CT-based radiomics to predict the pathological grade of bladder cancer.
Pattern recognition
Radiomics
Retrospective studies
Tomography, X-ray computed
Urinary bladder neoplasms
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
06
02
2020
accepted:
14
04
2020
revised:
16
03
2020
pubmed:
1
7
2020
medline:
16
3
2021
entrez:
1
7
2020
Statut:
ppublish
Résumé
To build a CT-based radiomics model to predict the pathological grade of bladder cancer (BCa) preliminarily. Patients with surgically resected and pathologically confirmed BCa and who received CT urography (CTU) in our institution from October 2014 to September 2017 were retrospectively enrolled and randomly divided into training and validation groups. After feature extraction, we calculated the linear dependent coefficient between features to eliminate the collinearity. F-test was then used to identify the best features related to pathological grade. The logistic regression method was used to build the prediction model, and diagnostic performance was analyzed by plotting receiver operating characteristic (ROC) curve and calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Out of 145 included patients, 108 constituted the training group and 37 the validation group. The AUC value of the radiomics prediction model to diagnose the pathological grade of BCa was 0.950 (95% confidence interval [CI] 0.912-0.988) in the training group and 0.860 (95% CI 0.742-0.979) in the validation group, respectively. In the validation group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 83.8%, 88.5%, 72.7%, 88.5%, and 72.7%, respectively. CT-based radiomics model can differentiate high-grade from low-grade BCa with a fairly good diagnostic performance. •CT-based radiomics model can predict the pathological grade of bladder cancer. •This model has good diagnostic performance to differentiate high-grade and low-grade bladder cancer. •This preoperative and non-invasive prediction method might become an important addition to biopsy.
Identifiants
pubmed: 32601949
doi: 10.1007/s00330-020-06893-8
pii: 10.1007/s00330-020-06893-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6749-6756Subventions
Organisme : National Natural Science Foundation of China
ID : 81901742
Organisme : National Natural Science Foundation of China
ID : 91859119
Organisme : Natural Science Foundation of Beijing Municipality
ID : 7192176
Organisme : Clinical and Translational Research Project of Chinese Academy of Medical Sciences
ID : 2019XK320028
Organisme : National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences
ID : 2018PT32003 and 2019PT320008
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