دورية أكاديمية
Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction .
| العنوان: | Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction . |
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| المؤلفون: | Jiao C; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Lao Y; Department of Radiation Oncology, UC Los Angeles, Los Angeles, CA 90095, United States of America., Zhang W; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Braunstein S; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Salans M; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Villanueva-Meyer J; Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94143, United States of America., Hervey-Jumper SL; Department of Neurosurgery, UC San Francisco, San Francisco, CA 94143, United States of America., Yang B; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Morin O; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Valdes G; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Fan Z; Department of Radiology, University of Southern California, Los Angeles, CA 90033, United States of America., Shiroishi M; Department of Radiology, University of Southern California, Los Angeles, CA 90033, United States of America., Zada G; Department of Neurosurgery, University of Southern California, Los Angeles, CA 90033, United States of America., Sheng K; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America., Yang W; Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America. |
| المصدر: | Physics in medicine and biology [Phys Med Biol] 2024 Jul 25; Vol. 69 (15). Date of Electronic Publication: 2024 Jul 25. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bristol : IOP Publishing |
| مواضيع طبية MeSH: | Glioblastoma*/diagnostic imaging , Glioblastoma*/pathology , Magnetic Resonance Imaging* , Brain Neoplasms*/diagnostic imaging , Brain Neoplasms*/pathology , Neoplasm Recurrence, Local*/diagnostic imaging, Humans ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Retrospective Studies ; Recurrence ; Male ; Female ; Middle Aged |
| مستخلص: | Objective. We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction. Approach. 57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed. Main results. The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVM (© 2024 Institute of Physics and Engineering in Medicine.) |
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| معلومات مُعتمدة: | R21 CA267139 United States CA NCI NIH HHS |
| فهرسة مساهمة: | Keywords: GBM recurrence prediction; feature enhancement; multi-modal fusion; stem cell niches |
| تواريخ الأحداث: | Date Created: 20240717 Date Completed: 20240725 Latest Revision: 20240809 |
| رمز التحديث: | 20240809 |
| مُعرف محوري في PubMed: | PMC11308744 |
| DOI: | 10.1088/1361-6560/ad64b8 |
| PMID: | 39019073 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1361-6560 |
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| DOI: | 10.1088/1361-6560/ad64b8 |