Automated quantification of TP53 using digital immunohistochemistry for acute myeloid leukemia prognosis
Bibliographic record
Abstract
Accurate quantification of TP53 is essential for guiding therapy in patients with acute myeloid leukemia (AML). While next-generation sequencing is considered the gold standard for TP53 detection, it is a costly test. As an alternative, immunohistochemistry (IHC) is utilized. However, the manual interpretation of IHC is a labor-intensive task and is very subjective with variation among pathologists. To address this issue, we propose a convolutional neural network-based approach to automatically analyze TP53 IHC images. This method involves detecting and segmenting tumor cells on digital IHC images, categorizing negative and positive cells, quantifying staining intensity (strong positive, moderate, and weak positive cells), and computing positivity and staining intensity indexes (PI and SII). The proposed method was developed and evaluated using 35 and 115 IHC images. The estimated metrics were compared against those of pathologists, showing a strong correlation of 0.9535 and 0.9093 for PI and SII, respectively. These results indicated a high level of agreement between artificial intelligence (AI) and pathologists. Furthermore, the AI method was significantly faster, taking approximately one-ninth of the time required for a hematopathologist. Additionally, the proposed method outperformed the state-of-the-art cell segmentation method for TP53 IHC quantification, with a statistically significant p-value of 0.001.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".