A radiogenomic dataset of non-small cell lung cancer.
Shaimaa BakrOlivier GevaertSebastian EchegarayKelsey AyersMu ZhouMajid ShafiqHong ZhengJalen Anthony BensonWeiruo ZhangAnn N C LeungMichael KadochChuong D HoangJoseph ShragerAndrew QuonDaniel L RubinSylvia K PlevritisSandy NapelPublished in: Scientific data (2018)
Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
Keyphrases
- positron emission tomography
- computed tomography
- deep learning
- pet ct
- healthcare
- dual energy
- convolutional neural network
- gene expression
- big data
- image quality
- electronic health record
- contrast enhanced
- artificial intelligence
- magnetic resonance imaging
- small cell lung cancer
- optical coherence tomography
- papillary thyroid
- small molecule
- squamous cell carcinoma
- high throughput
- data analysis
- genome wide