Case Report: Myeloid Sarcoma Development During Treatment for B Cell Lymphoblastic Lymphoma in a Boy with KRAS/NRAS Gene Mutations
Bibliographic record
Abstract
Here, we report a rare case of a 12-year-old boy who was initially diagnosed with B cell lymphoblastic lymphoma (BLBL) and developed myeloid sarcoma (MS) eight months after chemotherapy. Next-generation sequencing (NGS) showed mutations of KRAS and NRAS genes in both the bone marrow and lymph node. He presented an abnormal karyotype of 46, XY, -9, der (16) t (9; 16) (q13; q12), +mar. He received chemotherapy according to the South China Children's Leukemia Group 2016 protocol. Complete remission was achieved by the 15th day post-treatment. Eight months later and immediately prior to the start of maintenance therapy, the patient developed fever, skin nodules in both upper arms, and enlargement of bilateral testes. Pathological analysis of skin and testicular biopsies suggested the diagnosis of myeloid sarcoma (MS). Again, NGS examination showed mutations of KRAS and NRAS genes. The patient underwent haploidentical hematopoietic stem cell transplantation but unfortunately did not survive. The interval of eight-month interval between the initial disease onset and MS brings into question whether MS developed as part of the initial onset of disease or as a secondary tumor in association with chemotherapy. Thus, understanding the pathogenesis of MS involving abnormalities of lymphoid progenitors may assist in the prediction of prognosis and development of novel target therapies.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".