Non-Hodgkin lymphoma is a heterogeneous group of cancers that triggers bone marrow infiltration in 20–40% of cases. Bone marrow biopsy in combination with a visual assessment of [¹⁸F]FDG PET/CT images is used to assess the marrow status. Despite the potential of both techniques, they still have limitations due to the subjectivity of visual assessment. The present study aims to develop models based on bone marrow uptake in [¹⁸F]FDG PET/CT images at the time of diagnosis to differentiate bone marrow status. For this purpose, a model trained for skeleton segmentation and based on the U-Net architecture is retrained for bone marrow segmentation from CT images. The mask obtained from this segmentation together with the [¹⁸F]FDG PET image is used to extract radiomics features with which 11 machine learning models for marrow status differentiation are trained. The segmentation model yields very satisfactory results with Jaccard and Dice index values of 0.933 and 0.964, respectively. As for the classification models, a maximum F1_score_weighted and F1_score_macro of 0.962 and 0.747, respectively, are achieved. This highlights the potential of these features for bone marrow assessment, laying the foundation for a new clinical decision support system.

Background/Objectives: This study aimed to determine whether interim PET/CT (iPET) scans could identify follicular lymphoma (FL) patients at high risk of relapse following first-line therapy. Methods: A total of 117 FL patients who underwent iPET scans were included, with responses interpreted using the Deauville score (DS). Progression-free survival (PFS) was evaluated over a median follow-up of 34 months. Results: Overall, 34% of patients were classified as iPET (+), with significantly worse estimated 5-year PFS compared to iPET (−) patients (34% vs. 76%, hazard ratio 4.3, p < 0.001). Multivariate analysis confirmed iPET (+) as an independent predictor of PFS. Conclusions: Interim PET results are significant predictors of PFS in FL first-line therapy and could inform response-adapted treatment strategies.

Background/Objectives: This study aimed to determine whether interim PET/CT (iP-ET) scans could identify follicular lymphoma (FL) patients at high risk of relapse fol-lowing first-line therapy. Additionally, the potential of cell-free DNA (cfDNA) analysis to complement iPET in predicting outcomes was explored. Methods: A total of 121 FL patients who underwent iPET scans were included, with responses interpreted using the Deauville score (DS). Progression-free survival (PFS) was evaluated over a median follow-up of 34 months. Interim cfDNA data were analyzed for 14 patients to assess its potential for detecting false-positive PET results. Results: Overall, 34% of patients were classified as iPET(+), with significantly worse estimated 5-year PFS compared to iPET(-) patients (29% vs. 72%, hazard ratio 4.31, p < 0.001). Multivariate analysis confirmed iPET(+) as an independent predictor of PFS. Rituximab maintenance was predictive of reduced progression within the iPET(+) group but not among iPET(-) patients. cfDNA analysis identified two false-positive iPET cases and showed potential to identify pa-tients with complete response at risk of early progression. Conclusions: Interim PET results are significant predictors of PFS in FL first-line therapy and could inform re-sponse-adapted treatment strategies. cfDNA analysis has the potential to complement PET/CT by improving specificity and identifying patients at risk of early progression, offering a more precise approach to managing FL.