Development, validation, and comparison of a nomogram based on radiologic findings for predicting malignancy in intraductal papillary mucinous neoplasms of the pancreas: An international multicenter study
Bibliographic record
Abstract
BACKGROUND: Although we previously proposed a nomogram to predict malignancy in intraductal papillary mucinous neoplasms (IPMN) and validated it in an external cohort, its application is challenging without data on tumor markers. Moreover, existing nomograms have not been compared. This study aimed to develop a nomogram based on radiologic findings and to compare its performance with previously proposed American and Korean/Japanese nomograms. METHODS: We recruited 3708 patients who underwent surgical resection at 31 tertiary institutions in eight countries, and patients with main pancreatic duct >10 mm were excluded. To construct the nomogram, 2606 patients were randomly allocated 1:1 into training and internal validation sets, and area under the receiver operating characteristics curve (AUC) was calculated using 10-fold cross validation by exhaustive search. This nomogram was then validated and compared to the American and Korean/Japanese nomograms using 1102 patients. RESULTS: Among the 2606 patients, 90 had main-duct type, 900 had branch-duct type, and 1616 had mixed-type IPMN. Pathologic results revealed 1628 low-grade dysplasia, 476 high-grade dysplasia, and 502 invasive carcinoma. Location, cyst size, duct dilatation, and mural nodule were selected to construct the nomogram. AUC of this nomogram was higher than the American nomogram (0.691 vs 0.664, P = .014) and comparable with the Korean/Japanese nomogram (0.659 vs 0.653, P = .255). CONCLUSIONS: A novel nomogram based on radiologic findings of IPMN is competitive for predicting risk of malignancy. This nomogram would be clinically helpful in circumstances where tumor markers are not available. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.
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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 | 0.003 | 0.003 |
| 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".