Modern Radiation Therapy for Extranodal Lymphomas: Field and Dose Guidelines From the International Lymphoma Radiation Oncology Group
Bibliographic record
Abstract
Extranodal lymphomas (ENLs) comprise about a third of all non-Hodgkin lymphomas (NHL). Radiation therapy (RT) is frequently used as either primary therapy (particularly for indolent ENL), consolidation after systemic therapy, salvage treatment, or palliation. The wide range of presentations of ENL, involving any organ in the body and the spectrum of histological sub-types, poses a challenge both for routine clinical care and for the conduct of prospective and retrospective studies. This has led to uncertainty and lack of consistency in RT approaches between centers and clinicians. Thus far there is a lack of guidelines for the use of RT in the management of ENL. This report presents an effort by the International Lymphoma Radiation Oncology Group (ILROG) to harmonize and standardize the principles of treatment of ENL, and to address the technical challenges of simulation, volume definition and treatment planning for the most frequently involved organs. Specifically, detailed recommendations for RT volumes are provided. We have applied the same modern principles of involved site radiation therapy as previously developed and published as guidelines for Hodgkin lymphoma and nodal NHL. We have adopted RT volume definitions based on the International Commission on Radiation Units and Measurements (ICRU), as has been widely adopted by the field of radiation oncology for solid tumors. Organ-specific recommendations take into account histological subtype, anatomy, the treatment intent, and other treatment modalities that may be have been used before RT.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".