A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model
PurposeThis study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).MethodsWe retrospectively analyzed 232 participants from our institution...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1538854/full |
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| author | Xiang Liu Zhong-Xin Zhang Bing Zheng Min Xu Xin-Yu Cao Hai-Ming Huang |
| author_facet | Xiang Liu Zhong-Xin Zhang Bing Zheng Min Xu Xin-Yu Cao Hai-Ming Huang |
| author_sort | Xiang Liu |
| collection | DOAJ |
| description | PurposeThis study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).MethodsWe retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.ResultsIn our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR (P = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, P = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.ConclusionTo our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable. |
| format | Article |
| id | doaj-art-a6422cddfa5d48808f29d9ac26ed7aa4 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-a6422cddfa5d48808f29d9ac26ed7aa42025-08-20T03:17:44ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15388541538854A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics modelXiang Liu0Zhong-Xin Zhang1Bing Zheng2Min Xu3Xin-Yu Cao4Hai-Ming Huang5Department of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Urology Surgery, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, ChinaPurposeThis study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).MethodsWe retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.ResultsIn our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR (P = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, P = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.ConclusionTo our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.https://www.frontiersin.org/articles/10.3389/fonc.2025.1538854/fullbi-parametricultrasounddeep learningradiomicsprostate cancer |
| spellingShingle | Xiang Liu Zhong-Xin Zhang Bing Zheng Min Xu Xin-Yu Cao Hai-Ming Huang A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model Frontiers in Oncology bi-parametric ultrasound deep learning radiomics prostate cancer |
| title | A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model |
| title_full | A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model |
| title_fullStr | A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model |
| title_full_unstemmed | A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model |
| title_short | A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model |
| title_sort | retrospective study on predicting clinically significant prostate cancer via a bi parametric ultrasound based deep learning radiomics model |
| topic | bi-parametric ultrasound deep learning radiomics prostate cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1538854/full |
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