Synthetic PET From CT Improves Precision Diagnosis and Treatment of Lung Cancer: a Prospective, Observational, Multicenter Study

Synthetic PET From CT Improves Precision Diagnosis and Treatment of Lung Cancer: a Prospective, Observational, Multicenter Study

This study aims to synthesise PET data that preserves biological relevance and adds clinical value to the diagnosis and prognosis of lung cancer by establishing anatomical-to-metabolic mapping based on paired diagnostic CT and FDG-PET scans, thereby prospectively validating the clinical utility of the model.

Inclusion Criteria:

1. Patients with non-small cell lung cancer scheduled to undergo PET-CT and pathological examinations;
2. Voluntarily participate and sign an informed consent form;

Exclusion Criteria:

1. History of other malignant tumours;
2. Image artefacts;
3. Without pathological diagnostic information;
4. Without paired CT and FDG-PET scan images.