Predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer via artificial intelligence: A systematic review and meta-analysis

Table 1 Characteristic of the studies included in the meta-analysis

Ref.	Country	Total cohort (n)	pCR Prevalence (%)	Imaging modality	AI approach	Validation type	Reported AUC
Bibault et al[49], 2018	France	95	23.2	CT	DL + radiomics	Internal (5-fold CV)	0.72
Ju et al[50], 2023	South Korea	156	17.3	PET/CT	Radiomics + ML	External	0.896
Wu et al[51], 2025	China	768	30.1	MRI	Hybrid (DL + radiomics)	External	0.915
Liu et al[52], 2017	China	222	17.1	MRI	Radiomics	Temporal	0.979
Li et al[53], 2021	China	80	18.8	MRI	Radiomics	Internal (LOO-CV)	0.945
Peng et al[54], 2023	China	83	26.5	MRI	Radiomics	Internal	0.944
Boldrini et al[55], 2022	Italy/China	59	16.9	MRI	Radiomics	External	0.83
Jin et al[56], 2021	China	141	30.5	MRI	Deep learning	External	0.92
Cao et al[57], 2025	China	181	17.1	MRI	Deep learning	External	0.9
Shi et al[58], 2023	China	77	22.1	MRI	Radiomics	External	0.84
Nie et al[59], 2016	China	48	22.9	MRI	Radiomics	Internal	0.84
Horvat et al[60], 2022	United States/Brazil	50	16	MRI	Radiomics	External	0.83
Petkovska et al[61], 2020	United States	102	18.6	MRI	Radiomics	Internal	0.75

ML: Machine learning; DL: Deep learning; CV: Cross-validation; LOO-CV: Leave-one-out cross-validation; CT: Computed tomography; PET: Positron emission tomography; MRI: Magnetic resonance imaging; AUC: Area under the curve.