Revolutionizing Colorectal Cancer Imaging: AI-Driven Insights with RECOMIA for Enhanced PET Metrics and Precision Medicine

Purpose: Colorectal cancer (CRC) poses a significant global health challenge, with accurate staging and restaging being critical for effective treatment planning.Traditional reliance on standardized uptake values (SUV) from 18 F-FDG PET/CT imaging is limited by image noise, inter-patient variability, and segmentation inconsistencies.Advanced metrics like Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) provide a more comprehensive assessment but are hindered by the need for precise tumor segmentation, a challenge with conventional methods.Methods: This retrospective observational study analyzed 18 F-FDG PET/CT scans from 15 CRC patients at the Sultan Qaboos Comprehensive Cancer Care and Research Centre.Tumor metrics, including SUVmax, SUVmean, MTV, and TLG, were derived using artificial intelligence (AI) on the RECOMIA platform and compared to conventional SyngoVia software outputs.Bland-Altman analysis evaluated agreement, with statistical controls addressing potential biases.Results: AI-derived metrics showed significant differences in SUVmean, MTV, and TLG compared to SyngoVia (p-values: 0.0001, 0.0003, and 0.0312, respectively), demonstrating enhanced sensitivity and comprehensiveness.No significant differences were observed for SUVmax (p = 0.2058).AI-based analysis consistently produced higher metric values, indicating a more detailed evaluation of metabolic tumor activity.Conclusion: AI-powered platforms like RECOMIA show transformative potential in assessing metabolic tumor volumes in CRC, enhancing precision and reliability.These findings highlight the role of AI in improving clinical decision-making and patient outcomes.Broader validation is needed to facilitate routine integration into clinical workflows for CRC management.