MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Abdominal radiology (New York) - (2024) vom: 11. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Roller, Lauren A [VerfasserIn] |
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Date Revised 12.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s00261-024-04198-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369575822 |
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| 245 | 1 | 0 | |a MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma |
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| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a PURPOSE: To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS) | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Fibroids | |
| 650 | 4 | |a Leiomyoma | |
| 650 | 4 | |a Leiomyosarcoma | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a Radiomics | |
| 700 | 1 | |a Wan, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xiaoyang |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Chapel, David |e verfasserin |4 aut | |
| 700 | 1 | |a Burk, Kristine S |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Shinagare, Atul B |e verfasserin |4 aut | |
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