Abstract
INTRODUCTION: Liver tumours are a leading cause of global morbidity and mortality. Current diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI) and intraoperative ultrasound (IOUS), have limitations in detecting liver neoplasms. Indocyanine green (ICG) has emerged as a promising tool for improving liver tumour detection. This study aims to assess the impact of preoperative ICG on intraoperative tumour detection in minimally invasive surgery and develop a machine-learning algorithm to enhance tumour detection using ICG fluorescence. METHODS AND ANALYSIS: This prospective, multicentre, phase IV clinical trial adheres to Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. Patients with liver tumours eligible for minimally invasive surgery and a preoperative imaging test will be included. ICG will be administered intravenously 24 hours before surgery. Intraoperative procedures will include IOUS, ICG mapping and photographic documentation. Patients will be followed for 90 days to assess tumour progression, morbidity and mortality. The photographic analysis will enable the development of an artificial intelligence algorithm using machine learning and neural networks to identify lesions based on ICG fluorescence. The estimated sample size is 173 patients and the trial is predicted to accrue in 3 years. ETHICS AND DISSEMINATION: The trial will be conducted in accordance with the Declaration of Helsinki and the Spanish Agency of Medicines and Medical Devices (AEMPS) guidelines. Approved by the local institutional Ethics Committee and the AEMPS, the results will be shared with the scientific community through publications and conferences. EUDRACT NUMBER: 2023-5 08 316-27-00. TRIAL REGISTRATION NUMBER: NCT06398028. PROTOCOL VERSION: V.12, 18 March 2025.