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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".