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Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

Authors :
Zongjie Wei
Huayun Liu
Yingjie Xv
Fangtong Liao
Quanhao He
Yongpeng Xie
Fajin Lv
Qing Jiang
Mingzhao Xiao
Source :
Heliyon, Vol 10, Iss 2, Pp e24878- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.

Details

Language :
English
ISSN :
24058440
Volume :
10
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Heliyon
Publication Type :
Academic Journal
Accession number :
edsdoj.559dc62a609a4e43a56eaaa91545dd53
Document Type :
article
Full Text :
https://doi.org/10.1016/j.heliyon.2024.e24878