表1：宫颈癌患者盆腔放疗期间4级淋巴细胞减少预测的机器学习模型

数据集获取（Figshare）

• 源站标题：Table_1_A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.docx
• DOI：10.3389/fonc.2022.905222.s003
• 发布日期：2022-09-15
• 文件数：1 个
• 文件总体积：33.17 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_machine_learning_model_for_grade_4_lymphopenia_prediction_during_pelvic_radiotherapy_in_patients_with_cervical_cancer_docx/21114151

命令行下载

curl -L -o "Table_1_A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.docx" "https://ndownloader.figshare.com/files/37459762"

文件清单

• Table_1_A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.docx（33.17 KB）

文件总体积 33.17 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

Background/purposeSevere lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer.
MethodsThis retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir 9 cells/L during RT according to Common Terminology Criteria for Adverse Events (CTCAE) v4.03. Elastic-net logistic regression models were constructed for the prediction of G4 lymphopenia during pelvic RT using a repeated cross-validation methodology.
ResultsA total of 130 patients were eligible, and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariable analysis, G4 ALC nadir was associated with poor overall survival (OS) [hazard ratio (HR), 3.91; 95% confidence interval (CI), 1.34–11.38, p = 0.01]. Seven significant factors [Eastern Cooperative Oncology Group (ECOG) performance score, pre-RT hemoglobin, pre-RT lymphocytes, concurrent chemotherapy, gross tumor volume of regional lymphadenopathy (GTV_N volume), body volume, and maximum dose of planning target volume receiving at least 55 Gy (PTV_5500 Dmax)] were obtained by elastic-net logistic regression models and were included in the final prediction model for G4 ALC nadir. The model’s predicting ability in test set was area under the curve (AUC) = 0.77 and accuracy = 0.76. A nomogram of the final predicting model was constructed.
ConclusionsThis study developed and validated a comprehensive model integrating clinical and dosimetric parameters by machine learning method, which performed well in predicting G4 lymphopenia during pelvic RT for cervical cancer and will facilitate physicians to identify patients at high risk of G4 lymphopenia who might benefit from modified treatment app

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.905222.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_machine_learning_model_for_grade_4_lymphopenia_prediction_during_pelvic_radiotherapy_in_patients_with_cervical_cancer_docx/21114151）。