Search papers, labs, and topics across Lattice.
This study introduces MonteRET, a novel framework that enhances chest CT report generation by integrating global CT features with region-specific anatomical representations and a knowledge retrieval mechanism. By leveraging predicted medical conditions and vision-language alignment, MonteRET refines initial reports through a knowledge-guided rewriting agent, demonstrating significant improvements in report quality and clinical efficacy. Evaluated on large public and external cohorts, MonteRET notably increased recall, indicating a reduction in omitted findings compared to existing methods.
MonteRET significantly boosts chest CT report accuracy and completeness, reducing omitted findings while enhancing clinical relevance.
Automated chest CT report generation remains challenging because clinically faithful reporting requires both whole-volume understanding and accurate description of localized anatomical findings. Here we developed and retrospectively evaluated MonteRET, a region-aware retrieval-enhanced framework for generating chest CT findings sections. MonteRET integrates global CT features with region-level anatomical representations, retrieves clinically relevant knowledge using predicted medical conditions and region-level vision-language alignment, and refines initial reports through a knowledge-guided report rewriting agent. We trained our model on a public cohort with 24,128 CT scans from RadGenome-ChestCT. We evaluated MonteRET on the public RadGenome-ChestCT test set of 1,564 CT scans and an external cohort of 82 CT scans from NewYork-Presbyterian/Weill Cornell Medical Center. MonteRET improved report quality, semantic similarity, and clinical efficacy compared with a matched baseline and several state-of-the-art methods. Gains were most pronounced for recall, suggesting fewer omitted findings. Human expert evaluation by radiology residents also favored MonteRET.
