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.

Table 2 Predictive values at varying pathological complete response prevalence levels

pCR prevalence (%)	NPV (95%CI) (%)	PPV (95%CI) (%)
15	95 (92-97)	42 (38-46)
18.8 (Validationcohort median)	94.9 (92-97)	54.4 (51-56)
22 (Fullcohort median)	93.7 (92-95)	59.7 (56-62)
27	90 (86-93)	59 (56-62)
30	88 (84-92)	63 (59-67)

NPV: Negative predictive value; PPV: Positive predictive value; pCR: Pathological complete response.

Table 3 Subgroup analyses per PROSPERO protocol and exploratory analyses

Subgroup	Studies (n)	Pooled AUC (95%CI)	I2 (%)	P value1
Prespecified subgroups
AI methodology
Radiomics	9	0.85 (0.81–0.88)	76.2	Reference
Deep learning (descriptive only)	2	0.92, 0.901
Hybrid (descriptive only)	2	0.915, 0.7201
Validation type
External	7	0.84 (0.80–0.88)	65.3	Reference
Internal/temporal (combined)	6	0.91 (0.87-0.94)	82.7	0.003
Internal (descriptive only)	4	0.92 (0.88-0.95)
Imaging timing
Pre-treatment	5	0.79-0.86
Longitudinal	3	0.90-0.97
Exploratory subgroups
Clinical data incorporation (exploratory analyses)
Imaging-only	6	0.86 (0.83-0.89)	72.3	Reference
Imaging + clinical	7	0.90 (0.86-0.93)	78.1	0.042
MRI sequence (exploratory analyses)
T2-only	6	0.86 (0.82-0.89)	70.1	Reference
Multiparametric (T2+DWI+DCE)	5	0.92 (0.89-0.95)	65.8	0.04

¹P value vs reference subgroup.

AUC: Area under the curve; ML: Machine learning; DL: Deep learning; DWI: Diffusion-weighted imaging; DCE: Dynamic contrast-enhanced.

Table 4 Risk of bias assessment using Prediction Model Risk of Bias Assessment Tool, Quality Assessment of Diagnostic Accuracy Studies-artificial intelligence, and Radiomics Quality Score, n (%)

Domain	PROBAST	QUADAS-AI	RQS
	Low	High	Unclear1	Low	High	Unclear1		Mean score (%)
Overall risk	5 (38.5)	8 (61.5)	0 (0)
By domain
Participants/patient selection	10 (76.9)	2 (15.4)	1 (7.7)	5 (38.5)	8 (61.5)	0 (0)
Predictors/index test	8 (61.5)	5 (38.5)	0 (0)	5 (38.5)	8 (61.5)	0 (0)
Outcome/reference standard	13 (100)	0 (0)	0 (0)	13 (100)	0 (0)	0 (0)
Analysis/flow and timing	5 (38.5)	8 (61.5)	0 (0)	10 (76.9)	0 (0)	2 (23.1)
RQS domains
Image protocol quality							28
Feature segmentation							42
Phantom study							0
Biological correlates							8
Model validation							45
Clinical utility (DCA)							0
Open science (code sharing)							15
Total RQS							28.30

¹Insufficient nCRT-to-surgery interval reporting.

PROBAST: Prediction Model Risk of Bias Assessment Tool; QUADAS-AI: Quality Assessment of Diagnostic Accuracy Studies-artificial intelligence; RQS: Radiomics quality score; DCA: Decision curve analysis.