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Record W2787007196 · doi:10.1016/j.bbmt.2017.12.069

A Dose Escalation Study of Total Marrow Irradiation and Autologous Stem-Cell Transplantation for Relapsed Multiple Myeloma Patients

2018· article· en· W2787007196 on OpenAlexaff
Amarilis Figueiredo, Rajiv Samant, Arleigh McCurdy, Andrea Kew, Jason Tay, Lothar Huebsch, Christopher Bredeson, Mitchell Sabloff, Sultan Altouri, Natasha Kekre, Dawn Sheppard, D Fraser, Gosia Niedbala, Balázs Nyíri, Isabelle Bence‐Bruckler, David Allan, Grizel Anstee, Michael Hodgins, Carolyn Faught, Harold Atkins

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsUniversity of CalgaryUniversity of OttawaUniversity Health NetworkOttawa Hospital
Fundersnot available
KeywordsMedicineMucositisMultiple myelomaTotal body irradiationMelphalanToxicityStem cellBone marrowNuclear medicineHematopoietic stem cell transplantationRadiation therapyTransplantationSurgeryInternal medicineOncologyChemotherapyCyclophosphamide

Abstract

fetched live from OpenAlex

Background: Local irradiation results in long-term control and even cure of solitary plasmacytomas, but traditional total body irradiation (TBI) elicits excessive normal tissue toxicity limiting dose escalation for multiple myeloma patients. Intensity modulated radiation, a more targeted form of irradiation to the entire marrow (total marrow irradiation or TMI), delivers up to 70% less radiation to adjacent normal tissues compared to standard TBI approaches. We postulate that TMI will allow delivery of higher doses of radiation to the marrow which might improve outcome of patients with myeloma. Objectives: Primary outcomes are to determine safety and maximum tolerated dose (MTD) of TMI. A secondary outcome was time to next treatment (TTNT). Methods: This is an ongoing dose-escalation study of TMI as sole conditioning for salvage autologous hematopoietic stem-cell transplant (TMI-SCT) in radiation-naïve myeloma patients failing a first-line melphalan-aHSCT (Mel-SCT). Measurable myeloma and stored CD34+cells (≥2.5 × 106/kg) were study prerequisite. TMI doses were given twice daily starting with 14 Gy, then one fraction of 2 Gy was added to successive cohorts of three patients. Acute and long-term toxicity were graded using Bearman scale and LENT-SOMA respectively. Results: Fourteen patients were transplanted between January-2010 and May-2017. Three patients received 14 Gy, 16 Gy, 18 Gy and 20 Gy. One patient received 22 Gy and one patient scheduled for 22 Gy received 19.8 Gy because of early mucositis. Median age at TMI was 59.5 years and median time between Mel-SCT and TMI-SCT was 2.9 years. Increase in TMI dose was associated with mucositis worsening (P < .01), but not with narcotics usage, or fever. Xerostomia was the most common long-term toxicity observed, and it was graded LENT-SOMA ≥ 2 in 63.6%, 38.5% and 25% of the patients at D100, 6 months and 12 months post-TMI respectively. There was no difference in length of hospitalization, neutrophil engraftment, and incidence of acute toxicity between TMI-SCT and Mel-SCT. There was no difference in myeloma response after Mel-SCT compared to after TMI-SCT (Table 1).Table 1Response StatusCRVGPRPRSDPDTotalStatus after Mel-SCT554--14Status before TMI-SCT-164314Status after TMI-SCT3461-14Median TTNT was 1.6 year after second-line TMI-SCT and 2.0 years after first-line Mel-SCT (P = .21). Open table in a new tab Median TTNT was 1.6 year after second-line TMI-SCT and 2.0 years after first-line Mel-SCT (P = .21). Conclusion: When compared to a well-established conditioning, TMI was safe and disease control was encouraging. MTD was not reached. Further increase in TMI dose and longer follow-up are needed for better investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.301
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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