In vitro three dimensional culture of hepatocellular carcinoma to measure prognosis and responsiveness to chemotherapeutic agents
Bibliographic record
Abstract
BACKGROUND: Understanding the prognosis of hepatocellular carcinoma (HCC) informs plans for care. Tumor morphology and molecular markers have been correlated with outcomes. Three-dimensional tissue culture (3DTC) allows for direct in vitro measurement of a tumor's ability to grow and metastasize. The impact of chemotherapeutic agents, alone or in combinations, may also be measured. METHODS: All patients with a presumed diagnosis of HCC were eligible for this study including those undergoing resection, chemoembolization and transplantation. Concomitant diseases and outcomes were recorded. One mm(3) HCC specimens were grown in multiwell plates containing gel media, without and with chemotherapeutic agents. RESULTS: Tumors were sampled from 17 patients. Only 13 had HCC, all of whom had liver transplantation. Of the confirmed HCC patients, 6 (46%) are alive and disease free 82 months following transplantation, 1 (7%) is alive with recurrence of disease and 6 (46%) died, with a mean survival of 12 months post liver transplant. Ten of thirteen 3DTC samples grew, having an average migration distance of 108.3µm in the first 24 hours. Two of three patients who had prior chemoembolization had successful 3DTC. Migration distances (µm) were 188.8±104.3, 104.5±111.7 and 39.6±32.4 for tumors categorized as high, intermediate and low grade, respectively. Tumor migration was inhibited by irinotecan, paclitaxel and docetaxel (-68%±7%, -61%±19% and -60%±21%, respectively) whereas the effect was variable with 5 fluorouracil (5FU) and doxorubicin (-12%±51% and 9%±76%, respectively). CONCLUSIONS: It is feasible to grow tissue from HCC in 3DTC to study the tumor's capacity to grow and migrate and its responsiveness to commonly used chemotherapeutic protocols.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".