Automated detection and segmentation of non-small cell lung cancer computed tomography images
Sergey Primakov, Abdalla Ibrahim, Janita E. van Timmeren, Guangyao Wu, Simon Keek, Manon Beuque, Renée W. Y. Granzier, Elizaveta Lavrova, Madeleine Scrivener, Sebastian Sanduleanu, Esma Kayan, Iva Halilaj, Anouk Lenaers, Jianlin Wu, René Monshouwer, Xavier Geets, Hester A. Gietema, Lizza Hendriks, Olivier Morin, Arthur Jochems, Henry C. Woodruff, Philippe Lambin
Abstract
Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.