Automated analysis of whole body FDG PET/CT Data using Machine Learning methods

Automated analysis of whole body FDG PET/CT Data using Machine Learning methods - AutoPET

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00026991

Enrollment

2000

Registered

2022-03-10

Start date

2022-03-17

Completion date

Unknown

Last updated

2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

C34 C81 C83 C43

Interventions

Group 1: Patients with a diagnosis of histologically confirmed oncologic disease who have received whole-body PET/CT (neck, thorax, abdomen, pelvis) and have at least one measurable PET-positive lesio

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: • Histologically confirmed malignancy (bronchial carcinoma, melanoma, lymphoma) • Baseline (therapy-naïve) whole-body 18F -FDG PET/CT with at least one PET-positive lesion • Control group: Patients without oncological disease who have received a whole-body 18F -FDG PET/CT due to another indication and do not show pathological tracer accumulation.

Exclusion criteria

Exclusion criteria: - Patients who have already received therapy prior to imaging - Missing or incomplete image records in PACS

Design outcomes

Primary

Measure	Time frame
Automation of lesion detection and segmentation on whole-body PET/CT image data.	—

Secondary

Measure	Time frame
Increase availability of anonymized clinical image datasets to researchers and support advancements in AI-based automated image analysis through public accessibility of datasets.	—

Countries

Germany

Contacts

Public ContactMathias Fabritius

Klinikum der Universität München

mathias.fabritius@med.uni-muenchen.de+49 89 4400-73620

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026