Radiomics for the prediction of overall survival in patients with bladder cancer prior to radical cystectomy

Please always quote using this URN: urn:nbn:de:bvb:20-opus-288098
  • (1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48–74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657–0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617–0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Piotr Woźnicki, Fabian Christopher Laqua, Katharina Messmer, Wolfgang Gerhard Kunz, Christian Stief, Dominik Nörenberg, Andrea Schreier, Jan Wójcik, Johannes Ruebenthaler, Michael Ingrisch, Jens Ricke, Alexander Buchner, Gerald Bastian Schulz, Eva Gresser
URN:urn:nbn:de:bvb:20-opus-288098
Document Type:Journal article
Faculties:Medizinische Fakultät / Institut für diagnostische und interventionelle Radiologie (Institut für Röntgendiagnostik)
Language:English
Parent Title (English):Cancers
ISSN:2072-6694
Year of Completion:2022
Volume:14
Issue:18
Article Number:4449
Source:Cancers (2022) 14:18, 4449. https://doi.org/10.3390/cancers14184449
DOI:https://doi.org/10.3390/cancers14184449
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:bladder cancer; outcome prediction; radical cystectomy; radiomics
Release Date:2023/08/18
Date of first Publication:2022/09/13
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International