Number of Nodules but not Size of Hepatocellular Carcinoma Can Predict Refractoriness to Transarterial Chemoembolization and Poor Prognosis
Bibliographic record
Abstract
BACKGROUND: To determine whether response to transarterial chemoembolization (TACE) predicts survival and to identify pretreatment factors associated with TACE response and prognosis. METHODS: Between April and September 2010, 50 patients underwent TACE for hepatocellular carcinoma. Response to TACE was assessed using post-treatment computed tomography (CT) and magnetic resonance imaging (MRI) scans and tumor marker levels and classified as Response Poor (P) and Non-poor (NP). Time zero was set to September 30, 2010, and survival rates were analyzed by landmarking. Cumulative survival rates were calculated using the Kaplan-Meier method and compared according to grades using the log-rank test; contributing factors to survival were analyzed using a Cox proportional hazards model. Pretreatment factors were analyzed for 109 TACE sessions performed until October 2017, using a multiple logistic regression model. Receiver operating characteristic (ROC) curves were generated to determine the best tumor number for predicting response P. RESULTS: Response P patients showed significantly lower cumulative survival rates than Response NP patients (P < 0.001). On multivariate analysis, tumor number (hazard ratio (HR), 1.475), protein-induced vitamin-K absence-II (HR, 4.539), and the number of previous TACE sessions (HR, 1.472) were identified as pretreatment factors contributing to Response P. Further, pre-treatment platelet count (HR, 0.876) and tumor number (HR, 1.330) were factors contributing to survival in multivariate analysis. ROC curve analysis revealed that the optimal cut-off value to discriminate Response P was 7.5. CONCLUSIONS: Response to TACE can predict survival. Pretreatment tumor number is a useful factor for predicting both TACE response and prognosis.
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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 | 0.003 |
| 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".