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CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Riccardo LeviMaximiliano MolluraGiovanni SaviniFederico GaroliMassimiliano BattagliaAngela AmmirabileLuca A CappelliniSimona SuperbiMarco GrimaldiRiccardo BarbieriLetterio Salvatore Politi
Published in: Scientific data (2024)
Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.
Keyphrases
  • image quality
  • contrast enhanced
  • dual energy
  • computed tomography
  • magnetic resonance imaging
  • positron emission tomography
  • magnetic resonance
  • lymph node metastasis
  • deep learning
  • ultrasound guided
  • adverse drug