Automated detection and segmentation of non-small cell lung cancer computed tomography images

Sergey P. Primakov, Abdalla Ibrahim, Janita E. Timmeren, Guangyao Wu, Simon A. 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 E. L. Hendriks, Olivier Morin, Arthur Jochems, Henry C. Woodruff and Philippe Lambin ()
Additional contact information
Sergey P. Primakov: Maastricht University
Abdalla Ibrahim: Maastricht University
Janita E. Timmeren: Maastricht University
Guangyao Wu: Maastricht University
Simon A. Keek: Maastricht University
Manon Beuque: Maastricht University
Renée W. Y. Granzier: Maastricht University Medical Centre+
Elizaveta Lavrova: Maastricht University
Madeleine Scrivener: Department of Radiation Oncology, Cliniques universitaires St-Luc
Sebastian Sanduleanu: Maastricht University
Esma Kayan: Maastricht University
Iva Halilaj: Maastricht University
Anouk Lenaers: Maastricht University
Jianlin Wu: Affiliated Zhongshan Hospital of Dalian University
René Monshouwer: Radboud University Medical Center
Xavier Geets: Department of Radiation Oncology, Cliniques universitaires St-Luc
Hester A. Gietema: Maastricht University Medical Centre+
Lizza E. L. Hendriks: Maastricht University Medical Center
Olivier Morin: University of California San Francisco, San Francisco
Arthur Jochems: Maastricht University
Henry C. Woodruff: Maastricht University
Philippe Lambin: Maastricht University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-30841-3

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