Infarct-like Necrosis
Bibliographic record
Abstract
The response of colorectal adenocarcinoma liver metastases to perioperative chemotherapy can be assessed histologically in partial hepatectomy specimens. Necrosis in this scenario may represent a lack of treatment effect or a therapeutic response to chemotherapy. This study sought to validate the histologic classification of necrosis into 2 types: usual necrosis (UN) representing an absence of treatment effect, and infarct-like necrosis (ILN) representing a therapeutic response to chemotherapy. Tumor regression grade (TRG) is a previously described prognosticating method that estimates tumor replacement by fibrosis. We incorporated ILN into a modified TRG (mTRG) and compared its performance as a prognostic factor against TRG. A retrospective clinical and histologic review was undertaken of all partial hepatectomies performed for colorectal liver metastases at our center between 2004 and 2010. Clinicopathologic features were compared between the 2 types of necrosis, including survival stratified by TRG and mTRG. A total of 109 cases were reviewed, with 46 patients receiving perioperative chemotherapy. ILN was identified in 12 cases, and all of these cases were associated with perioperative chemotherapy. ILN was significantly associated with perioperative treatment with bevacizumab. In patients receiving perioperative chemotherapy, those with ILN had superior disease-free survival compared with those with UN (P=0.047). mTRG1 to 2 scores were associated with significantly better survival compared with mTRG3 to 5 scores. In contrast, use of TRG did not demonstrate a significant difference in disease-free and overall survival. ILN represents a form of treatment effect and should be distinguished from UN. A modified grading system that incorporates ILN may enhance the prognostic utility of TRG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".