Role of 18F-Fluorodeoxyglucose–Positron Emission Tomography/Computed Tomography Imaging in the Prediction of Prognosis in Patients With Indolent Lymphoma: Prospective Study
Bibliographic record
Abstract
BACKGROUND: The role of fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) in indolent lymphoma has been minimally studied. OBJECTIVE: This study aims to assess the value of FDG-PET/CT in predicting the prognosis of indolent lymphoma. METHODS: We prospectively recruited 42 patients with indolent lymphoma. A total of 2 patients were excluded, and 40 underwent baseline PET/CT and follow-up at various time points. A total of 9 patients were observed only, 7 received 4 doses of rituximab alone, and 24 received chemoimmunotherapy. Metabolic response on follow-up PET/CT was assessed using the maximum standardized uptake value (SUVmax) and Deauville criteria (DC). We aimed to obtain the best SUVmax and DC to predict optimal survival rates, risk stratification, and optimize therapeutic strategies. The mean follow-up from the initial diagnosis was 33.83 months. RESULTS: SUVmax <4.35 at interim PET/CT provided the best discrimination, with a progression-free survival (PFS) of 100% and a median survival time of 106.67 months compared with SUVmax ≥4.35 (P=.04), which had a PFS of 43.8% and a median survival time of 50.17 months. This cutoff was also valuable in predicting overall survival at baseline, that is, 100% overall survival with baseline SUVmax <4.35, versus 58.4% for SUVmax ≥4.35 (P=.13). The overall survival of patients with a baseline DC score <3.0 was 100%, with a median overall survival of 106.67 months. CONCLUSIONS: We demonstrated the utility of PET/CT in indolent lymphomas. SUVmax (<4.35 vs ≥4.35) on interim PET/CT performed best in predicting PFS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".