Cargando…

Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images

BACKGROUND: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. OBJECTIVE: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical mode...

Descripción completa

Detalles Bibliográficos
Autores principales: Qian, Jing, Yang, Ling, Hu, Su, Gu, Siqian, Ye, Juan, Li, Zhenkai, Du, Hongdi, Shen, Hailin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201948/
https://www.ncbi.nlm.nih.gov/pubmed/35719972
http://dx.doi.org/10.3389/fonc.2022.899897
_version_ 1784728426423255040
author Qian, Jing
Yang, Ling
Hu, Su
Gu, Siqian
Ye, Juan
Li, Zhenkai
Du, Hongdi
Shen, Hailin
author_facet Qian, Jing
Yang, Ling
Hu, Su
Gu, Siqian
Ye, Juan
Li, Zhenkai
Du, Hongdi
Shen, Hailin
author_sort Qian, Jing
collection PubMed
description BACKGROUND: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. OBJECTIVE: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. METHODS: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. RESULTS: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. CONCLUSION: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.
format Online
Article
Text
id pubmed-9201948
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92019482022-06-17 Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images Qian, Jing Yang, Ling Hu, Su Gu, Siqian Ye, Juan Li, Zhenkai Du, Hongdi Shen, Hailin Front Oncol Oncology BACKGROUND: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. OBJECTIVE: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. METHODS: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. RESULTS: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. CONCLUSION: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9201948/ /pubmed/35719972 http://dx.doi.org/10.3389/fonc.2022.899897 Text en Copyright © 2022 Qian, Yang, Hu, Gu, Ye, Li, Du and Shen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Qian, Jing
Yang, Ling
Hu, Su
Gu, Siqian
Ye, Juan
Li, Zhenkai
Du, Hongdi
Shen, Hailin
Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
title Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
title_full Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
title_fullStr Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
title_full_unstemmed Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
title_short Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
title_sort feasibility study on predicting recurrence risk of bladder cancer based on radiomics features of multiphase ct images
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201948/
https://www.ncbi.nlm.nih.gov/pubmed/35719972
http://dx.doi.org/10.3389/fonc.2022.899897
work_keys_str_mv AT qianjing feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT yangling feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT husu feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT gusiqian feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT yejuan feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT lizhenkai feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT duhongdi feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages
AT shenhailin feasibilitystudyonpredictingrecurrenceriskofbladdercancerbasedonradiomicsfeaturesofmultiphasectimages