A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors
© 2019 International Society for Magnetic Resonance in Medicine..
BACKGROUND: Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients.
PURPOSE: To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk.
STUDY TYPE: Retrospective.
POPULATION: Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21).
FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences.
ASSESSMENT: Radiomics features were extracted from the T2 W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine-based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle-invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve.
STATISTICAL TESTS: Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction.
RESULTS: Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone.
DATA CONCLUSION: The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence.
LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893-1904.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
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| Enthalten in: |
Journal of magnetic resonance imaging : JMRI - 50(2019), 6 vom: 01. Dez., Seite 1893-1904 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Xiaopan [VerfasserIn] |
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| Links: |
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| Themen: |
Bladder cancer |
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| Anmerkungen: |
Date Completed 29.10.2020 Date Revised 01.12.2020 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jmri.26749 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM296016152 |
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| 041 | |a eng | ||
| 100 | 1 | |a Xu, Xiaopan |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 29.10.2020 | ||
| 500 | |a Date Revised 01.12.2020 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2019 International Society for Magnetic Resonance in Medicine. | ||
| 520 | |a BACKGROUND: Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients | ||
| 520 | |a PURPOSE: To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk | ||
| 520 | |a STUDY TYPE: Retrospective | ||
| 520 | |a POPULATION: Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21) | ||
| 520 | |a FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences | ||
| 520 | |a ASSESSMENT: Radiomics features were extracted from the T2 W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine-based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle-invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve | ||
| 520 | |a STATISTICAL TESTS: Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction | ||
| 520 | |a RESULTS: Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone | ||
| 520 | |a DATA CONCLUSION: The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence | ||
| 520 | |a LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893-1904 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a SVM-RFE | |
| 650 | 4 | |a bladder cancer | |
| 650 | 4 | |a multiparametric MRI | |
| 650 | 4 | |a nomogram | |
| 650 | 4 | |a recurrence prediction | |
| 700 | 1 | |a Wang, Huanjun |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Fan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Shurong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhongwei |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Zhengrong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Hongbing |e verfasserin |4 aut | |
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