Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study

Table 4 The performance of each model for predicting csPCa in all PI-RADS 3 lesions

modality	Training set				Inner test set				External validation set				Mean AUC*	P value
modality	AUC	ACC	SEN	SPE	AUC	ACC	SEN	SPE	AUC	ACC	SEN	SPE	Mean AUC*	P value
T2WI-model	0.740	0.668	0.793	0.633	0.738	0.774	0.680	0.800	0.695	0.763	0.706	0.778	0.717	0.264
DWI-model	0.798	0.802	0.690	0.833	0.635	0.730	0.440	0.811	0.681	0.813	0.471	0.905	0.658	0.086
ADC-model	0.805	0.784	0.655	0.819	0.767	0.739	0.760	0.733	0.724	0.775	0.588	0.825	0.746	0.269
Integrated-model	0.854	0.828	0.741	0.852	0.804	0.748	0.800	0.733	0.801	0.863	0.647	0.921	0.803	0.019
PSAD-model	0.724	0.634	0.793	0.590	0.709	0.652	0.840	0.600	0.692	0.650	0.765	0.619	0.701	0.013

csPCa clinically significant prostate cancer; T2WI T1 weighted imaging; DWI diffusion weighted imaging; ADC apparent diffusion coefficient; PSAD PSA-density; AUC area under the receiver operating characteristic curve; ACC accuracy; SEN sensitivity; SPE specificity
*Mean AUC = [AUC(Internal test set) + AUC(External validation set)]/2
The P-values from the non-inferiority tests

ISSN: 1471-2342