晚期喉癌化疗反应预测模型数据集

数据集获取（Figshare）

• 源站标题：Data Sheet 4_Integrating CT radiomics and transcriptomics: a biologically-informed machine learning model for predicting chemotherapy response in advanced laryngeal cancer.xlsx
• DOI：10.3389/fonc.2026.1740896.s004
• 发布日期：2026-04-15
• 文件数：1 个
• 文件总体积：13.86 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_4_Integrating_CT_radiomics_and_transcriptomics_a_biologically-informed_machine_learning_model_for_predicting_chemotherapy_response_in_advanced_laryngeal_cancer_xlsx/32017629

命令行下载

curl -L -o "Data Sheet 4_Integrating CT radiomics and transcriptomics: a biologically-informed machine learning model for predicting chemotherapy response in adva.xlsx" "https://ndownloader.figshare.com/files/63740220"

文件清单

• Data Sheet 4_Integrating CT radiomics and transcriptomics: a biologically-informed machine learning model for predicting chemotherapy response in adva.xlsx（13.86 KB）

文件总体积 13.86 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

BackgroundPredicting response to induction chemotherapy (IC) in advanced laryngeal cancer (LC) remains a clinical challenge. This study aimed to develop a non-invasive, interpretable model integrating CT radiomics and clinical features to predict chemotherapy outcomes.
MethodsWe retrospectively analyzed 161 advanced LC patients treated with IC. From pre-treatment CT images, 1,321 radiomics features were extracted, and a radiomics score (Rad-score) was constructed using LASSO regression. Transcriptomic analysis explored the biological basis of Rad-score. Independent predictors were identified via multivariate logistic regression and used to build five machine learning models. Model performance was evaluated using AUC, accuracy, and specificity. SHAP analysis was applied to interpret the optimal model.
ResultsFour robust radiomics features were selected to construct the Rad-score. The Rad-score demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.715 in the training set and 0.707 in the validation set. In multivariate analysis, the Rad-score (Odds Ratio [OR]=2.89, 95% CI: 1.29–6.48, P = 0.010), gap invasion and validation were identified as independent predictors of chemotherapy response. Among the machine learning models, the Random Forest model achieved the best performance, yielding an AUC of 0.914 in the training set, 0.856 in the validation set, and 0.810 in the external test set. Decision curve analysis confirmed the clinical utility of the model. SHAP analysis confirmed Rad-score and fat space invasion as core predictors, with synergistic effects.
ConclusionsWe developed a highly accurate and interpretable Random Forest model that integrates radiomics and clinical features to predict IC response in advanced LC. This tool enables precise risk stratification and personalized treatment decisions, sparing non-responders from ineffective therapy. Prospective studies are needed to validate its clinical utility.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2026.1740896.s004（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_4_Integrating_CT_radiomics_and_transcriptomics_a_biologically-informed_machine_learning_model_for_predicting_chemotherapy_response_in_advanced_laryngeal_cancer_xlsx/32017629）。