AI-Driven Bone and Marrow Segmentation on FLT-PET/CT: Technical Multi-organ Validation in AML and HCT.

BACKGROUND

[18F] 3'-deoxy-3'-fluorothymidine positron emission tomography (FLT-PET) is valuable for detecting acute myeloid leukemia (AML) and monitoring stem cell engraftment after hematopoietic stem cell transplant (HCT) by assessing cellular proliferation in marrow-rich tissues. Reliable marrow quantification is difficult to achieve, and manual segmentation is impractical in clinical workflows. Most automated tools focus on solid tumors and lack clinical validation for skeletal FLT-PET/CT. This study evaluates deep learning whole-body segmentation and cortical-trabecular marrow quantification on FLT-PET/CT in AML with HCT.

RESULTS

Twenty refractory AML patients undergoing total marrow and lymphoid irradiation (TMLI) and transplantation were analyzed. From 134 predefined regions, five representative ROIs (spleen, liver, T6, L1, L3) validated agreement with manual segmentation. Automated and manual count measurements showed strong agreement, with a high correlation (r > 0.98, p < 0.0001). Consistent hotspot detection by both methods supports the AI tool's accuracy and clinical applicability. Small liver/spleen differences and larger positive vertebral trabecular biases were observed. AI cut processing time by ~ 95%, markedly improving efficiency.

CONCLUSION

This study provides a technical validation of an AI-driven multi-organ segmentation platform for FLT-PET/CT in AML and HCT, including separate cortical bone and trabecular marrow compartments. The automated approach demonstrated high agreement, excellent reproducibility, and substantial efficiency gains in skeletal marrow and organ quantification. These findings establish a scalable framework for future studies that will correlate FLT-based bone marrow metrics with clinical response and transplant outcomes.

TRIAL REGISTRATION: ClinicalTrials.gov NCT03422731. Registered 6 February 2018, https//www.cancer.gov/research/participate/clinicaltrialssearch/v?id=NCI201701778.