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
- risk factors
- deep learning
- contrast enhanced
- dual energy
- image quality
- convolutional neural network
- positron emission tomography
- electronic health record
- high resolution
- end stage renal disease
- big data
- machine learning
- magnetic resonance imaging
- newly diagnosed
- case control
- chronic kidney disease
- ejection fraction
- public health
- minimally invasive
- squamous cell carcinoma
- healthcare
- photodynamic therapy
- peritoneal dialysis
- mesenchymal stem cells
- patient reported outcomes
- combination therapy
- stem cells
- drug delivery
- data analysis
- virtual reality
- cancer therapy
- patient reported