A Prognosis Method for Esophageal Squamous Cell Carcinoma Based on CT Image and Three-Dimensional Convolutional Neural Networks
| Autor: | Lizhi Peng, Jifeng Guo, Jian Zhu, Baosheng Li, Bo Yang, Lin Wang, Ajith Abraham, Kaipeng Fan |
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| Rok vydání: | 2020 |
| Předmět: |
Chemotherapy
medicine.medical_specialty business.industry medicine.medical_treatment Cancer 02 engineering and technology medicine.disease Esophageal squamous cell carcinoma Convolutional neural network Gross tumor volume Radiation therapy High morbidity 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Radiology business |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030493417 ISDA |
| DOI: | 10.1007/978-3-030-49342-4_60 |
| Popis: | Esophageal squamous cell carcinoma (ESCC) is one species cancer with high morbidity and mortality. Most cancer treatments need radiotherapy and chemotherapy. Before treatments, patients are often hesitant, thus delaying the time of treatment. If the effect of the treatment can be directly known according to the patient’s current condition, the patient can make decisions sensibly and enable to get timely treatment. In this paper, a prognosis method for esophageal squamous cell carcinoma is proposed. Three-dimensional convolutional neural networks (CNNs) is used to predict the effect of radiotherapy from computer tomography (CT) images of patients. The experiments confirm the predicted gross tumor volume are consistent with the actual values. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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