Radiomics for the prediction of overall survival in patients with bladder cancer prior to radical cystectomy
(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....
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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 September 2022
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| In: |
Cancers
Year: 2022, Jahrgang: 14, Heft: 18, Pages: 1-12 |
| ISSN: | 2072-6694 |
| DOI: | 10.3390/cancers14184449 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/cancers14184449 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2072-6694/14/18/4449 
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| Verfasserangaben: | 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 and Eva Gresser |
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| 245 | 1 | 0 | |a Radiomics for the prediction of overall survival in patients with bladder cancer prior to radical cystectomy |c 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 and Eva Gresser |
| 264 | 1 | |c 13 September 2022 | |
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| 500 | |a Dieser Artikel gehört zum Special issue: Advance and new insights in bladder cancer | ||
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| 520 | |a (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. | ||
| 650 | 4 | |a bladder cancer | |
| 650 | 4 | |a outcome prediction | |
| 650 | 4 | |a radical cystectomy | |
| 650 | 4 | |a radiomics | |
| 700 | 1 | |a Laqua, Fabian Christopher |e VerfasserIn |4 aut | |
| 700 | 1 | |a Messmer, Katharina |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kunz, Wolfgang Gerhard |e VerfasserIn |4 aut | |
| 700 | 1 | |a Stief, Christian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Nörenberg, Dominik |e VerfasserIn |0 (DE-588)1064719996 |0 (DE-627)814432123 |0 (DE-576)424084228 |4 aut | |
| 700 | 1 | |a Schreier, Andrea |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wójcik, Jan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ruebenthaler, Johannes |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ingrisch, Michael |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ricke, Jens |e VerfasserIn |4 aut | |
| 700 | 1 | |a Buchner, Alexander |e VerfasserIn |4 aut | |
| 700 | 1 | |a Schulz, Gerald Bastian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Gresser, Eva |e VerfasserIn |4 aut | |
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