التفاصيل البيبلوغرافية
| العنوان: |
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment |
| المؤلفون: |
Son, Seungyeon, Joo, Bio, Park, Mina, Suh, Sang Hyun, Oh, Hee Sang, Kim, Jun Won, Lee, Seoyoung, Ahn, Sung Jun, Lee, Jong-Min |
| المصدر: |
Frontiers in Oncology ; volume 13 ; ISSN 2234-943X |
| بيانات النشر: |
Frontiers Media SA |
| سنة النشر: |
2024 |
| المجموعة: |
Frontiers (Publisher - via CrossRef) |
| مصطلحات موضوعية: |
Cancer Research, Oncology |
| الوصف: |
Purpose/objective(s) Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| DOI: |
10.3389/fonc.2023.1273013 |
| DOI: |
10.3389/fonc.2023.1273013/full |
| الإتاحة: |
https://doi.org/10.3389/fonc.2023.1273013Test |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.6641BB00 |
| قاعدة البيانات: |
BASE |
