@article{c9555f59925f4f6e9978482e3feddcdc,
title = "Automated detection and segmentation of non-small cell lung cancer computed tomography images",
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.",
keywords = "INFORMATION, INTEROBSERVER, RADIOMICS, RADIOTHERAPY, TUMOR, VARIABILITY, Variability, Tumor, Radiotherapy, Information, Radiomics, Interobserver",
author = "S.P. Primakov and A. Ibrahim and {van Timmeren}, J.E. and G.Y. Wu and S.A. Keek and M. Beuque and R.W.Y. Granzier and E. Lavrova and M. Scrivener and S. Sanduleanu and E. Kayan and I. Halilaj and Anouk Lenaers and J.L. Wu and R. Monshouwer and X. Geets and H.A. Gietema and L.E.L. Hendriks and O. Morin and A. Jochems and H.C. Woodruff and P. Lambin",
note = "Funding Information: S.P.P., M.B., and I.H. acknowledge the financial support of the Marie Sk{\l}odowska-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{\textquoteright}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). Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = jun,
day = "14",
doi = "10.1038/s41467-022-30841-3",
language = "English",
volume = "13",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",
}