Table 2.

Comparison of performance in three ACNN models for predicting four-classification molecular subtypes of breast cancers.

ACNN models	Datasets	AUC	Sensitivity (%)	Specificity (%)	Accuracy (%)	F1-score
Monomodal ACNN model	Internal validation cohort (n=85)	0.75	66.67	53.33	51.76	0.53
	Test cohort A (n=93)	0.73	64.29	64.29	53.76	0.59
	Test cohort B (n=95)	0.75	52.94	56.25	54.73	0.55
Dual-modal ACNN model	Internal validation cohort (n=85)	0.81	71.43	66.67	62.35	0.64
	Test cohort A (n=93)	0.84	75.00	64.28	74.19	0.69
	Test cohort B (n=95)	0.81	61.11	68.75	68.42	0.69
Multimodal ACNN model	Internal validation cohort (n=85)	0.89	92.31	80.00	84.71	0.82
	Test cohort A (n=93)	0.92	91.67	78.57	81.72	0.80
	Test cohort B (n=95)	0.96	87.50	87.50	82.11	0.85

Note. —The monomodal ACNN model was trained and tested with greyscale US images.

The dual-modal ACNN model was trained and tested with greyscale US and CDFI images.

The multimodal ACNN model was trained and tested with greyscale US and CDFI as well as SWE images.

ACNN, assembled convolutional neural network; AUC, area under the receiver operating characteristic curve.