Fingerprint method applied to data from a phase III clinical trial

Abstract

Researchers in the RECOMIA network have been developing AI tools for the automated analysis of PET/CT studies in lymphoma patients. To enhance these AI tools, the CALGB 50303 dataset from The Cancer Imaging Archive was identified for inclusion in their project. Ensuring the quality of databases used for AI training is crucial, and one quality control (QC) measure involves the AI-based Fingerprint method to verify correct de-identification of clinical trial images. The study applied the Fingerprint method to PET/CT studies from 130 patients, successfully detecting an incorrectly de-identified study and identifying its correct trial identification number. This demonstrates the feasibility of using AI for QC in clinical trials. AI-based methods offer significant opportunities for enhancing QC, providing automated, consistent, and objective analyses that reduce the workload on human annotators. Integrating AI into QC processes promises to improve accuracy, consistency, and efficiency, thereby enhancing data integrity and the reliability of clinical trial results. This study underscores the importance of further developing AI-based QC methods in clinical trials.