Successful Multimodal Treatment for Aggressive Metastatic and Recurrent Fibrolamellar Hepatocellular Carcinoma in a Child
Bibliographic record
Abstract
Fibrolamellar variant of hepatocellular carcinoma (FLHCC) does not have a favorable prognosis than conventional HCC, and there is no difference regarding the response to chemotherapy and the degree of surgical resectability. FLHCC commonly recurs after complete surgical resection, and there is a high rate of lymph node metastases. Herein, we report a 12-year-old girl with metastatic FLHCC with multiple recurrences aggressively treated with surgery, chemotherapy, and antiangiogenic agents. She is in complete remission after 4 years and 2 months after the diagnosis of metastatic FLHCC. The standard treatment of FLHCC is excision of the primary tumor and its metastases. Chemotherapy for FLHCC is controversial, and it has been suggested that cytoreductive chemotherapy was ineffective and adjuvant chemotherapy did not improve survival. Our patient with multiple recurrences was successfully treated with surgery, first-line chemotherapy with cisplatin and doxorubicin, second-line chemotherapy with 5-fluorouracil/interferon-α combination, and adjuvant antiangiogenic agents like cyclophosphamide and thalidomide. As FLHCC patients have no underlying liver disease, they can tolerate higher doses of chemotherapy compared with conventional HCC patients. We support the use of repeated aggressive surgery with adjuvant chemotherapy and antiangiogenic therapy, which provided complete remission in our patient with metastatic and recurrent FLHCC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".