Tumour burden predicts outcomes after curative resection of multifocal intrahepatic cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: Liver resection for multifocal intrahepatic cholangiocarcinoma (ICC) remains controversial due to a poor prognosis, driven by aggressive tumour biology. The aim of this study was to stratify multifocal ICC patients to identify those who are likely to benefit from resection. METHODS: Patients who underwent upfront curative-intent hepatectomy for ICC were identified from an international multi-institutional database. Among patients with multifocal tumours, overall survival (OS) was analysed using multivariable Cox regression to identify prognostic factors. Tumour burden score (TBS) was used for stratification of multifocal ICC, with the optimal cut-off determined via restricted cubic spline (RCS) analysis. RESULTS: Of 1502 patients, 208 (13.8%) had multifocal ICC. Among them, independent predictors of prognosis included TBS (HR 1.09), ASA grade >II (HR 1.48), cirrhosis (HR 2.05), periductal infiltrating/mass forming plus periductal infiltrating morphological subtype (HR 1.58), and receipt of adjuvant chemotherapy (HR 0.59). RCS analysis identified a TBS of 7.0 as the optimal cut-off. Notably, multifocal ICC patients with a low TBS (<7.0) demonstrated comparable 3-year OS to solitary ICC patients with AJCC stage II/III. In contrast, patients with a high TBS (≥7.0) and multifocal ICC exhibited the worst prognosis (3-year OS: stage I and solitary 67.1%, stage II/III and solitary 43.2%, low TBS and multifocal 43.4%, and high TBS and multifocal 17.8% (P < 0.001)). CONCLUSION: Whereas patients with high-TBS multifocal ICC had a poor prognosis, individuals with low-TBS multifocal ICC demonstrated survival outcomes comparable to solitary ICC patients. These findings emphasize the importance of stratifying patients by tumour burden to guide surgical decision-making and optimize treatment strategies for multifocal ICC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".