Artificial intelligence-driven three-dimensional modeling of biliary strictures using magnetic resonance cholangiopancreatography for quantitative assessment and stent planning

Table 1 Baseline demographic and clinical characteristics of the study cohort, n (%)/mean ± SD

Characteristic	Controls (n = 242)	Cases (n = 142)	Total (n = 384)
Age (years)	48.8 ± 14.1	68.0 ± 11.2	55.9 ± 16.0
Sex
Male	121 (50.0)	81 (57.0)	202 (52.6)
Female	121 (50.0)	61 (43.0)	182 (47.4)
Stricture characteristics (cases only, n = 142)
Ground-truth severity
Grade 0 (none)	242 (100)	0 (0)	242 (63.0)
Grade 1 (mild)	0 (0)	77 (54.2)	77 (20.1)
Grade 2 (severe)	0 (0)	65 (45.8)	65 (16.9)
Etiology
Malignant		129 (90.8)
Benign		11 (7.7)
Indeterminate		2 (1.4)
Distribution
Focal		137 (96.5)
Diffuse		5 (3.5)
Location
Extrahepatic		106 (74.6)
Intrahepatic		7 (4.9)
Perihilar/hilar		29 (20.4)
Bismuth type
Bismuth type I		4 (2.8)
Bismuth type II		3 (2.1)
Bismuth type IIIa		7 (4.9)
Bismuth type IIIb		2 (1.4)
Bismuth type IV		5 (3.5)
Bismuth type, indeterminate		8 (5.6)
Stricture length, mm, median (range)		25.0 (6.7-120.0)
Max upstream duct diameter, mm		13.7 ± 3.0

Severity is an expert-assigned ordinal ground-truth grade (0 = no stricture, 1 = mild, 2 = severe). Bismuth-Corlette type was assessed for perihilar (hilar) strictures; “indeterminate” denotes cases spanning types (e.g., II-IIIa) or not classifiable. Malignant etiologies comprised cholangiocarcinoma (n = 78), pancreatic cancer (n = 36), gallbladder cancer (n = 9), ampullary cancer (n = 4), and metastatic/extrinsic disease (n = 1); benign/indeterminate causes included inflammatory (primary sclerosing cholangitis/immunoglobulin G4, n = 5), chronic-pancreatitis-related (n = 3), and postoperative/anastomotic (n = 2) strictures.

Table 2 Summary of segmentation and morphology-restoration experiments selected for the main manuscript

Analysis component	Detailed experiment summarized	Representative condition for main table	Primary metric	Result	Main interpretation
Bile duct segmentation	Swin-UNETR input resizing and window-size comparison	Input size 100 × 200 × 200; window size 96 × 96 × 96	Dice score	0.8113	Best result in the segmentation hyperparameter comparison; supports feasibility of whole biliary-tree segmentation
Bile duct segmentation	Extended normal-group segmentation experiment	Best-performing model in the extended normal-group setting	Dice score	0.8944	Higher overlap in non-strictured anatomy; useful for showing that stenotic signal loss is the harder condition
Morphology restoration	Loss-function comparison	DiceFocalLoss	Dice score	0.7531	Best restoration result; appropriate main-text evidence for imbalance-aware restoration of thin ductal structures
Morphology restoration	Training-iteration comparison	300000 iterations	Dice score	0.7508	Longer training improved reconstruction of interrupted duct segments
Morphology restoration	Lesion-weighting comparison	Lesion weight = 9	Dice score	0.7245	Targeted lesion weighting improved restoration around the synthetically interrupted segment
Three-dimensional reassembly and planning geometry	Representative qualitative reconstruction and centerline-guided mesh generation	Simple extrahepatic anatomy with limited branch overlap	Qualitative feasibility	Plausible restoration/candidate mesh	Shows technical feasibility for support path, approximate caliber, and curvature, but should not be interpreted as a validated stent design

Table 3 Projection-based classification performance for stricture presence and severity

Experiment	Evaluation level/aggregation	Accuracy (95%CI)	Balanced accuracy (95%CI)	Macro F1 (95%CI)	ROC AUC (95%CI)
Binary presence, view OOF	Individual projected view; patient-cluster bootstrap	0.9478 (0.9302-0.9636)	0.9399 (0.9187-0.9590)	0.9430 (0.9236-0.9603)	0.9768 (0.9649-0.9866)
Binary presence, patient OOF	Case-level aggregation across projected views	0.9770 (0.9605-0.9934)	0.9721 (0.9494-0.9909)	0.9749 (0.9564-0.9928)	0.9953 (0.9903-0.9988)
Binary presence, hold-out ensemble	Internal 20% hold-out ensemble; soft voting	0.9737 (0.9342-1.0000)	0.9643 (0.9107-1.0000)	0.9713 (0.9262-1.0000)	0.9970 (0.9888-1.0000)
3-class severity, patient OOF	Case-level severity classification	0.8711 (0.8395-0.9000)	0.7833 (0.7302-0.8355)	0.7817 (0.7287-0.8330)	0.9328 (0.9070-0.9557)

Confidence intervals were obtained from 10000 patient-level bootstrap resamples; projected views were clustered by patient for the view-level analysis. Multiclass receiver operating characteristic area under the curve used macro-averaged one-vs-rest evaluation. The hold-out ensemble used soft-voting aggregation. OOF: Out-of-fold; ROC AUC: Receiver operating characteristic area under the curve.