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)


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%.


Deep learning Lung cancer Machine learning 


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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

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