International consensus guidelines for scoring the histopathological growth patterns of liver metastasis
Bibliographic record
Abstract
BACKGROUND: Liver metastases present with distinct histopathological growth patterns (HGPs), including the desmoplastic, pushing and replacement HGPs and two rarer HGPs. The HGPs are defined owing to the distinct interface between the cancer cells and the adjacent normal liver parenchyma that is present in each pattern and can be scored from standard haematoxylin-and-eosin-stained (H&E) tissue sections. The current study provides consensus guidelines for scoring these HGPs. METHODS: Guidelines for defining the HGPs were established by a large international team. To assess the validity of these guidelines, 12 independent observers scored a set of 159 liver metastases and interobserver variability was measured. In an independent cohort of 374 patients with colorectal liver metastases (CRCLM), the impact of HGPs on overall survival after hepatectomy was determined. RESULTS: Good-to-excellent correlations (intraclass correlation coefficient >0.5) with the gold standard were obtained for the assessment of the replacement HGP and desmoplastic HGP. Overall survival was significantly superior in the desmoplastic HGP subgroup compared with the replacement or pushing HGP subgroup (P=0.006). CONCLUSIONS: The current guidelines allow for reproducible determination of liver metastasis HGPs. As HGPs impact overall survival after surgery for CRCLM, they may serve as a novel biomarker for individualised therapies.
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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 | 0.054 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".