Effect of PET/CT on the Management and Outcomes of Participants with Hodgkin and Aggressive Non-Hodgkin Lymphoma: A Multicenter Registry
Bibliographic record
Abstract
Purpose To determine the relationship of PET/CT staging to the management and outcomes of participants with apparent limited-stage (LS) Hodgkin lymphoma (HL) or aggressive non-HL (ANHL) treated with curative intent. Materials and Methods This prospective multicenter registry included 850 participants (467 men and 383 women; median age, 54.1 years) from nine centers who had LS HL or ANHL on the basis of clinical data and CT, or with equivocal CT for advanced stage, who were considered for curative-intent first-line therapy. Participants were recruited between May 1, 2013, and December 31, 2015. Pre-PET/CT treatment plan was compared with treatment provided. Survival and second-line therapy initiation were compared with an historical control pool staged by using CT alone. Administrative data sources were used to control for baseline characteristics. Outcomes were assessed by using adjusted Cox proportional hazards regression and propensity score matching. Results PET/CT helped to upstage 150 of 850 participants (17.6%). There was a change in planned therapy in 224 of 580 (38.6%) of participants after PET/CT. There was a lower 1-year mortality for participants with ANHL in the PET/CT versus CT cohort (hazard ratio, 0.63; 95% confidence interval: 0.40, 1.0; P < .05) and for those with LS at PET/CT compared with those with LS at CT (hazard ratio, 0.40; 95% confidence interval: 0.21, 0.74; P = .004). For participants with HL, no 1-year outcome difference was found (P = .16). Conclusion PET/CT helped to upstage approximately 18% of participants and planned management was frequently altered. Participants with aggressive non-Hodgkin lymphoma whose first-line therapy was guided by PET/CT had significantly better survival compared with participants whose treatment was guided by CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Scott in this issue.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".