Hypofractionated Radiotherapy for Hematological Malignancies during COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
Purpose/Objective(s) Conventionally fractionated radiotherapy (RT) has shown to have excellent local control in hematological malignancies (HM). Up to date there is none or scant literature about the use of hypofractionated radiotherapy (HFRT) for the treatment of HM, this single institution study analyzed the efficacy of HFRT in HM. We hypothesized that HFRT in HM will result in a similar local tumor control that has been reported with standard fractionated RT. Materials/Methods In this retrospective study, we analyzed the data from patients within the provincial cancer registry diagnosed with HM treated with a curative intent using HFRT regimens suggested by International Lymphoma Radiation Oncology Group between 2020-2022 during the COVID-19 pandemic. Primary outcome of the study was overall response rate (ORR), measured as complete response (CR), partial response (PR) or stable disease (SD) within the irradiated field determined radiologically or clinically post completion of RT. Secondary end point was freedom from local progression (FFLP), calculated from the date of initiation of RT to the first date among in-field progression, death, and last follow-up. Summary statistics were used to describe cohort and treatment characteristics. FFLP was calculated by 1 minus cumulative incidence accounting for competing risk (i.e., death). Results Of the 36 patients included for analysis, 18 were aggressive non-Hodgkin lymphoma (NHL), 9 were indolent NHL, 6 were Hodgkin lymphoma (HL) and 3 were other HM. Among them 25 had consolidation RT and 11 had definitive RT. HFRT daily dose per fraction ranged from 2.67 Gy to 5 Gy and total dose regimens ranged from 18 Gy to 42.5 Gy in 6 - 17 fractions and median equivalent dose in 2 Gy fractions (EQD2) for alpha/beta = 10Gy was 36 Gy (±7.5). ORR for the entire cohort was 94.4%. With a median follow up of 13.2 months, FFLP at one year for the entire cohort was 91.5% and death without infield progression was 8.7%. Among the 4 patients who had in radiation field recurrence, 2 had aggressive NHL and 2 had HL. No grade 3 or 4 acute toxicities were reported. Conclusion This retrospective study using HFRT showed an ORR and FFLP comparable to historical studies using standard fractionation. Further long-term follow-up is warranted to confirm these findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".