Radiology 186(2), 415–422 (1993)CrossRefGoogle Scholar
  6. 6.
    Hawkins, S., et al.: Predicting malignant nodules from screening CT scans. J. Thorac. Oncol. 11(12), 2120–2128 (2016)CrossRefGoogle Scholar
  7. 7.
    Surveillance, Epidemiology, and End Results (SEER) Program (2008–2014). www.seer.cancer.gov
  8. 8.
    Lokhandwala, T., et al.: Costs of diagnostic assessment for lung cancer: a medicare claims analysis. Clin. Lung Cancer 18(1), e27–34 (2017).  https://doi.org/10.1016/j.cllc.2016.07.006CrossRefGoogle Scholar
  9. 9.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, NIPS 2014, vol. 27. NIPS Foundation (2014)Google Scholar
  10. 10.
    Zhao, X., Liu, L., Qi, S., Teng, Y., Li, J., Qian, W.: Agile convolutional neural network for pulmonary nodule classification using CT images. Int. J. Comput. Assist. Radiol. Surg. 13(4), 585–95 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • William Lindsay
    • 1
  • Jiancong Wang
    • 1
  • Nicholas Sachs
    • 1
  • Eduardo Barbosa
    • 1
  • James Gee
    • 1
    Email author
  1. 1.University of PennsylvaniaPhiladelphiaUSA

Personalised recommendations