Timing of End-of-Treatment PET Scans and Impact on Subsequent Testing in Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Introduction: First-line treatment of diffuse large B-cell lymphoma (DLBCL) consists of chemoimmunotherapy followed by end-of-treatment (EOT) positron-emission tomography (PET) scan to assess response. However, EOT PET has a high false-positive rate, thus, positive results must be followed by further testing to clarify response status, either by tissue biopsy or repeat PET. Excess testing can be invasive, expose to radiation, and increase costs. Guidelines suggest waiting minimum 3 weeks, preferably at least 6 weeks from last chemotherapy to perform PET, to avoid false-positives from post-treatment inflammation. However, this recommendation is primarily based on mouse models, and the optimal timing of EOT PET, and its potential impact on follow-up testing, has never been examined. We hypothesized that shorter time from last chemotherapy to EOT PET increases the risk of follow-up testing without impacting patient outcomes. Methods: We conducted a retrospective cohort study using population-level administrative health data of all patients in Ontario, Canada, ≥18 years old with DLBCL, who received frontline rituximab-based chemoimmunotherapy followed by PET within 16 weeks of last chemotherapy from October 2009-April 2023. Primary exposure of interest was time from last chemoimmunotherapy to EOT PET, with short time to PET defined as ≤6 weeks, and long time to PET >6 weeks. Sensitivity analysis modelling time from last chemotherapy to PET continuously as restricted cubic splines was performed. Primary outcome was follow-up testing, defined as either repeat PET or tissue biopsy within 6 months of EOT PET. Time to PET and follow-up testing was modelled with cause-specific hazard models, adjusting for age, sex, income, comorbidities, year of PET, number of treatment cycles, radiotherapy, and accounting for clustering by institution. Cancer-specific survival from last chemoimmunotherapy cycle was examined by univariate and multivariable cause-specific hazard models. Difference in median 30-day costs in the 12 months post-PET scan in Canadian dollars (CAD) was estimated by quantile regression. Results: A total of 5129 individuals with DLBCL had EOT PET scan following first-line treatment; 2982 (58%) had short time to PET (≤6 weeks), 2147 (42%) had long time to PET (>6 weeks). Patients with short time to PET were younger (mean 61.7 vs 63.7 years, standardized mean difference (SMD)=0.13), with less comorbidities (mean aggregated diagnosis groups 9.4 versus 9.8, SMD=0.13). Other characteristics were similar between groups. Overall, 1120 PETs and 1074 biopsies were performed in the 6 months following EOT PET. Patients with short time to PET had 717 repeat PETs (24 per 100 persons), 22% had ≥1 repeat PET, versus 403 PETs (18.8 per 100 persons), and 17% with ≥1 repeat PET in the long time to PET group. There were 643 biopsies (16.7% with ≥1 biopsy) versus 431 (16.2% with ≥1 biopsy), in short versus long time to PET. Short time to PET was associated with a higher risk of follow-up testing, with unadjusted hazard ratio (HR) of 1.15 (95% CI 1.03-1.29, p-value=0.014) and adjusted HR 1.13 (95% CI 1.01-1.26, p-value=0.033). Cumulative incidence of follow-up testing at 6 months was 32.4% (95% CI 30.8-34.1%) versus 28.7% (95% CI 26.8-30.6%) for short and long time to PET, respectively. Sensitivity analysis modelling time to PET continuously using restricted cubic splines showed similar results, with higher risk of follow-up testing for each week from last chemotherapy up until approximately 6 weeks, after which the risk was not significantly different from the 6-week mark. Sensitivity analysis using Fine-Gray model to account for competing risk of death showed similar results. There was no difference in cancer-specific death between short versus long time to PET in univariate (p-value=0.18) or multivariable analyses (p-value=0.85). Short time to PET was associated with $216.33 CAD (95% CI $91.03-$341.63, p-value<0.001) higher median 30-day costs compared to long time to PET. Median 30-day costs were $1549.36 versus $1332.41 CAD, respectively. Conclusions:Short time to PET from last chemotherapy increases the risk of follow-up testing compared to long time to PET, increases healthcare costs, despite no impact on cancer-specific survival. Specifically, 6 weeks or longer appears to be the optimal time to wait from the last chemotherapy to mitigate this risk.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".