Automated detection and segmentation of non-small cell lung cancer computed tomography images.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
14 06 2022
14 06 2022
Historique:
received:
11
03
2021
accepted:
09
05
2022
entrez:
14
6
2022
pubmed:
15
6
2022
medline:
18
6
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35701415
doi: 10.1038/s41467-022-30841-3
pii: 10.1038/s41467-022-30841-3
pmc: PMC9198097
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3423Informations de copyright
© 2022. The Author(s).
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