| Autor: |
Haoming Zhuang, Beibei Li, Jingtong Ma, Patrice Monkam, Wei Qian, Dianning He |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 41000-41008 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3377560 |
| Popis: |
Ovarian cancer is one of the three most common types of gynecological cancer globally, with high-grade serous ovarian cancer being the most common and aggressive histological type. Guided treatment of high-grade serous ovarian cancer typically involves platinum-based combination chemotherapy, necessitating the assessment of whether the patient is platinum resistant. This study proposes a deep learning-based method to determine whether a patient is platinum resistant using multimodal positron emission tomography/computed tomography images. In total, 289 patients with high-grade serous ovarian cancer were included in this study. An end-to-end Squeeze-Excitation–Spatial Pyramid Pooling–Dense Convolutional Network model was built by adding a Squeeze-Excitation Block and Spatial Pyramid Pooling Layer to a Dense Convolutional Network. Multimodal data from positron emission tomography/computed tomography images of regions of interest were used to predict platinum resistance in patients. Through five-fold cross-validation, the Squeeze-Excitation–Spatial Pyramid Pooling–Dense Convolutional Network achieved a high accuracy rate and area under the curve of 92.6% and 0.93, respectively, for predicting platinum resistance in patients. The importance of incorporating the Squeeze-Excitation Block and Spatial Pyramid Pooling Layer into the deep learning model and considering multimodal data was substantiated by performing ablation studies and experiments with single-modality data. The classification results indicate that our proposed deep learning framework performs better in predicting platinum resistance in patients, which can help gynecologists make more appropriate treatment decisions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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