Preoperative Risk Score and Prediction of Long-Term Outcomes after Hepatectomy for Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: Accurate prediction of prognosis for patients with intrahepatic cholangiocarcinoma (ICC) remains a challenge. We sought to define a preoperative risk tool to predict long-term survival after resection of ICC. STUDY DESIGN: Patients who underwent hepatectomy for ICC at 1 of 16 major hepatobiliary centers between 1990 and 2015 were identified. Clinicopathologic data were analyzed and a prognostic model was developed based on the regression β-coefficients on data in training set. The model was subsequently assessed using a validation set. RESULTS: Among 538 patients, most patients had a solitary tumor (median tumor number 1; interquartile range 1 to 2) and median tumor size was 5.7 cm (interquartile range 4.0 to 8.0 cm). Median and 5-year overall survival was 39.0 months and 39.0%, respectively. On multivariable analyses, preoperative factors associated with long-term survival included tumor size (hazard ratio [HR] 1.12; 95% CI 1.06 to 1.18), natural logarithm carbohydrate antigen 19-9 level (HR 1.33; 95% CI 1.22 to 1.45), albumin level (HR 0.76; 95% CI 0.55 to 0.99), and neutrophil to lymphocyte ratio (HR 1.05; 95% CI 1.02 to 1.09). A weighted composite prognostic score was constructed based on these factors: [9 + (1.12 × tumor size) + (2.81 × natural logarithm carbohydrate antigen 19-9) + (0.50 × neutrophil to lymphocyte ratio) + (-2.79 × albumin)]. The model demonstrated good performance in the testing (area under the curve 0.696) and validation (0.691) datasets. The model performed better than both the T categories (area under the curve 0.532) and the cumulative stage classifications in the American Joint Committee on Cancer staging manual, 8th edition (area under the curve 0.559). When assessing risk of death within 1 year of operation, a risk score ≥25 had a positive predictive value of 59.8% compared with a positive predictive value of 35.3% for American Joint Committee on Cancer staging manual, 8th edition T4 disease and 31.8% for stage IIIB disease. CONCLUSIONS: Postsurgical long-term outcomes could be predicted using a composite weighted scoring system based on preoperative clinical parameters. The preoperative risk model can be used to inform patient to provider conversations and expectations before operation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".