ESCCPro predicts surgical and postoperative adjuvant chemotherapy outcomes for patients with esophageal squamous cell carcinoma to improve clinical management.
main.py: Includes the entry function of ESCCPro and the code for training and validation.
input_dir_train: The path where your training dataset is stored.
input_dir_test: The path where your test dataset is stored.
HDF5_read.py: Defines the function of PET/CT image reading.
train_data: The input for ESCCPro. Examples of the input images are presented in Fig. S1 below.
target_data: The label for training and test.
keys[index]: The name of each image.
nets folder: The definition of the ESCCPro network, including the three pooling functions.
utils folder: Necessary functions used in the ESCCPro.
Fig. S1. Examples of the input PET/CT images of ESCCPro. R1, R2, and R3 represent the manual segmentation from three radiologists.
Cite this study:
PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma. Insights Into Imaging, 2024. DOI: 10.1186/s13244-024-01737-1