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Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation.

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Radiomics can predict hepatocellular carcinoma (HCC) treatment response before transarterial chemo-embolization (TACE). This study details a dataset for developing radiomic models to improve HCC patient outcomes and reduce TACE failure.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Data Science

Background:

  • Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer with increasing incidence.
  • Most HCC cases are diagnosed at advanced stages, limiting treatment options to transarterial chemo-embolization (TACE) or systemic therapy.
  • TACE exhibits a high failure rate (up to 60%), imposing significant burdens on patients.

Purpose of the Study:

  • To establish a comprehensive dataset of Hepatocellular Carcinoma patients undergoing TACE.
  • To facilitate the development of radiomics-based algorithms for predicting TACE response.
  • To enable automatic liver tumor segmentation for improved treatment planning.

Main Methods:

  • Data collection from confirmed HCC patients with pre- and post-procedure CT imaging.
  • Inclusion of treatment outcomes such as time-to-progression and overall survival.
  • Clinically curated segmentation of pre-procedural CT scans for algorithm training.

Main Results:

  • The dataset comprises detailed imaging and clinical outcome data for HCC patients treated with TACE.
  • Radiomics analysis from pre-procedural CT scans shows potential for predicting TACE efficacy.
  • The curated data supports the training of predictive models for tumor response.

Conclusions:

  • Radiomics offers a promising non-invasive approach to predict HCC response to TACE.
  • This curated dataset is valuable for advancing AI-driven tools in liver cancer treatment.
  • Improved prediction of TACE outcomes can lead to better patient management and reduced healthcare costs.