Multiple Primary Malignancies in Patients With Hepatocellular Carcinoma
Bibliographic record
Abstract
Multiple primary malignancies (MPMs) are defined as 2 or more malignancies without subordinate relationship detected in different organs of an individual patient. Reports addressing MPM patients with hepatocellular carcinoma (HCC) are rare. We perform a 26-year follow-up study to investigate characteristics and prognosis of MPM patients associated with HCC due to the scarcity of relative researches.We retrospectively analyzed records of 40 patients who were diagnosed with MPM including HCC at the Departments of Surgery at Peking Union Medical College Hospital during 1989 to 2010. Their clinical characteristics and postoperative survival were compared with those of 448 patients who had HCC only during the study period.Among the 40 MPM patients, 11 were diagnosed synchronously and 29 metachronously. The most common extra-hepatic malignancies were lung cancer (15%), colorectal (12.5%), and thyroid carcinoma (12.5%). MPM patients had a negative hepatitis B virus infection rate (P = 0.013) and lower median alfa-fetoprotein (AFP) level (P = 0.001). Post-operative 1-, 3-, and 5-year overall survival (OS) rates for MPM patients were 82.5%, 64.5%, and 38.6% respectively, and showed no significant difference with those of HCC-only patients (84.7%, 54.2%, and 38.3% P = 0.726). During follow-up, 24 MPM patients died, including 17 (70.8%) who died of HCC-related causes. In univariate analysis, synchronous diagnosis, higher gamma glutamyltransferase (GGT) and/or AFP levels, tumor >5 cm and vascular invasion were significantly associated with shorter OS, but only tumor size was an independent OS factor in Cox modeling analysis.HCC should be considered as a potential second primary for all cancer survivors. Most MPM patients died of HCC-related causes and showed no significant difference in OS compared with HCC-only patients. Tumor size of HCC, rather than MPMs itself, was the only independent OS predictor for the MPM patients.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".