Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis

Table 1 Patient-level diagnostic accuracy data included in the primary meta-analysis

Ref.	Country/design	Index test	Target/reference	Validation	N	TP/FN	TN/FP	Sensitivity	Specificity
Xiang et al[21], 2024	China; retrospective	CT ResNet50	GBC vs benign lesions; pathology	Internal non-training validation	84	28/5	39/12	0.848 (0.681-0.949)	0.765 (0.625-0.872)
Gupta et al[22], 2024	India; prospective	US deep learning	GBC vs non-GBC; pathology/cytology/biopsy/follow-up	Prospective test	273	169/14	67/23	0.923 (0.875-0.958)	0.744 (0.642-0.831)
Zhang et al[23], 2024	China; retrospective external validation	CT FCovResNet18	GBC vs XGC; pathology	External validation	74	48/4	18/4	0.923 (0.815-0.979)	0.818 (0.597-0.948)
Gupta et al[24], 2024	India; prospective independent test	US RadFormer	GBC vs XGC; pathology/cytology	Independent test	80	48/7	21/4	0.873 (0.755-0.947)	0.840 (0.639-0.955)
Wang et al[25], 2023	China; prospective multi-institutional	US XGBoost radiomics	GBC vs benign masses; pathology	External validation	104	18/4	79/3	0.818 (0.597-0.948)	0.963 (0.897-0.992)

Sensitivity and specificity are shown as point estimate (95% confidence interval). Extended model-selection rationale, threshold handling, validation design, and target-contrast details are provided in Supplementary Table 2. TP: True positive; FN: False negative; TN: True negative; FP: False positive; CT: Computed tomography; US: Ultrasonography; GBC: Gallbladder cancer; XGC: Xanthogranulomatous cholecystitis.