دورية أكاديمية
Whole-volume tumor MRI radiomics for prognostic modeling in endometrial cancer
| العنوان: | Whole-volume tumor MRI radiomics for prognostic modeling in endometrial cancer |
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| المؤلفون: | Fasmer, Kristine Eldevik, Hodneland, Erlend, Dybvik, Julie Andrea, Wagner-Larsen, Kari Strøno, Trovik, Jone, Salvesen, Øyvind, Krakstad, Camilla, Haldorsen, Ingfrid S. |
| المصدر: | 1-10 ; Journal of Magnetic Resonance Imaging |
| بيانات النشر: | Wiley |
| سنة النشر: | 2020 |
| المجموعة: | NTNU Open Archive (Norges teknisk-naturvitenskapelige universitet / Norwegian University of Science and Technology) |
| الوصف: | Background In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC. Purpose To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease. Study Type Retrospective. Population A total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30). Field Strength/Sequence Axial oblique T1‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection. Assessment Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area. Statistical Tests Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT) and validation (AUCV) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model. Results The whole‐tumor radiomic signatures yielded AUCT/AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P < ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1053-1807 |
| العلاقة: | Journal of Magnetic Resonance Imaging. 2020, 1-10.; urn:issn:1053-1807; https://hdl.handle.net/11250/2726329Test; https://doi.org/10.1002/jmri.27444Test; cristin:1859469 |
| DOI: | 10.1002/jmri.27444 |
| الإتاحة: | https://doi.org/10.1002/jmri.27444Test https://hdl.handle.net/11250/2726329Test |
| حقوق: | Navngivelse-Ikkekommersiell 4.0 Internasjonal ; http://creativecommons.org/licenses/by-nc/4.0/deed.noTest |
| رقم الانضمام: | edsbas.20AAB98B |
| قاعدة البيانات: | BASE |
| تدمد: | 10531807 |
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| DOI: | 10.1002/jmri.27444 |