Rankings
Publications
Sources
Publishers
Scholars
Organizations
About
Login
Register
Home
Publications
Data from A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Home
Publications
Data from A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Data from A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
SW
Shaoxu Wu
Shaoxu Wu
JZ
Junjiong Zheng
Junjiong Zheng
YL
Yong Li
Yong Li
HY
Hao Yu
Hao Yu
SS
Siya Shi
Siya Shi
WX
Weibin Xie
Weibin Xie
HL
Hao Liu
Hao Liu
YS
Yangfan Su
Yangfan Su
JH
Jian Huang
Jian Huang
TL
Tianxin Lin
Tianxin Lin
Open Access
Publisher Website
Google Scholar
Cite
Download
Share
Download
31 March 2023
other
Published by
American Association for Cancer Research (AACR)
https://doi.org/10.1158/1078-0432.c.6525144
Abstract
Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in bladder cancer.Experimental Design: A total of 118 eligible bladder cancer patients were divided into a training set (n = 80) and a validation set (n = 38). Radiomics features were extracted from arterial-phase CT images of each patient. A radiomics signature was then constructed with the least absolute shrinkage and selection operator algorithm in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. Nomogram performance was assessed in the training set and validated in the validation set. Finally, decision curve analysis was performed with the combined training and validation set to estimate the clinical usefulness of the nomogram.Results: The radiomics signature, consisting of nine LN status–related features, achieved favorable prediction efficacy. The radiomics nomogram, which incorporated the radiomics signature and CT-reported LN status, also showed good calibration and discrimination in the training set [AUC, 0.9262; 95% confidence interval (CI), 0.8657–0.9868] and the validation set (AUC, 0.8986; 95% CI, 0.7613–0.9901). The decision curve indicated the clinical usefulness of our nomogram. Encouragingly, the nomogram also showed favorable discriminatory ability in the CT-reported LN-negative (cN0) subgroup (AUC, 0.8810; 95% CI, 0.8021–0.9598).Conclusions: The presented radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the radiomics signature and CT-reported LN status, shows favorable predictive accuracy for LN metastasis in patients with bladder cancer. Multicenter validation is needed to acquire high-level evidence for its clinical application. Clin Cancer Res; 23(22); 6904–11. ©2017 AACR.
Keywords
METASTASIS
RADIOMICS NOMOGRAM
VALIDATION SET
PATIENTS WITH BLADDER CANCER
AUC
FAVORABLE PREDICTION
All Articles
Open Access