MIB-1 Index as a Surrogate for Mitosis-Karyorrhexis Index in Neuroblastoma
Bibliographic record
Abstract
Neuroblastoma, the most common extracranial solid tumor in infancy, shows marked biological heterogeneity. Multiple prognostic markers are combined to risk-stratify neuroblastoma patients for treatment. One marker assesses histology, dividing patients into favorable and unfavorable categories based, in part, on the mitosis-karyorrhexis index (MKI). The recommended scoring of 5000 cells is, however, time-consuming and observer-dependent, and accurate counts may not always be performed. In the present study, we investigated using MIB-1 as a surrogate marker for the MKI. Twenty-five cases of neuroblastoma, ranging from low to high MKI, were immunostained for MIB-1. A total of 375 microscopic fields were digitally captured with > 100,000 cells scored. The MIB-1 index was determined by image analysis and MKI, by manual counting of the same immunostained fields. There was a significant correlation between the MIB-1 index and MKI comparing all fields (r = 0.7869, P < 0.01) and an even better correlation comparing individual cases (r = 0.9147, P < 0.01). Using a linear regression model, a formula was generated to calculate MKI from the MIB-1 index as follows: MKI = (MIB-1 index × 0.124) + 1.412. With this formula, a low MKI corresponds to an MIB-1 index < 4.74, intermediate MKI to an MIB-1 index of 4.74 to 20.87, and high MKI to an MIB-1 index > 20.87. For comparison, the calculations were repeated using a manual MIB-1 count on the same images. Similar significant correlations were obtained, with nearly identical cutoff values for MKI categories. This approach can facilitate determination of the MKI by assessing the MIB-1 index, either by image analysis or manual counting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".