An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer
ObjectiveTo establish a combined radiomics-clinical model for the early prediction of a prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment with abiraterone acetate(AA).MethodsThe data of a total of 60 mCRPC patients from tw...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1491848/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585098619256832 |
|---|---|
| author | Yi Wu Xiang Liu Shaoxian Chen Fen Fang Feng Shi Yuwei Xia Zehong Yang Daiying Lin |
| author_facet | Yi Wu Xiang Liu Shaoxian Chen Fen Fang Feng Shi Yuwei Xia Zehong Yang Daiying Lin |
| author_sort | Yi Wu |
| collection | DOAJ |
| description | ObjectiveTo establish a combined radiomics-clinical model for the early prediction of a prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment with abiraterone acetate(AA).MethodsThe data of a total of 60 mCRPC patients from two hospitals were retrospectively analyzed and randomized into a training group(n=48) or a validation group(n=12). By extracting features from biparametric MRI, including T2-weighted imaging(T2WI), diffusion-weighted imaging(DWI), and apparent diffusion coefficient(ADC) maps, radiomics features from the training dataset were selected using least absolute shrinkage and selection operator(LASSO) regression. Four predictive models were developed to assess the efficacy of abiraterone in treating patients with mCRPC. The primary outcome variable was the PSA response following AA treatment. The performance of each model was evaluated using the area under the receiver operating characteristic curve(AUC). Univariate and multivariate analyses were performed using Cox regression to identify significant predictors of the efficacy of abiraterone treatment in patients with mCRPC.ResultsThe integrated model was constructed from seven radiomics features extracted from the T2WI, DWI, and ADC sequence images of the training data. This model demonstrated the highest AUC in both the training and validation cohorts, with values of 0.889 (95% CI, 0.764-0.961) and 0.875 (95% CI, 0.564-0.991). The Rad-score served as an independent predictor of the response to abiraterone treatment in patients with mCRPC (HR: 2.21, 95% CI: 1.01-4.44).ConclusionThe biparametric MRI-based radiomics model has the potential to predict the PSA response in patients with mCRPC following abiraterone treatment.Clinical relevance statementThe MRI-based radiomics model could be used to noninvasively identify the AA response in mCRPC patients, which is helpful for early clinical decision-making. |
| format | Article |
| id | doaj-art-d1816aa10a124de78bf07935df3e14e1 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-d1816aa10a124de78bf07935df3e14e12025-01-27T05:14:41ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011510.3389/fonc.2025.14918481491848An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancerYi Wu0Xiang Liu1Shaoxian Chen2Fen Fang3Feng Shi4Yuwei Xia5Zehong Yang6Daiying Lin7Department of Radiology, Shantou Central Hospital, Shantou, Guangdong, ChinaDepartment of Medical Imaging, Sun Yat-Sen Memorial Hospital, State Sun Yat-Sen University, Guangzhou, ChinaDepartment of Radiology, Shantou Central Hospital, Shantou, Guangdong, ChinaDepartment of Radiology, Shantou Central Hospital, Shantou, Guangdong, ChinaShanghai United Imaging Intelligence, Shanghai United Imaging Intelligence, Co. Ltd., Shanghai, ChinaShanghai United Imaging Intelligence, Shanghai United Imaging Intelligence, Co. Ltd., Shanghai, ChinaDepartment of Medical Imaging, Sun Yat-Sen Memorial Hospital, State Sun Yat-Sen University, Guangzhou, ChinaDepartment of Radiology, Shantou Central Hospital, Shantou, Guangdong, ChinaObjectiveTo establish a combined radiomics-clinical model for the early prediction of a prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment with abiraterone acetate(AA).MethodsThe data of a total of 60 mCRPC patients from two hospitals were retrospectively analyzed and randomized into a training group(n=48) or a validation group(n=12). By extracting features from biparametric MRI, including T2-weighted imaging(T2WI), diffusion-weighted imaging(DWI), and apparent diffusion coefficient(ADC) maps, radiomics features from the training dataset were selected using least absolute shrinkage and selection operator(LASSO) regression. Four predictive models were developed to assess the efficacy of abiraterone in treating patients with mCRPC. The primary outcome variable was the PSA response following AA treatment. The performance of each model was evaluated using the area under the receiver operating characteristic curve(AUC). Univariate and multivariate analyses were performed using Cox regression to identify significant predictors of the efficacy of abiraterone treatment in patients with mCRPC.ResultsThe integrated model was constructed from seven radiomics features extracted from the T2WI, DWI, and ADC sequence images of the training data. This model demonstrated the highest AUC in both the training and validation cohorts, with values of 0.889 (95% CI, 0.764-0.961) and 0.875 (95% CI, 0.564-0.991). The Rad-score served as an independent predictor of the response to abiraterone treatment in patients with mCRPC (HR: 2.21, 95% CI: 1.01-4.44).ConclusionThe biparametric MRI-based radiomics model has the potential to predict the PSA response in patients with mCRPC following abiraterone treatment.Clinical relevance statementThe MRI-based radiomics model could be used to noninvasively identify the AA response in mCRPC patients, which is helpful for early clinical decision-making.https://www.frontiersin.org/articles/10.3389/fonc.2025.1491848/fullneoplasms (prostate)biparametric MRIradiomics modelabirateronemetastatic castration-resistant prostate cancer |
| spellingShingle | Yi Wu Xiang Liu Shaoxian Chen Fen Fang Feng Shi Yuwei Xia Zehong Yang Daiying Lin An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer Frontiers in Oncology neoplasms (prostate) biparametric MRI radiomics model abiraterone metastatic castration-resistant prostate cancer |
| title | An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer |
| title_full | An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer |
| title_fullStr | An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer |
| title_full_unstemmed | An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer |
| title_short | An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer |
| title_sort | mri radiomics model for predicting a prostate specific antigen response following abiraterone treatment in patients with metastatic castration resistant prostate cancer |
| topic | neoplasms (prostate) biparametric MRI radiomics model abiraterone metastatic castration-resistant prostate cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1491848/full |
| work_keys_str_mv | AT yiwu anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT xiangliu anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT shaoxianchen anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT fenfang anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT fengshi anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT yuweixia anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT zehongyang anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT daiyinglin anmriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT yiwu mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT xiangliu mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT shaoxianchen mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT fenfang mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT fengshi mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT yuweixia mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT zehongyang mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer AT daiyinglin mriradiomicsmodelforpredictingaprostatespecificantigenresponsefollowingabirateronetreatmentinpatientswithmetastaticcastrationresistantprostatecancer |