Non-Small Cell Lung Cancer CT Scan Dataset (NSCLC-Radiomics-Genomics)

This collection contains images from 89 non-small cell lung cancer (NSCLC) patients that were treated with surgery. For these patients pretreatment CT scans, gene expression, and clinical data are available. This dataset refers to the Lung3 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 da

本集合汇集了89例非小细胞肺癌（NSCLC）患者的手术图像。对于这些患者，提供了术前CT扫描、基因表达和临床数据。该数据集参照了发表在《自然通讯》杂志上的研究中的Lung3数据集。简言之，该研究通过运用放射组学方法对1,019例患有肺部或头颈部癌症患者的计算机断层扫描数据进行处理。放射组学涉及通过应用大量定量图像特征对肿瘤表型进行综合量化。在本项分析中，提取了440个特征，用以量化肿瘤图像的强度、形状和纹理。我们发现，大量放射组学特征在独立数据集中具有预后价值，其中许多特征在先前并未被认定为具有显著性。放射基因组学分析揭示了，一个能够捕捉肿瘤异质性的预后放射组学特征与潜在的基因表达模式相关。

搜集汇总

背景与挑战

背景概述

该数据集包含89名非小细胞肺癌（NSCLC）手术患者的预处理CT扫描、基因表达和临床数据，用于研究影像组学特征与基因表达谱之间的关联。作为《自然通讯》研究中Lung3数据集的一部分，它提取了440个量化肿瘤图像强度、形状和纹理的特征，旨在探索影像组学在癌症预后预测和临床决策支持中的应用潜力。