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

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.