Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors

Table 4 Accuracy, sensitivity, specificity, F1 score and areas under the receiver operating characteristic curves for each tumor risk stratification, n (%)/95%CI

		Accuracy (n = 165)	Sensitivity	Specificity	F1 score (%)	AUROC
DLM	High	138 (84); (78-90)	81 (52/64); (76-86)	85 (86/101); (78-91)	79	0.90 (85-95)
	Moderate	131 (79); (71-87)	50 (21/42); (25-74)	89 (110/123); (82-96)	55	0.74 (67-81)
	Low	144 (87); (81-93)	86 (51/59); (81-91)	88 (93/106); (83-94)	83	0.96 (94-98)
	Overall	75	72	87	72	0.88 (83-93)
Combined model	High	148 (90); (86-94)	88 (56/64); (83-93)	91 (92/101); (83-98)	87	0.96 (94-98)
	Moderate	143 (87); (83-92)	69 (29/42); (66-71)	93 (114/123); (86-98)	72	0.83 (78-88)
	Low	152 (92); (85-96)	92 (54/59); (89-93)	92 (98/106); (86-97)	89	0.97 (96-98)
	Overall	84	83	92	83	0.94 (93-95)

DLM: Deep learning model; AUROC: Areas under the receiver operating characteristic curve.