Ultrasound-based radiomics score for pre-biopsy prediction of prostate cancer to reduce unnecessary biopsies
© 2022 The Authors. The Prostate published by Wiley Periodicals LLC..
BACKGROUND: Patients undergoing prostate biopsies (PBs) suffer from low positive rates and potential risk for complications. This study aimed to develop and validate an ultrasound (US)-based radiomics score for pre-biopsy prediction of prostate cancer (PCa) and subsequently reduce unnecessary PBs.
METHODS: Between December 2015 and March 2018, 196 patients undergoing initial transrectal ultrasound (TRUS)-guided PBs were retrospectively enrolled and randomly assigned to the training or validation cohort at a ratio of 7:3. A total of 1044 radiomics features were extracted from grayscale US images of each prostate nodule. After feature selection through the least absolute shrinkage and selection operator (LASSO) regression model, the radiomics score was developed from the training cohort. The prediction nomograms were developed using multivariate logistic regression analysis based on the radiomics score and clinical risk factors. The performance of the nomograms was assessed and compared in terms of discrimination, calibration, and clinical usefulness.
RESULTS: The radiomics score consisted of five selected features. Multivariate logistic regression analysis demonstrated that the radiomics score, age, total prostate-specific antigen (tPSA), and prostate volume were independent factors for prediction of PCa (all p < 0.05). The integrated nomogram incorporating the radiomics score and three clinical risk factors reached an area under the curve (AUC) of 0.835 (95% confidence interval [CI], 0.729-0.941), thereby outperforming the clinical nomogram which based on only clinical factors and yielded an AUC of 0.752 (95% CI, 0.618-0.886) (p = 0.04). Both nomograms showed good calibration. Decision curve analysis indicated that using the integrated nomogram would add more benefit than using the clinical nomogram.
CONCLUSION: The radiomics score was an independent factor for pre-biopsy prediction of PCa. Addition of the radiomics score to the clinical nomogram shows incremental prognostic value and may help clinicians make precise decisions to reduce unnecessary PBs.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:83 |
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| Enthalten in: |
The Prostate - 83(2023), 1 vom: 08. Jan., Seite 109-118 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ou, Wei [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 15.12.2022 Date Revised 15.04.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/pros.24442 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM34723058X |
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| 245 | 1 | 0 | |a Ultrasound-based radiomics score for pre-biopsy prediction of prostate cancer to reduce unnecessary biopsies |
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| 520 | |a © 2022 The Authors. The Prostate published by Wiley Periodicals LLC. | ||
| 520 | |a BACKGROUND: Patients undergoing prostate biopsies (PBs) suffer from low positive rates and potential risk for complications. This study aimed to develop and validate an ultrasound (US)-based radiomics score for pre-biopsy prediction of prostate cancer (PCa) and subsequently reduce unnecessary PBs | ||
| 520 | |a METHODS: Between December 2015 and March 2018, 196 patients undergoing initial transrectal ultrasound (TRUS)-guided PBs were retrospectively enrolled and randomly assigned to the training or validation cohort at a ratio of 7:3. A total of 1044 radiomics features were extracted from grayscale US images of each prostate nodule. After feature selection through the least absolute shrinkage and selection operator (LASSO) regression model, the radiomics score was developed from the training cohort. The prediction nomograms were developed using multivariate logistic regression analysis based on the radiomics score and clinical risk factors. The performance of the nomograms was assessed and compared in terms of discrimination, calibration, and clinical usefulness | ||
| 520 | |a RESULTS: The radiomics score consisted of five selected features. Multivariate logistic regression analysis demonstrated that the radiomics score, age, total prostate-specific antigen (tPSA), and prostate volume were independent factors for prediction of PCa (all p < 0.05). The integrated nomogram incorporating the radiomics score and three clinical risk factors reached an area under the curve (AUC) of 0.835 (95% confidence interval [CI], 0.729-0.941), thereby outperforming the clinical nomogram which based on only clinical factors and yielded an AUC of 0.752 (95% CI, 0.618-0.886) (p = 0.04). Both nomograms showed good calibration. Decision curve analysis indicated that using the integrated nomogram would add more benefit than using the clinical nomogram | ||
| 520 | |a CONCLUSION: The radiomics score was an independent factor for pre-biopsy prediction of PCa. Addition of the radiomics score to the clinical nomogram shows incremental prognostic value and may help clinicians make precise decisions to reduce unnecessary PBs | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a nomogram | |
| 650 | 4 | |a prostate cancer | |
| 650 | 4 | |a radiomics | |
| 650 | 4 | |a ultrasound | |
| 700 | 1 | |a Lei, Jiahao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Minghao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xinyao |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Ruiming |e verfasserin |4 aut | |
| 700 | 1 | |a Long, Lingli |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Changxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Lingwu |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Junxing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Junlong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zongren |e verfasserin |4 aut | |
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