Unexpectedly high variability in determining tumour extent in prostatic biopsies: implications for active surveillance
Bibliographic record
Abstract
AIMS: Tumour content in prostatic biopsies is an important indicator of prostate cancer volume and patient prognosis. Consequently, guidelines typically recommend reporting it as a percentage or linear length (mm). This study aimed to determine the current practices for reporting tumour content in prostatic biopsies and evaluated the consistency among pathologists in diagnosing 10 standard biopsy cases of prostate cancer to assess interobserver variability. METHODS AND RESULTS: A web-based survey gathered data on demographics, experience and attitudes regarding the reporting of prostate cancer and its extent in biopsies. Virtual microscopy allowed analysis of 10 biopsy cases, each consisting of a single slide of prostate cancer. Self-reports from 304 participants recruited via the International Society of Urological Pathology and the German Society of Pathology were analysed. Most participants (43.4%) reported tumour extent as percentage of the biopsy core, 37.6% reported percentages and mm and 18.3% reported mm exclusively. The methods used to determine percentages showed an unexpected spread of choices, leading to considerable variability in results. Additionally, 40.8% of participants took part in the practical segment of the survey. The reported measures of tumour extent confirmed a notable interobserver variability, which was significantly higher for reported percentages. CONCLUSION: A high rate of interobserver variability in reporting tumour content in prostatic biopsies was found. This matter is especially critical for patients who are candidates for active surveillance. Reporting absolute measures of tumour content has the advantage of lower variability in comparison to percentages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".