Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer.
Diagnosis, Differential
Female
Humans
Image Processing, Computer-Assisted
/ methods
Lung Neoplasms
/ diagnostic imaging
Male
Middle Aged
Nomograms
Pattern Recognition, Automated
/ methods
Positron Emission Tomography Computed Tomography
/ methods
ROC Curve
Retrospective Studies
Tomography, X-Ray Computed
/ methods
Tuberculosis, Pulmonary
/ diagnostic imaging
Active pulmonary tuberculosis
Diagnosis
FDG-PET/CT
Lung cancer
Radiomics
Journal
Molecular imaging and biology
ISSN: 1860-2002
Titre abrégé: Mol Imaging Biol
Pays: United States
ID NLM: 101125610
Informations de publication
Date de publication:
04 2021
04 2021
Historique:
received:
15
04
2020
accepted:
29
09
2020
revised:
25
09
2020
pubmed:
9
10
2020
medline:
16
11
2021
entrez:
8
10
2020
Statut:
ppublish
Résumé
We aim to accurately differentiate between active pulmonary tuberculosis (TB) and lung cancer (LC) based on radiomics and semantic features as extracted from pre-treatment positron emission tomography/X-ray computed tomography (PET/CT) images. A total of 174 patients (77/97 pulmonary TB/LC as confirmed by pathology) were retrospectively selected, with 122 in the training cohort and 52 in the validation cohort. Four hundred eighty-seven radiomics features were initially extracted to quantify phenotypic characteristics of the lesion region in both PET and CT images. Eleven semantic features were additionally defined by two experienced nuclear medicine physicians. Feature selection was performed in 5 steps to enable derivation of robust and effective signatures. Multivariable logistic regression analysis was subsequently used to develop a radiomics nomogram. The calibration, discrimination, and clinical usefulness of the nomogram were evaluated in both the training and independent validation cohorts. The individualized radiomics nomogram, which combined PET/CT radiomics signature with semantic features, demonstrated good calibration and significantly improved the diagnostic performance with respect to the semantic model alone or PET/CT signature alone in training cohort (AUC 0.97 vs. 0.94 or 0.91, p = 0.0392 or 0.0056), whereas did not significantly improve the performance in validation cohort (AUC 0.93 vs. 0.89 or 0.91, p = 0.3098 or 0.3323). The radiomics nomogram showed potential for individualized differential diagnosis between solid active pulmonary TB and solid LC, although the improvement of performance was not significant relative to semantic model.
Identifiants
pubmed: 33030709
doi: 10.1007/s11307-020-01550-4
pii: 10.1007/s11307-020-01550-4
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
287-298Références
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