The role of FDG-PET in defining prognosis of Hodgkin lymphoma for early-stage disease
Bibliographic record
Abstract
Given the excellent survival rates for early-stage Hodgkin lymphoma (HL), the young age of many patients, and concerns regarding acute and late treatment-related toxicities, there is a desire to have a predictive tool that enables therapy to be tailored toward the individual patient. Early (or interim) (18)F-fluorodeoxyglucose positron emission tomography with computerized tomography (FDG-PET/CT), as a test of tumor sensitivity to ongoing/planned therapy, has been shown to be prognostic for survival in HL. Based on results of interim FDG-PET/CT, therapy may be subsequently modified through minimization or via intensification for low- and high-risk patient populations, respectively (ie, response-adapted therapy). Important data have been generated to standardize the interpretability and reproducibility of interim FDG-PET/CT (eg, the Deauville 5-point system), and observational and noncontrolled prospective studies have produced evidence supporting the hypothesis that response-adapted therapy may potentially serve as a predictive tool. Furthermore, results from noninferiority phase 3 clinical trials randomizing early-stage HL patients with negative interim FDG-PET/CT to combined modality therapy versus chemotherapy alone have been reported. The current collective findings from these randomized early-stage HL studies have shown that acute relapse rates are lower with combined modality therapy, even in patients with negative interim FDG-PET/CT. Additional randomized response-adapted studies are ongoing and novel FDG-PET/CT applications involving quantitative techniques and innovative imaging modalities are being investigated to identify more robust imaging biomarkers. Treatment of early-stage HL remains a clinical management choice for physicians and patients to make with consideration of acute and long-term outcomes.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".