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

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.