Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study
Abstract Background Bone metastasis of prostate cancer (PCa) is a challenging problem, leading to poor prognosis of patients. Existing biomarkers have limited sensitivity and specificity. Therefore, we urgently need a novel diagnostic tool to predict PCa bone metastasis. Methods Patient data and blo...
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BMC
2025-06-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06691-0 |
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| author | Chao Wang Xu-Pan Wei Chuan Zhou Jia Wang Yun-Feng Zhang Han He Wen-Bo Zhang Hao-Xuan Lv Fang Wang Feng-Hai Zhou |
| author_facet | Chao Wang Xu-Pan Wei Chuan Zhou Jia Wang Yun-Feng Zhang Han He Wen-Bo Zhang Hao-Xuan Lv Fang Wang Feng-Hai Zhou |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | Abstract Background Bone metastasis of prostate cancer (PCa) is a challenging problem, leading to poor prognosis of patients. Existing biomarkers have limited sensitivity and specificity. Therefore, we urgently need a novel diagnostic tool to predict PCa bone metastasis. Methods Patient data and blood samples were collected according to the inclusion and exclusion criteria. logistic regression analysis was used to screen clinical indicators and miRNAs, and radiomics was used to construct a prediction model. Finally, the performance of the model was evaluated by internal verification, external verification and Delong test. Two nomograms were successfully established by analyzing clinical data, plasma miRNAs and imaging data. Nomogram 1 predicts the presence or absence of bone metastasis; Nomogram 2 predicts whether the number of bone metastases is ≥ 4. Results Nomogram 1 constructed by tPSA, hsa-miR-548o-3p and radiomics had an AUC of 0.904. The AUC of the internal training set was 0.879, the internal test set was 0.956, and the AUC of the external data set was 0.877. The calibration curve and decision curve all performed well. Nomogram 2 constructed by ALP, hsa-miR-548o-3p and radiomics had an AUC of 0.849, with an AUC of 0.916 in the internal training set, 0.806 in the internal test set and 0.839 in the external data set. The calibration curve and decision curve showed good performance. Conclusions The combination of plasma exosomal miRNA and radiomics model has high reliability and accuracy in predicting the presence and number of bone metastases of PCa. |
| format | Article |
| id | doaj-art-8d072f4cb38b456eb7cd235ba42567c9 |
| institution | DOAJ |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-8d072f4cb38b456eb7cd235ba42567c92025-08-20T03:22:54ZengBMCJournal of Translational Medicine1479-58762025-06-0123111510.1186/s12967-025-06691-0Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center studyChao Wang0Xu-Pan Wei1Chuan Zhou2Jia Wang3Yun-Feng Zhang4Han He5Wen-Bo Zhang6Hao-Xuan Lv7Fang Wang8Feng-Hai Zhou9The First Clinical Medical College of Lanzhou UniversityDepartment of Urology, Liaocheng People’s Hospital, Liaocheng Hospital Affiliated to Shandong First Medical UniversityThe First Clinical Medical College of Lanzhou UniversityThe First Clinical Medical College of Gansu University of Chinese MedicineThe First Clinical Medical College of Lanzhou UniversityThe First Clinical Medical College of Lanzhou UniversityThe First Clinical Medical College of Gansu University of Chinese MedicineThe First Clinical Medical College of Lanzhou UniversityMedical Experimental Center of Lanzhou UniversityThe First Clinical Medical College of Lanzhou UniversityAbstract Background Bone metastasis of prostate cancer (PCa) is a challenging problem, leading to poor prognosis of patients. Existing biomarkers have limited sensitivity and specificity. Therefore, we urgently need a novel diagnostic tool to predict PCa bone metastasis. Methods Patient data and blood samples were collected according to the inclusion and exclusion criteria. logistic regression analysis was used to screen clinical indicators and miRNAs, and radiomics was used to construct a prediction model. Finally, the performance of the model was evaluated by internal verification, external verification and Delong test. Two nomograms were successfully established by analyzing clinical data, plasma miRNAs and imaging data. Nomogram 1 predicts the presence or absence of bone metastasis; Nomogram 2 predicts whether the number of bone metastases is ≥ 4. Results Nomogram 1 constructed by tPSA, hsa-miR-548o-3p and radiomics had an AUC of 0.904. The AUC of the internal training set was 0.879, the internal test set was 0.956, and the AUC of the external data set was 0.877. The calibration curve and decision curve all performed well. Nomogram 2 constructed by ALP, hsa-miR-548o-3p and radiomics had an AUC of 0.849, with an AUC of 0.916 in the internal training set, 0.806 in the internal test set and 0.839 in the external data set. The calibration curve and decision curve showed good performance. Conclusions The combination of plasma exosomal miRNA and radiomics model has high reliability and accuracy in predicting the presence and number of bone metastases of PCa.https://doi.org/10.1186/s12967-025-06691-0PCamiRNAsRadiomicsExosomesBiomarkers |
| spellingShingle | Chao Wang Xu-Pan Wei Chuan Zhou Jia Wang Yun-Feng Zhang Han He Wen-Bo Zhang Hao-Xuan Lv Fang Wang Feng-Hai Zhou Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study Journal of Translational Medicine PCa miRNAs Radiomics Exosomes Biomarkers |
| title | Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study |
| title_full | Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study |
| title_fullStr | Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study |
| title_full_unstemmed | Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study |
| title_short | Precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics: a multi-center study |
| title_sort | precise prediction of bone metastases and metastatic burden using exosomal mirnas and radiomics a multi center study |
| topic | PCa miRNAs Radiomics Exosomes Biomarkers |
| url | https://doi.org/10.1186/s12967-025-06691-0 |
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