Toward quantitative digital histopathology for prostate cancer: comparison of inter-slide interpolation methods for tumour measurement
Bibliographic record
Abstract
Accurate pathology assessment of post-prostatectomy specimens is important to determine the need for and to guide potentially life-saving adjuvant therapy. Digital pathology imaging is enabling a transition to a more objective quantification of some surgical pathology assessments, such as tumour volume, that are currently visually estimated by pathologists and subject to inter-observer variability. One challenge for tumour volume quantification is the traditional 3–5 mm spacing of images acquired from sections of radical prostatectomy specimens. Tumour volume estimates may benefit from a well-motivated approach to inter-slide tumour boundary interpolation. We implemented and tested a level set-based interpolation method and found that it produced 3D tumour surfaces that may be more biologically plausible than those produced via a simpler nearest-slide interpolation. We found that the simpler method produced larger tumour volumes, compared to the level set method, by a median factor of 2.3. For contexts where only tumour volume is of interest, we determined that the volumes produced via the simpler method can be linearly adjusted to the level setproduced volumes. The smoother surfaces from level set interpolation yielded measurable differences in tumour boundary location; this may be important in several clinical/research contexts (e.g. pathology-based imaging validation for focal therapy planning).
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".