Algorithms; Humans; Prospective Studies; Tomography, X-Ray Computed/methods; Carcinoma, Non-Small-Cell Lung/diagnostic imaging; Lung Neoplasms/diagnostic imaging; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Tomography, X-Ray Computed; Chemistry (all); Biochemistry, Genetics and Molecular Biology (all); Physics and Astronomy (all); General Physics and Astronomy; General Biochemistry, Genetics and Molecular Biology; General Chemistry; Multidisciplinary
Abstract :
[en] 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.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Primakov, Sergey P ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Ibrahim, Abdalla Khalil ; Université de Liège - ULiège > GIGA ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands ; Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany ; Department of Radiology, Columbia University Irving Medical Center, New York, USA
van Timmeren, Janita E ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands ; Department of Radiation Oncology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
Wu, Guangyao; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands ; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Keek, Simon A; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Beuque, Manon ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Granzier, Renée W Y ; Department of Surgery, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
Lavrova, Elizaveta ; Université de Liège - ULiège > GIGA ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Scrivener, Madeleine; Department of Radiation Oncology, Cliniques universitaires St-Luc, Brussels, Belgium
Sanduleanu, Sebastian; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Kayan, Esma; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Halilaj, Iva ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Lenaers, Anouk ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands ; Department of Surgery, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
Wu, Jianlin; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
Monshouwer, René ; Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
Geets, Xavier; Department of Radiation Oncology, Cliniques universitaires St-Luc, Brussels, Belgium
Gietema, Hester A ; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
Hendriks, Lizza E L ; Department of Pulmonary Diseases, GROW - School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, the Netherlands
Morin, Olivier ; Department of Radiation Oncology, University of California San Francisco, San Francisco, California, CA, USA
Jochems, Arthur; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
Woodruff, Henry C ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
Lambin, Philippe ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands. philippe.lambin@maastrichtuniversity.nl ; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. philippe.lambin@maastrichtuniversity.nl
S.P.P., M.B., and I.H. acknowledge the financial support of the Marie Skłodowska-Curie grant (PREDICT - ITN - No. 766276). A.I. acknowledges the financial support from the Liege-Maastricht imaging valley grant. P.L. and H.C.W. acknowledge financial support from ERC advanced grant (ERC-ADG-2015 n° 694812 - Hypoximmuno), ERC-2018-PoC: 813200-CL-IO, ERC-2020-PoC: 957565-AUTO.DISTINCT, SME Phase 2 (RAIL n°673780), EUROSTARS (DART, DECIDE, COMPACT-12053), the European Union’s Horizon 2020 research and innovation program under grant agreement: ImmunoSABR n° 733008, FETOPEN- SCANnTREAT n° 899549, CHAIMELEON n° 952172, EuCanImage n° 952103, TRANSCAN Joint Transnational Call 2016 (JTC2016 CLEARLY n° UM 2017-8295), and Interreg V-A Euregio Meuse-Rhine (EURADIOMICS n° EMR4).
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