Malignant Colorectal Polyps; Pathological Consideration (A review)
Bibliographic record
Abstract
BACKGROUND: Routine screening colonoscopy is on the rise and pathologists have to deal with the ever larger numbers of excised colonic polyps. It is very important to optimize the patients' individual treatment and further surveillance. Pathologists play a critical role in management, as most of the clinical decisions concerning colonic polyp management are based on pathologic findings. One of the most important clinical issues in colonic adenomas is the diagnosis of malignancy and reporting its different aspects by the pathologist. The histologic type and the extent of carcinoma within a malignant polyp have considerable impact on the decisions of gastroenterologists and surgeons for further management. Therefore, the most recent literature regarding the diagnosis and reporting of the different features of malignant polyps was reviewed. DATA ACQUISITION: There is growing literature regarding the different pathologic features and reporting of malignant colonic polyps, and in this review, published articles that are listed on Google Scholar and Pub Med are discussed. CONCLUSION: Diagnosis of malignant colon polyp requires the presence of tumor cells that are penetrating beyond the muscular mucosa into submucosa (pT1). As well as establishing a diagnosis of malignant polyp, it is very important to report the size of the invasive component, the presence or absence of lymphovascular invasion, the degree of tumor differentiation and the distance of the carcinoma from the line of resection. Other important features that may be reported include: the presence or absence of tumor budding, the depth of tumor cell penetration into the submucosa, and results of immunohistochemistry for mismatch repair proteins and BRAF.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".