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 ¹⁸F-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 ¹⁸F-FDG PET/CT scans from 15 CRC patients at the Sultan Qaboos Comprehensive Cancer Care and Research Centre. Tumor metrics, including SUV_max, SUV_mean, 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 SUV_mean, 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 SUV_max (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.