MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma.
Purpose: To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS). Methods: This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pa...
| Publicado en: | Abdominal Radiology Vol. 49; no. 5; pp. 1522 - 1534 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177371127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177371127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: May2024 vid: 49 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177371127 175956391 10.1007/s00261-024-04198-8 177371127 ppf: 1522 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma. aug: au: Roller, Lauren A. Wan, Qi Liu, Xiaoyang Qin, Lei Chapel, David Burk, Kristine S. Guo, Yang Shinagare, Atul B. affil: https://ror.org/04b6nzv94 Department of Radiology, Brigham and Women's Hospital, 02115, Boston, MA, USA sug: ab: Purpose: To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS). Methods: This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables. Results: 12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929). Conclusion: While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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