Accuracy of Staging as a Predictor for Recurrence After Liver Transplantation for Hepatocellular Carcinoma
Bibliographic record
Abstract
We read with interest the paper from Shah et al. regarding “Accuracy of staging as a predictor for recurrence after liver transplantation for hepatocellular carcinoma” published in Transplantation (1). We at the Liver Transplant Unit in Newcastle-upon-Tyne, United Kingdom, have carried out a similar analysis of patients transplanted for hepatocellular carcinoma (HCC) and end-stage liver disease between January 2000 and December 2005 in our unit. During the above period, 15 patients with HCC underwent liver transplantation. Thirteen patients were within the Milan criteria on preoperative imaging. Two patients had tumors that were outside the Milan criteria but considered to be within the extended UCSF criteria. All patients had imaging repeated at three monthly intervals while on the transplant waiting list. Ten patients received Adriamycin based neoadjuvant therapy (transarterial chemoembolization in nine, systemic chemotherapy in one) while on the waiting list. All patients received a single dose of Adriamycin intraoperatively during the anhepatic phase and systemic chemotherapy posttransplantation. At a median follow-up of 57 months, three patients had developed tumor recurrence after 8, 18, and 32 months respectively. Detailed analysis of the explanted livers revealed that preoperative imaging had understaged tumors in eight patients. Six patients were understaged in terms of number of lesions (more than three), one patient in terms of both size and number of lesions, and one patient had previously unidentified major vascular invasion. The accuracy of cross-sectional imaging in our study was hence 46.7%. One patient with recurrence had been transplanted for a tumor that was within the Milan criteria both on imaging and explant examination. All three patients with recurrence had Edmonton grade 3/4 tumors with microvascular invasion. We agree with Shah et al. that current imaging technology has several limitations in staging HCC in the context of cirrhosis. Better markers of disease progression are needed to refine patient selection and growth rate of the largest nodule as described by the authors appears to be a step in this direction. There is, however, an urgent need for the identification of more reliable tumor markers (genomic- or proteomic-based) that are predictive of high pathological grade and vascular invasiveness because these factors have been shown to correspond with disease recurrence and poor prognosis. Until such markers are available, cross-sectional imaging will remain but an imperfect guide to staging HCC for transplantation. Mettu S. Reddy Derek M. Manas Regional Liver Unit Freeman Hospital Newcastle-upon-Tyne, United Kingdom
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".