Limited-site adaptation reveals representation-specific MRI transportability for lymphovascular invasion in endometrial cancer
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Objectives
To determine how clinically anchored MRI representations behave from supportive internal testing through locked institutional transfer and limited target-site adaptation for prediction of substantial lymphovascular space invasion (LVSI) in endometrial cancer.
Materials and Methods
This retrospective two-center study included 206 women undergoing preoperative 3-T MRI between March 2016 and March 2023. Hospital A provided training (n = 117) and supportive internal testing (n = 13); Hospital B provided strict external testing (n = 76; 12 positive). Age and CA125 formed the clinical baseline and were included in every imaging model. In 200 repeated stratified two-fold Hospital B splits, each patient was evaluated outside the adaptation half. Models remained locked; adaptation changed only the threshold or applied no-empirical-Bayes (no-EB) ComBat before threshold selection.
Results
DenseNet121, U-NEXtractor, and fusion U-NEXtractor achieved internal AUC 1.000 but strict external AUCs of 0.685, 0.671, and 0.669, respectively. At development-locked thresholds, sensitivities were 0.167, 0.583, and 0.417. Threshold adaptation increased median sensitivity to 0.833 for all three, with specificities of 0.625, 0.500, and 0.500. No-EB ComBat changed median AUC by −0.007, −0.007, and −0.014, respectively, versus strict transfer. U-NEXtractor prediction ranking was most stable (median Spearman ρ, 0.999), whereas fusion did not improve transportability. Empirical-Bayes ComBat lacked complete repeated-split coverage.
Conclusion
Strong internal performance did not establish transportability. Limited target-site threshold adaptation recovered operating sensitivity without improving discrimination; feature harmonization produced no consistent AUC gain and affected representations differently.
