Implication of Unfavorable Histology, MYCN Amplification and Diploidy for Stage I and II Neuroblastomas
Bibliographic record
Abstract
BACKGROUND: Surgery is the first line treatment for low-grade neuroblastomas. In stage I tumors, the presence of MYCN amplification is rarely detected and the Shimada histology is not always taken into consideration when deciding on the treatment. This study concerns the significance of these two factors in the evolution of children with low-grade neuroblastomas. METHODS: We analyzed the assessment and follow-up of children with low-grade neuroblastomas (stages I and II) with or without MYCN amplification, with either a favorable or unfavorable histology and with or without tumor cell diploidy. Favorable histology was defined as stroma-poor tumors with more than 5 % differentiating neuroblasts and a mitosis karyorrhexis index (MKI) of less than 100/5000 cells. RESULTS: From 1995 to 2006, out of 114 neuroblastomas, nine (7.9 %) were stage I and 21 (18.4 %) stage II. Of these 30 patients, 27 underwent surgery alone and three received chemotherapy after surgery. The combination of MYCN amplification, unfavorable histology and diploidy was noted in one patient who developed metastases within two months. MYCN amplification alone was noted in two cases who are still tumor-free after two years. Unfavorable histology alone was noted in four patients, of whom one suffered a recurrence of the tumor (previously stage I) and three are tumor-free after six years. Tumor cell diploidy alone was present in 11 patients whose evolution is satisfactory. CONCLUSION: Because MYCN amplification and unfavorable histology are rare in early stage neuroblastomas, these tumors may be misclassified if they are not investigated further. It seems that no single clinical or biological feature can be considered a significant factor in establishing a prognosis or determining whether additional treatment is required.
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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 | 0.001 | 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) | 0.001 | 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".