Creation of 3-Dimensional Prostate Cancer Maps: Methodology and Clinical and Research Implications
Bibliographic record
Abstract
CONTEXT: The creation of 3-dimensional prostate cancer maps could assist with surgical intervention, radiotherapy treatment planning and for correlative pathology-imaging research. OBJECTIVES: To develop methodology for creating detailed, 3-dimensional, prostate cancer maps (3DPCM) of tumor location, extra prostatic extension sites, and positive margins and to assess the adequacy of current clinical target volumes for postoperative radiotherapy to the prostate using 3DPCM coregistered with preoperative magnetic resonance imaging. DESIGN: Parallel slices of prostatectomy specimens were created with ProCUT, and 2-dimensional cancer maps were generated as line diagrams after microscopic examination of each slice. The 2-dimensional cancer maps were aligned and stacked to create a 3DPCM, which was coregistered with the preoperative magnetic resonance imaging scan. The map was exported to the radiotherapy planning system and was used to determine the areas at greater risk, which were then compared against the current Radiation Therapy Oncology Group guidelines for contouring postoperative clinical target volumes to assess the adequacy of coverage. RESULTS: Twenty-eight patients with a mean age of 66 years (range, 52-73) underwent radical prostatectomy and postoperative radiotherapy. Seventeen patients (61%) received adjuvant radiotherapy for pT3 disease and/or positive margins, and the rest underwent salvage radiotherapy. Thirty-nine percent (11 of 28) of the patients had Gleason scores of 8 or 9. The contours based on the Radiation Therapy Oncology Group guidelines for postoperative radiotherapy resulted in inadequate coverage of extraprostatic extensions in 79% (22 of 28) and positive margins in 64% (18 of 28) of the cases. CONCLUSIONS: We have developed a methodology for creation of 3DPCM. Modification of the radiotherapy contours, based on the 3DPCM coregistered with pretreatment magnetic resonance imaging, covers the areas at high risk of recurrence. The 3DPCM could become an important clinical and research tool for urologists, pathologists, radiologists, and oncologists.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".