Predictors of a true complete response among disappearing liver metastases from colorectal cancer after chemotherapy
Bibliographic record
Abstract
BACKGROUND: During chemotherapy, some colorectal liver metastases (LMs) disappear on serial imaging. This disappearance may represent a complete response (CR) or a reduction in the sensitivity of imaging during chemotherapy. The objective of the current study was to determine the fate of disappearing LMs (DLMs) and the factors that predict a true CR. METHODS: Between 2000 and 2003, 435 patients who were evaluated by hepatobiliary surgeons received chemotherapy before they were considered for resection. Inclusion criteria were <12 LMs before chemotherapy, at least 1 DLM on a computed tomography (CT) scan, and either surgical resection or 1 year of clinical follow-up after the disappearance of LMs. A true CR was defined as either a pathologic CR (no tumor detected in the resection specimen) or a durable clinical CR (did not reappear on follow-up imaging). Clinical and pathologic factors were analyzed to identify those associated with a true CR. RESULTS: During chemotherapy, 39 patients (9%) had a total of 118 DLMs on follow-up CT scans. Sixty-eight DLMs were resected, and 50 were followed clinically. Overall, 75 DLMs (64%) were true CRs, including 44 pathologic CRs and 31 durable clinical CRs. On multivariate analysis, the use of hepatic arterial infusion (HAI) chemotherapy (odds ratio [OR], 6.2; P = .02), the inability to observe the DLM on a magnetic resonance image (OR, 4.7; P = .005), and normalization of serum carcinoembryonic antigen levels (OR, 4.6; P = .006) were associated independently with a true CR. CONCLUSIONS: Approximately 66% of DLMs represented a true CR according to assessment by resection or radiologic follow-up. Predictive factors may help to stratify patients who are likely to harbor residual disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".