A dual-validation 3D nnU-Net framework with harmonized preprocessing for robust DLBCL segamentation in PET/CT images
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is an aggressive and common subtype of non-Hodgkin lymphoma. The automatic segmentation of DLBCL tumors from positron emission tomography/computed tomography (PET/CT) images remains a significant challenge due to the complexity and variable appearance of tumors. In this study, we developed and evaluated a 3D nn-UNet model for the automatic segmentation of DLBCL lesions to support treatment planning and monitoring. The model was trained on 18F-FDG PET/CT scans from 217 patients. Performance was assessed using geometric metrics, resulting in a mean Dice Similarity Coefficient (DSC) of 0.85, Intersection over Union (IoU) of 0.75, sensitivity of 88.3 %, specificity of 95.7 %, and accuracy of 97.1 %. To establish clinical validity, the Total Metabolic Tumor Volume (TMTV) was derived from both ground truth and predicted segmentations. Bland-Altman analysis demonstrated strong agreement, and linear regression confirmed a high correlation between the volumes. The key novelty of our work lies in a harmonized preprocessing pipeline and a dual-validation strategy that integrates geometric metrics (DSC, IoU) with volumetric and metabolic assessments (TMTV, Standardized Uptake Value (SUVmax)). The results, supported by box plots illustrating metric distributions, confirm the model's robustness, reliability, and potential for clinical utility in managing DLBCL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".