Preoperative prediction of the HER2 status and prognosis of patients with endometrial cancer using multiparametric MRI-based radiomics: a multicenter study.
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| Title: | Preoperative prediction of the HER2 status and prognosis of patients with endometrial cancer using multiparametric MRI-based radiomics: a multicenter study. |
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| Authors: | Lin G; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Chen W; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Chen Y; Department of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Shi C; Department of Interventional Vascular Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 323000, China., Cao J; Department of Pathology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Mao W; Department of Pathology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Zhao C; Department of Gynecology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Zhou H; Department of Gynecology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Hu Y; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Xia S; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Yang W; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Xu M; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Chen M; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China., Ji J; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China. jjstcty@wmu.edu.cn.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. jjstcty@wmu.edu.cn., Lu C; Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China. luchenying@zju.edu.cn.; Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. luchenying@zju.edu.cn. |
| Source: | Scientific reports [Sci Rep] 2025 Oct 13; Vol. 15 (1), pp. 35590. Date of Electronic Publication: 2025 Oct 13. |
| Publication Type: | Journal Article; Multicenter Study |
| Language: | English |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| MeSH Terms: | Endometrial Neoplasms*/diagnostic imaging , Endometrial Neoplasms*/pathology , Endometrial Neoplasms*/metabolism , Endometrial Neoplasms*/surgery , Endometrial Neoplasms*/mortality , Receptor, ErbB-2*/metabolism , Multiparametric Magnetic Resonance Imaging*/methods, Humans ; Female ; Middle Aged ; Prognosis ; Aged ; Adult ; ROC Curve ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; Radiomics |
| Abstract: | Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Non-invasive preoperative assessment of HER2 status is critical for identifying candidates for targeted therapy and personalizing treatment strategies in endometrial cancer (EC). This study aims to assess the preoperative value of multiparametric magnetic resonance imaging (MRI)-based radiomics in predicting HER2 status and prognosis of EC patients. We included 492 patients with EC divided into training (n = 215), internal validation (n = 92), and external validation cohorts 1 (n = 64) and 2 (n = 121). Models were constructed using six machine learning algorithms based on radiomics features derived from multiparametric MRI, including T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted sequences. A fusion model integrating key clinical predictors with the radiomics score (Rad-score) was created. Its predictive performance was evaluated through receiver operating characteristic (ROC) analysis, and its prognostic significance was assessed through survival analysis. HER2 (+) status was associated with poor differentiation and myometrial invasion in patients with EC. A support vector machine (SVM)-based model comprised of multiparametric MRI-based radiomics features demonstrated excellent performance in predicting HER2 status, with a mean area under the ROC curve (AUC) of 0.814 in the validation cohorts. A fusion model combining the SVM-based Rad-score with clinical factors significantly improved prediction accuracy, achieving AUCs of 0.914 in the training cohort, and 0.809-0.865 in the validation cohorts. Kaplan-Meier analysis revealed that patients with EC with predicted HER2 (+) status had worse progression-free survival than those with predicted HER2 (-) status. The fusion model based on multiparametric MRI-based radiomics features can potentially aid in the accurate preoperative prediction of HER2 status and prognosis of patients with EC, providing essential insights for clinical decision-making. (© 2025. The Author(s).) |
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| Grant Information: | 2024KY562 Zhejiang Provincial Healthcare Program; LGF20H220002 Zhejiang Public Welfare Research Program |
| Contributed Indexing: | Keywords: Endometrial cancer; Human epidermal growth factor receptor 2; Magnetic resonance imaging; Prognosis; Radiomics |
| Substance Nomenclature: | EC 2.7.10.1 (Receptor, ErbB-2) EC 2.7.10.1 (ERBB2 protein, human) |
| Entry Date(s): | Date Created: 20251013 Date Completed: 20251013 Latest Revision: 20251120 |
| Update Code: | 20251121 |
| PubMed Central ID: | PMC12518741 |
| DOI: | 10.1038/s41598-025-12894-8 |
| PMID: | 41083477 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declarations. Competing interests: The authors declare no competing interests.<br />Non-invasive preoperative assessment of HER2 status is critical for identifying candidates for targeted therapy and personalizing treatment strategies in endometrial cancer (EC). This study aims to assess the preoperative value of multiparametric magnetic resonance imaging (MRI)-based radiomics in predicting HER2 status and prognosis of EC patients. We included 492 patients with EC divided into training (n = 215), internal validation (n = 92), and external validation cohorts 1 (n = 64) and 2 (n = 121). Models were constructed using six machine learning algorithms based on radiomics features derived from multiparametric MRI, including T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted sequences. A fusion model integrating key clinical predictors with the radiomics score (Rad-score) was created. Its predictive performance was evaluated through receiver operating characteristic (ROC) analysis, and its prognostic significance was assessed through survival analysis. HER2 (+) status was associated with poor differentiation and myometrial invasion in patients with EC. A support vector machine (SVM)-based model comprised of multiparametric MRI-based radiomics features demonstrated excellent performance in predicting HER2 status, with a mean area under the ROC curve (AUC) of 0.814 in the validation cohorts. A fusion model combining the SVM-based Rad-score with clinical factors significantly improved prediction accuracy, achieving AUCs of 0.914 in the training cohort, and 0.809-0.865 in the validation cohorts. Kaplan-Meier analysis revealed that patients with EC with predicted HER2 (+) status had worse progression-free survival than those with predicted HER2 (-) status. The fusion model based on multiparametric MRI-based radiomics features can potentially aid in the accurate preoperative prediction of HER2 status and prognosis of patients with EC, providing essential insights for clinical decision-making.<br /> (© 2025. The Author(s).) |
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| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-12894-8 |