Pathology Reporting of Neuroendocrine Tumors: Application of the Delphic Consensus Process to the Development of a Minimum Pathology Data Set
Bibliographic record
Abstract
Epithelial neuroendocrine tumors (NETs) have been the subject of much debate regarding their optimal classification. Although multiple systems of nomenclature, grading, and staging have been proposed, none has achieved universal acceptance. To help define the underlying common features of these classification systems and to identify the minimal pathology data that should be reported to ensure consistent clinical management and reproducibility of data from therapeutic trials, a multidisciplinary team of physicians interested in NETs was assembled. At a group meeting, the participants discussed a series of "yes" or "no" questions related to the pathology of NETs and the minimal data to be included in the reports. After discussion, anonymous votes were taken, using the Delphic principle that 80% agreement on a vote of either yes or no would define a consensus. Questions that failed to achieve a consensus were rephrased once or twice and discussed, and additional votes were taken. Of 108 questions, 91 were answerable either yes or no by more than 80% of the participants. There was agreement about the importance of proliferation rate for tumor grading, the landmarks to use for staging, the prognostic factors assessable by routine histology that should be reported, the potential for tumors to progress biologically with metastasis, and the current status of advanced immunohistochemical and molecular testing for treatment-related biomarkers. The lack of utility of a variety of immunohistochemical stains and pathologic findings was also agreed upon. A consensus could not be reached for the remaining 17 questions, which included both minor points related to extent of disease assessment and some major areas such as terminology, routine immunohistochemical staining for general neuroendocrine markers, use of Ki67 staining to assess proliferation, and the relationship of tumor grade to degree of differentiation. On the basis of the results of the Delphic voting, a minimum pathology data set was developed. Although there remains disagreement among experts about the specific classification system that should be used, there is agreement about the fundamental pathology data that should be reported. Examination of the areas of disagreement reveals significant opportunities for collaborative study to resolve unanswered questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.634 | 0.534 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".