Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation.
Ahmed W MoawadAli MorshidAhmed M KhalafMohab M ElmohrJohn D HazleDavid FuentesMohamed BadawyAhmed O KasebManal HassanArmeen MahvashJanio SzklarukAliyya QayyumAbdelrahman AbusaifWilliam C BennettTracy S NolanBrittney CampKhaled M ElsayesPublished in: Scientific data (2023)
Hepatocellular carcinoma (HCC) is the most common primary liver neoplasm, and its incidence has doubled over the past two decades owing to increasing risk factors. Despite surveillance, most HCC cases are diagnosed at advanced stages and can only be treated using transarterial chemo-embolization (TACE) or systemic therapy. TACE failure may occur with incidence reaching up to 60% of cases, leaving patients with a financial and emotional burden. Radiomics has emerged as a new tool capable of predicting tumor response to TACE from pre-procedural computed tomography (CT) studies. This data report defines the HCC-TACE data collection of confirmed HCC patients who underwent TACE and have pre- and post-procedure CT imaging studies and available treatment outcomes (time-to-progression and overall survival). Clinically curated segmentation of pre-procedural CT studies was done for the purpose of algorithm training for prediction and automatic liver tumor segmentation.
Keyphrases
- computed tomography
- deep learning
- risk factors
- contrast enhanced
- dual energy
- image quality
- positron emission tomography
- convolutional neural network
- electronic health record
- high resolution
- big data
- newly diagnosed
- machine learning
- magnetic resonance imaging
- case control
- artificial intelligence
- ejection fraction
- healthcare
- magnetic resonance
- stem cells
- photodynamic therapy
- radiation therapy
- mesenchymal stem cells
- patient reported outcomes
- free survival
- radiofrequency ablation
- fluorescence imaging