Cancer Imaging

Table 4 Performance of the models for predicting PTC recurrence in the validation cohort

From: Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma

Model	Classifier	AUC	95% CI		ACC	SPE	SEN
Model	Classifier	AUC	Low	High	ACC	SPE	SEN
Radiomics models	LR	0.706	0.601	0.812	0.739	0.908	0.371
	SVM	0.710	0.604	0.816	0.721	0.961	0.200
	KNN	0.617	0.499	0.735	0.676	0.842	0.314
	NN	0.567	0.454	0.679	0.685	1.000	0
Clinical models	LR	0.709	0.597	0.821	0.748	0.895	0.429
	SVM	0.669	0.556	0.782	0.694	0.934	0.171
	KNN	0.642	0.522	0.763	0.739	0.855	0.486
	NN	0.665	0.552	0.778	0.730	0.895	0.371
Combined models	LR	0.746	0.640	0.852	0.739	0.895	0.400
	SVM	0.754	0.649	0.859	0.766	0.921	0.429
	KNN	0.669	0.552	0.785	0.730	0.842	0.486
	NN	0.711	0.607	0.816	0.766	0.947	0.371

ACC: Accuracy; AUC: Area under the receiver operating characteristic curve; CI: Confidence interval; KNN: K-nearest neighbor; LR: Logistic regression; NN: Neural network; SEN: Sensitivity; SPE: Specificity; SVM: Support vector machine

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ISSN: 1470-7330

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