表9_免疫治疗脑转移瘤患者囊性肿瘤进展MRI特征及大语言模型诊断评估（病例报告）
该数据集包含脑转移瘤免疫治疗后囊性肿瘤进展的MRI特征及大语言模型诊断评估，用于鉴别假性进展。
基本信息
资源简介
表9_免疫治疗脑转移瘤患者囊性肿瘤进展MRI特征及大语言模型诊断评估（病例报告）
【一句话概览】
本数据集是 figshare 公开的文本数据集，收录 1 个文件、合计 14.58 KB，主题涵盖 brain metastasis、tumor progression、radiation necrosis、immunotherapy、large language model，可用于医学自然语言处理（实体识别、信息抽取等），CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table 9_MRI features and preliminary diagnostic assessment using large language models of cystic tumor progression mimicking radiation necrosis in brain metastasis patients treated with immunotherapy: case report.docx》。配套论文《MR perfusion characteristics of pseudoprogression in brain tumors treated with immunotherapy – a comparative study with chemo-radiation induced pseudoprogression and radiation necrosis》发表于 Journal of Neuro-Oncology（2024），DOI：10.1007/s11060-024-04910-0。源站首次发布：2025-12-10。
【数据内容】
源站原始描述：The application of immunotherapy in patients with brain metastasis (BM) has increased rapidly in recent years, which led to a diagnostic dilemma between pseudoprogression and true progression in the post-treatment evaluation. Here, we reported MRI characteristics of pathology-confirmed large cystic rim-enhancing lesions of tumor progression in two BM cases with immunotherapy treatment, which mimicked radiation necrosis on conventional MRI. In addition, our preliminary findings showed that the cu…
中文译文（机器翻译，仅供快速理解，请以原文为准）：近年来，免疫治疗在脑转移（BM）患者中的应用迅速增加，这导致治疗后评估中假性进展与真性进展之间的诊断困境。在此，我们报告了两例接受免疫治疗的BM患者中经病理证实的肿瘤进展所致大型囊性环形强化病灶的MRI特征，这些病灶在常规MRI上酷似放射性坏死。此外，我们的初步发现显示，使用ChatGPT和DeepSeek的定制大型语言模型（LLMs）在这些病例中得出了正确诊断，这可能提示LLMs在该领域决策中的潜在应用价值。
【数据结构与技术规格】
文件数 1 个，合计 14.58 KB。Table 9_MRI features and preliminary diagnostic assessment using large language models of cystic tumor progression mimicking radiation necrosis in bra.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，14.58 KB。数据集 DOI：10.3389/fimmu.2025.1661918.s004。
【主题与分类】
源站学科分类：Genetic Immunology。主题标签：brain metastasis、tumor progression、radiation necrosis、immunotherapy、large language model、arterial spin labeling (ASL)。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Table_9_MRI_features_and_preliminary_diagnostic_assessment_using_large_language_models_of_cystic_tumor_progression_mimicking_radiation_necrosis_in_brain_metastasis_patients_treated_with_immunotherapy_case_report_docx/30845996
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tan, Guirong; Cai, Mingchen; Lu, Yu; Liu, Amy; Li, Shimin; Xiao, Gang; et al. (2025). Table 9_MRI features and preliminary diagnostic assessment using large language models of cystic tumor progression mimicking radiation necrosis in brain metastasis patients treated with immunotherapy: case report.d…
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
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公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Table 9_MRI features and preliminary diagnostic assessment using large language models of cystic tumor progression mimicking radiation necrosis in brain metastasis patients treated with immunotherapy: case report.docx
- DOI：10.3389/fimmu.2025.1661918.s004
- 发布日期：2025-12-10
- 文件数：1 个
- 文件总体积：14.58 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_9_MRI_features_and_preliminary_diagnostic_assessment_using_large_language_models_of_cystic_tumor_progression_mimicking_radiation_necrosis_in_brain_metastasis_patients_treated_with_immunotherapy_case_report_docx/30845996
命令行下载
curl -L -o "Table 9_MRI features and preliminary diagnostic assessment using large language models of cystic tumor progression mimicking radiation necrosis in bra.docx" "https://ndownloader.figshare.com/files/60252137"
文件清单
Table 9_MRI features and preliminary diagnostic assessment using large language models of cystic tumor progression mimicking radiation necrosis in bra.docx（14.58 KB）
文件总体积 14.58 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
The application of immunotherapy in patients with brain metastasis (BM) has increased rapidly in recent years, which led to a diagnostic dilemma between pseudoprogression and true progression in the post-treatment evaluation. Here, we reported MRI characteristics of pathology-confirmed large cystic rim-enhancing lesions of tumor progression in two BM cases with immunotherapy treatment, which mimicked radiation necrosis on conventional MRI. In addition, our preliminary findings showed that the custom large language models (LLMs) using ChatGPT and DeepSeek yielded correct diagnoses in these cases, which may suggest the potential utility of LLMs for the decision-making in this field.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fimmu.2025.1661918.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_9_MRI_features_and_preliminary_diagnostic_assessment_using_large_language_models_of_cystic_tumor_progression_mimicking_radiation_necrosis_in_brain_metastasis_patients_treated_with_immunotherapy_case_report_docx/30845996）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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