Data From NSCLC-Radiomics

This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications. In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. The dataset described here (Lung1) was used to build a prognostic radiomic signature. The Lung3 dataset used to investigate the association of radiomic imaging features with gene-expression profiles consisting of 89 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics-Genomics. For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School ([e-mail]). More Description

本数据集收录了422例非小细胞肺癌（non-small cell lung cancer, NSCLC）患者的相关资料。针对该队列患者，我们公开了其治疗前的计算机断层扫描（computed tomography, CT）影像、放射肿瘤医师手动勾画的大体肿瘤体积三维轮廓，以及临床结局数据。本数据集对应发表于《自然·通讯》的一项研究中的Lung1数据集。简言之，该研究针对1019例肺癌或头颈部癌症患者的CT数据，采用了放射组学研究方法。放射组学指通过提取大量定量影像特征，实现对肿瘤表型的全面量化表征。在本次分析中，我们共提取了440项可量化肿瘤影像强度、形态与纹理的特征。研究发现，大量放射组学特征在独立数据集内展现出预后价值，其中多数特征此前未被证实具有统计学显著性。放射基因组学分析显示，可捕捉肿瘤内部异质性的预后放射组学特征标记，与潜在的基因表达模式存在显著关联。上述结果表明，放射组学可识别出同时存在于肺癌与头颈部癌症中的通用预后表型。由于影像检查已成为临床常规诊疗手段，该发现具备潜在临床应用价值，可为低成本优化癌症治疗决策支持提供前所未有的机遇。本次介绍的Lung1数据集被用于构建预后放射组学特征标记。用于探究放射组学影像特征与基因表达谱关联的Lung3数据集，包含89例带有临床结局数据的NSCLC患者CT影像，可于NSCLC-Radiomics-Genomics获取。若需针对本数据集开展学术咨询，请联系达纳-法伯癌症研究所/哈佛医学院的Hugo Aerts博士（邮箱：[e-mail]）。更多描述

搜集汇总

背景与挑战

背景概述

该数据集包含422名非小细胞肺癌（NSCLC）患者的CT扫描图像、肿瘤3D体积勾画和临床结果数据，用于构建预后放射组学特征。它基于放射组学方法提取了440个图像特征，这些特征在预测预后方面显示出潜力，并与基因表达模式相关联，为癌症治疗决策提供低成本支持。数据集是Nature Communications研究中Lung1部分，旨在通过影像分析提升临床实践中的个性化治疗。