Oncological Resectability Criteria for Intrahepatic Cholangiocarcinoma: A Preoperative Framework for Multidisciplinary Management
Bibliographic record
Abstract
INTRODUCTION: Recent advancements in systemic chemotherapy have fueled debates regarding the feasibility of combining systemic therapy with surgery for advanced intrahepatic cholangiocarcinoma (ICC). However, the absence of consensus on oncological resectability criteria has hindered discussions on optimal multidisciplinary management. This study sought to propose preoperative oncological resectability criteria for ICC. METHODS: Patients undergoing upfront curative-intent hepatectomy for ICC were identified from an international multi-institutional database. Independent tumor-related prognostic factors for overall survival were identified by using multivariable Cox regression and utilized to develop resectability criteria. RESULTS: Among 953 patients, four independent tumor-related predictors of poor prognosis were identified: lymph node metastasis (LNM) on imaging (HR 1.3, 95% confidence interval [CI] 1.07-1.59), tumor size > 5 cm (hazard ratio [HR] 1.52, 95% CI 1.25-1.85), multinodular lesions (HR 2.03, 95% CI 1.64-2.52), and major vascular invasion (HR 1.64, 95% CI 1.34-2.01). High-risk points were identified based on a point system associated with the hazards of each factor: 1 point each for LNM, tumor size > 5 cm, and major vascular invasion, and 2 points for multinodular lesions. Patients were categorized as resectable (R) for scores of 0-1 or borderline resectable (BR) for scores ≥ 2. Patients with BR disease (n = 385, 40.4%) had markedly worse median overall survival versus individuals with R disease (n = 568, 59.6%) (24.6 months vs. 69.7 months, p < 0.001). Validation in an external cohort confirmed these findings. CONCLUSIONS: The proposed preoperatively assessable resectability criteria can help differentiate BR versus R disease among ICC patients. These criteria offer a practical framework for preoperative risk stratification, aiding in treatment planning.
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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 | 0.001 | 0.000 |
| 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".