Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study.
Adenocarcinoma of Lung
/ diagnostic imaging
Adult
Aged
Aged, 80 and over
Carcinoma, Non-Small-Cell Lung
/ diagnostic imaging
Carcinoma, Squamous Cell
/ diagnostic imaging
Female
Follow-Up Studies
Humans
Lung Neoplasms
/ diagnostic imaging
Male
Middle Aged
Neoplasm Recurrence, Local
/ diagnostic imaging
Pneumonectomy
/ mortality
Prognosis
Retrospective Studies
Survival Rate
Young Adult
Adjuvant chemotherapy
NSCLC
Quantitative imaging
Radiomics
Surgery
Journal
Lung cancer (Amsterdam, Netherlands)
ISSN: 1872-8332
Titre abrégé: Lung Cancer
Pays: Ireland
ID NLM: 8800805
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
16
10
2019
revised:
03
02
2020
accepted:
25
02
2020
pubmed:
3
3
2020
medline:
13
4
2021
entrez:
3
3
2020
Statut:
ppublish
Résumé
To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). CT scans of 610 patients with early stage (IA, IB, IIA) NSCLC from four independent cohorts were evaluated. A total of 350 patients from Cleveland Clinic Foundation and University of Pennsylvania were divided into two equal sets for training (D A feature selection strategy focusing on both feature discriminability and stability resulted in the classifier having a higher discriminability on validation datasets compared to the discriminability alone criteria in discriminating cancer recurrence (D Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.
Identifiants
pubmed: 32120229
pii: S0169-5002(20)30315-9
doi: 10.1016/j.lungcan.2020.02.018
pmc: PMC7141152
mid: NIHMS1569794
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
90-97Subventions
Organisme : NCI NIH HHS
ID : R01 CA216579
Pays : United States
Organisme : NCRR NIH HHS
ID : C06 RR012463
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA199374
Pays : United States
Organisme : BLRD VA
ID : I01 BX004121
Pays : United States
Organisme : NIBIB NIH HHS
ID : R43 EB028736
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA239055
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA220581
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA202752
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA208236
Pays : United States
Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Vamsidhar Velcheti is a Consultant for Clovis Oncology, Genentech, Bristol-Myers Squibb, Merck, Celgene, Foundation Medicine, AstraZeneca/MedImmune and Genoptix. He has received research funding from Genentech, Trovagene, Eisai, OncoPlex Diagnostics, Alkermes, NantOmics, Genoptix, Altor BioScience, Merck, Bristol-Myers Squibb, Atreca, Heat Biologics and Leap Therapeutics. Anant Madabhushi is an equity holder in Elucid Bioimaging and in Inspirata Inc. He is also a scientific advisory consultant for Inspirata Inc and also sits on its scientific advisory board. He is also an equity holder in Inspirata Inc. Additionally his technology has been licensed to Elucid Bioimaging and Inspirata Inc. He is also involved in a NIH U24 grant with PathCore Inc. Part of his research work is sponsored by Philips.
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