Three-dimensional microscopic image reconstruction of prostatic adenocarcinoma.
Bibliographic record
Abstract
CONTEXT: Routine microscopy provides only a 2-dimensional view of the complex 3-dimensional structure that makes up human tissue. Three-dimensional microscopic image reconstruction has not been described previously for prostate cancer. OBJECTIVES: To develop a simple method of computerized 3-dimensional image reconstruction and to demonstrate its applicability to the study of prostatic adenocarcinoma. METHODS: Serial sections were cut from archival paraffin-embedded prostate specimens, immunostained using antikeratin CAM5.2, and digitally imaged. Computer image-rendering software was used to produce 3-dimensional image reconstructions of prostate cancer of varying Gleason grades, normal prostate, and prostatic intraepithelial neoplasia. RESULTS: The rendering system proved easy to use and provided good-quality 3-dimensional images of most specimens. Normal prostate glands formed irregular fusiform structures branching off central tubular ducts. Prostatic intraepithelial neoplasia showed external contours similar to those of normal glands, but with a markedly complex internal arrangement of branching lumens. Gleason grade 3 carcinoma was found to consist of a complex array of interconnecting tubules rather than the apparently separate glands seen in 2 dimensions on routine light microscopy. Gleason grade 4 carcinoma demonstrated a characteristic form of glandular fusion that was readily visualized by optically sectioning and rotating the reconstructed images. CONCLUSIONS: Computerized 3-dimensional microscopic imaging holds great promise as an investigational tool. By revealing the structural relationships of the various Gleason grades of prostate cancer, this method could be used to refine diagnostic and grading criteria for this common tumor.
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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) | 0.003 | 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".