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<p>We introduce Longitudinal-CT, a publicly available resource of whole-body computed tomography (CT) studies with exhaustive expert manual annotations of tumor lesions across two timepoints. The dataset comprises 600 CT studies from 300 patients diagnosed with metastatic malignant melanoma, each including a baseline and a follow-up examination acquired during systemic therapy at the University Hospital Tübingen, Germany. In total, it contains 7,182 manually segmented tumor lesions - 4,079 at baseline and 3,103 at follow-up - each labeled with anatomical location, volume, and longitudinal correspondence to capture lesion evolution such as persistence, regression, merging, or new appearance. All CT data are provided in anonymized NIfTI format with corresponding segmentation masks and lesion metadata. Longitudinal-CT establishes a standardized foundation for developing and validating artificial intelligence methods for automated lesion detection, segmentation, and temporal tracking in oncology. As a reference, a baseline deep learning segmentation model trained using nnU-Net v2 demonstrates the dataset’s potential for advancing research in automated oncologic whole-body CT lesion segmentation.</p>

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A longitudinal whole-body CT dataset with manually annotated tumor lesions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月18日

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  • Sergios Gatidis,
  • Felix Peisen,
  • Andreas Wagner,
  • Pauline Ornela Megne Choudja,
  • Ahmed Othman,
  • Antoine Sanner,
  • Nils Grauhan,
  • Suam Kim,
  • Dirk Graafen,
  • Lukas Müller,
  • Tanja Loßau,
  • Jan Hendrik Moltz,
  • Temke Kohlbrandt,
  • Alessa Hering,
  • Christian La Fougère,
  • Konstantin Nikolaou,
  • Thomas Küstner

摘要

We introduce Longitudinal-CT, a publicly available resource of whole-body computed tomography (CT) studies with exhaustive expert manual annotations of tumor lesions across two timepoints. The dataset comprises 600 CT studies from 300 patients diagnosed with metastatic malignant melanoma, each including a baseline and a follow-up examination acquired during systemic therapy at the University Hospital Tübingen, Germany. In total, it contains 7,182 manually segmented tumor lesions - 4,079 at baseline and 3,103 at follow-up - each labeled with anatomical location, volume, and longitudinal correspondence to capture lesion evolution such as persistence, regression, merging, or new appearance. All CT data are provided in anonymized NIfTI format with corresponding segmentation masks and lesion metadata. Longitudinal-CT establishes a standardized foundation for developing and validating artificial intelligence methods for automated lesion detection, segmentation, and temporal tracking in oncology. As a reference, a baseline deep learning segmentation model trained using nnU-Net v2 demonstrates the dataset’s potential for advancing research in automated oncologic whole-body CT lesion segmentation.