预测局部晚期直肠癌新辅助放化疗病理完全缓解的深度学习模型（Table 1）

数据集获取（Figshare）

• 源站标题：Table_1_Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.docx
• DOI：10.3389/fonc.2022.807264.s003
• 发布日期：2022-06-08
• 文件数：1 个
• 文件总体积：18.46 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Deep_Learning_Model_for_Predicting_the_Pathological_Complete_Response_to_Neoadjuvant_Chemoradiotherapy_of_Locally_Advanced_Rectal_Cancer_docx/20024519

命令行下载

curl -L -o "Table_1_Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.docx" "https://ndownloader.figshare.com/files/35741573"

文件清单

• Table_1_Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.docx（18.46 KB）

文件总体积 18.46 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

ObjectiveThis study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images.
BackgroundnCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty.
Methods842 LARC patients treated with standard nCRT from three medical centers were retrospectively recruited and subgrouped into the training, testing and external validation sets. Treatment response was classified as pCR and non-pCR based on the pathological diagnosis after surgery as the ground truth. The hematoxylin & eosin (H&E)-stained biopsy slides were manually annotated and used to develop a deep pathological complete response (DeepPCR) prediction model by deep learning.
ResultsThe proposed DeepPCR model achieved an AUC-ROC of 0.710 (95% CI: 0.595, 0.808) in the testing cohort. Similarly, in the external validation cohort, the DeepPCR model achieved an AUC-ROC of 0.723 (95% CI: 0.591, 0.844). The sensitivity and specificity of the DeepPCR model were 72.6% and 46.9% in the testing set and 72.5% and 62.7% in the external validation cohort, respectively. Multivariate logistic regression analysis showed that the DeepPCR model was an independent predictive factor of nCRT (P=0.008 and P=0.004 for the testing set and external validation set, respectively).
ConclusionsThe DeepPCR model showed high accuracy in predicting pCR and served as an independent predictive factor for pCR. The model can be used to assist in clinical treatment decision making before surgery.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.807264.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Deep_Learning_Model_for_Predicting_the_Pathological_Complete_Response_to_Neoadjuvant_Chemoradiotherapy_of_Locally_Advanced_Rectal_Cancer_docx/20024519）。