التفاصيل البيبلوغرافية
| العنوان: |
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application |
| المؤلفون: |
Hangjie Ji, Kyle Lafata, Yvonne Mowery, David Brizel, Andrea L. Bertozzi, Fang-Fang Yin, Chunhao Wang |
| المصدر: |
Frontiers in Oncology, Vol 12 (2022) |
| بيانات النشر: |
Frontiers Media S.A., 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: |
biological modeling, deep learning, image outcome prediction, radiotherapy, 18FDG-PET, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: |
PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index ( |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2234-943X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fonc.2022.895544/full; https://doaj.org/toc/2234-943X |
| DOI: |
10.3389/fonc.2022.895544 |
| URL الوصول: |
https://doaj.org/article/09c0628f83634ba89dfc9bca51b74e10 |
| رقم الانضمام: |
edsdoj.09c0628f83634ba89dfc9bca51b74e10 |
| قاعدة البيانات: |
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