Efficacy and Feasibility of Proton Beam Therapy in Relapsed High-Risk Neuroblastoma-Experiences from the Prospective KiProReg Registry
Bibliographic record
Abstract
Background: Despite an intensive multimodal treatment approach, approximately 50% of high-risk (HR) neuroblastoma (NB) patients experience progression. Despite the advances in targeted therapy, high-dose chemotherapy, and other systemic treatment options, radiation therapy (RT) to sites of relapsed disease can be an option to reduce tumor burden and improve chance for disease control. Methods: Patients who received salvage irradiation with proton beam therapy (PBT) for local or metastatic relapse of HR NB within the prospective registry trials KiProReg and ProReg were eligible for this retrospective analysis. Data on patient characteristics, multimodality therapy, adverse events, and oncologic endpoints were evaluated. Adverse events were assessed before, during, and after PBT according to common terminology criteria for adverse events (CTCAE) V4.0. Results: Between September 2013 and September 2020, twenty (11 male; 9 female) consecutive patients experiencing local (N = 9) or distant recurrence (N = 25) were identified for this analysis. Distant recurrences included osteomedullary (N = 11) or CNS lesions (N = 14). Salvage therapy consisted of re-induction chemo- or chemo-immuno-therapy (N = 19), surgery (N = 6), high-dose chemotherapy and stem cell transplantation (N = 13), radiation (N = 20), and concurrent systemic therapy. Systemic therapy concurrent to RT was given to six patients and included temozolomide (N = 4), carboplatine (N = 1), or anaplastic lymphoma kinase tyrosine kinase inhibitors (ALK-TKI) (N = 1). A median dose of 36 Gy was applied to the 34 recurrent sites. Local RT was applied to 15 patients, while five patients, received craniospinal irradiation for CNS relapse. After a median follow-up (FU) of 20 months (4–66), the estimated rate for local control, distant metastatic free survival, and overall survival at 3 years was 68.0%, 37.9%, and 61.6%, respectively. During RT, ten patients (50%) presented with a higher-grade acute hematologic adverse event. Late higher-grade sequelae included transient myelitis with transverse section (N = 2) and secondary malignancy outside of the RT field (N = 1). Conclusion: Our study demonstrates the efficacy and safety of RT/PBT for recurrent HR NB in a multimodality second-line approach. To better define the role of RT for these patients, prospective studies would be desirable.
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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 | 0.007 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".