Co-occurring IPMN and pancreatic cancer: the same or different? An overview from histology to molecular pathology
Bibliographic record
Abstract
Intraductal papillary mucinous neoplasm (IPMN) of the pancreas is one of the most well-established precursors of pancreatic cancer. Its progression to acquire invasiveness is a complex process, based on the accumulation of morphological and genetic alterations. Recent advances in DNA sequencing also showed that co-occurring IPMNs and pancreatic cancers could be totally independent, further complicating our understanding of this complex scenario. The distinction between IPMN and related pancreatic cancer vs IPMN and co-occurring-but not related-pancreatic cancer is a challenging task in routine diagnostic activity, but may have important implications for precision oncology. Of note, recent multiregional sequencing-based studies focused not only on IPMN multi-step tumourigenesis, but also on the divergent intratumoural heterogeneity of this neoplasm. Globally considered, there are three different situations in which co-occurring IPMNs and invasive carcinomas can be found in the same pancreata, indicated with different terminologies: (1) IPMN-associated carcinoma: this definition indicates a carcinoma arising from an IPMN and can be also defined as IPMN-derived carcinoma, sequential or likely related; (2) independent IPMN and invasive carcinoma: the two lesions are not related, and this situation is defined as concomitant, de novo or likely independent; (3) branch-off pathway, where an invasive carcinoma and an adjacent IPMN develop divergently in a forked fashion from a common ancestral clone. In this review, we aim at clarifying the most important nomenclature/definitions of these different situations, also providing an overview of the molecular state-of-the-art and of the clinical implications of this complex landscape.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".