Limited-site adaptation reveals representation-specific MRI transportability for lymphovascular invasion in endometrial cancer
Year: 2026
Type: preprint
Study design: Observational Study
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.
Source: medRxiv
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7169000524