Cancer diagnostics using artificial intelligence

Clinical comparison and validation of openly available deep learning methods for automated metabolic tumor volume delineation on PET-CT of head and neck cancer

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN16907234

Enrollment

1200

Registered

2022-09-16

Start date

2021-03-21

Completion date

Unknown

Last updated

2025-03-24

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

Conditions

Head and neck cancer Cancer

Interventions

A single-centre study of automated tumor delineation accuracy with retrospectively registered head and neck cancer patients scanned with PET-CT for radiotherapy treatment planning between 01/01/2014 a

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients treated with radiotherapy for cancer of the head and neck who received a PET-CT scan for treatment planning purposes as a part of clinical routine

Exclusion criteria

Exclusion criteria: 1. Patient PET or CT image not acquired according to required protocol 2. Clinical metabolic tumor volume delineation incomplete

Design outcomes

Primary

Measure	Time frame
Tumor delineation accuracy measured using the dice coefficient at a single timepoint	—

Secondary

Measure	Time frame
1. Tumor delineation accuracy measured using Hausdorff distance at a single timepoint 2. Lesion-level detection accuracy measured using f1 score (harmonic mean of positive predictive value and sensitivity) at a single timepoint	—

Countries

Denmark

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 7, 2026