Histologic features and genomic alterations of primary colorectal adenocarcinoma predict growth patterns of liver metastasis
Bibliographic record
Abstract
BACKGROUND: Different histological growth patterns (HGPs) of colorectal carcinoma (CRC) liver metastasis are associated with patients' prognosis and response to antiangiogenic therapy. However, the relationship between HGPs of liver metastasis and clinicopathological and genomic characteristics of primary cancer has not been well established. AIM: To assess whether certain clinicopathological and genomic features of primary CRC could predict the HGPs of liver metastasis. METHODS: A total of 29 patients with paired resections of both primary CRC and liver metastasis were divided into two groups: A (15 cases with desmoplastic liver metastasis) and B (14 cases with replacement liver metastasis). Clinical information was obtained from patients' charts. Mismatch repair proteins, BRAFV600E, and PD-L1 were evaluated by immunohistochemistry. Five cases were selected randomly from each group for whole exome sequencing (WES) analysis. RESULTS: 0.05). On WES analysis, primary carcinoma with desmoplastic liver metastasis showed mutations in APC (4/5); TP53 (3/5); KRAS, PIK3CA, and FAT4 (2/5); BRCA-1, BRCA2, BRAF, and DNAH5 (1/5), whereas primary carcinoma with replacement liver metastasis showed mutations in APC and TP53 (3/5); KRAS, FAT4, DNH5, SMAD, ERBB2, ERBB3, LRP1, and SDK1 (1/5). CONCLUSION: The HGPs, TBS, and CDR of primary CRC as well as the presence of specific genetic mutations such as those in PIK3CA could be used to predict the HGPs of liver metastasis, response to therapy, and patients' prognosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".