Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer

Table 2 Information of the colorectal cancer immune score evaluation model

Number	Model	ACC	AUC	95%CI	Sensitivity	Specificity	PPV	NPV	Precision	Recall	F1	Threshold	Cohort
0	DenseNet-121	0.741	0.797	0.7383-0.8556	0.707	0.769	0.714	0.762	0.714	0.707	0.711	0.520	Train
1	DenseNet-121	0.800	0.759	0.6502-0.8676	0.705	0.882	0.838	0.776	0.838	0.705	0.765	0.407	Test
2	DenseNet-169	0.709	0.780	0.7185-0.8406	0.768	0.661	0.650	0.777	0.650	0.768	0.704	0.385	Train
3	DenseNet-169	0.768	0.772	0.6741-0.8696	0.682	0.834	0.789	0.754	0.789	0.682	0.732	0.541	Test
4	DenseNet-201	0.718	0.765	0.7018-0.8274	0.495	0.901	0.803	0.686	0.803	0.495	0.612	0.591	Train
5	DenseNet-201	0.768	0.737	0.6305-0.8432	0.636	0.882	0.824	0.738	0.824	0.636	0.718	0.586	Test
6	ResNet-101	0.786	0.852	0.8032-0.9011	0.859	0.727	0.720	0.863	0.720	0.859	0.783	0.432	Train
7	ResNet-101	0.737	0.752	0.6503-0.8541	0.636	0.824	0.757	0.724	0.757	0.636	0.691	0.446	Test
8	ResNet-152	0.732	0.816	0.7603-0.8718	0.869	0.620	0.652	0852	0.652	0.869	0.745	0.336	Train
9	ResNet-152	0.737	0.736	0.6272-0.8438	0.614	0.843	0.771	0.717	0.771	0.614	0.684	0.501	Test
10	ResNet-34	0.782	0.860	0.8120-0.9072	0.808	0.760	0.734	0.829	0.734	0.808	0.769	0.444	Train
11	ResNet-34	0.747	0.741	0.6344-0.8474	0.705	0.784	0.738	0.755	0.738	0.705	0.721	0.498	Test
12	ResNet-50	0.795	0.863	0.8144-0.9120	0.828	0.769	0.745	0.845	0.745	0.828	0.785	0.464	Train
13	ResNet-50	0.747	0.754	0.6491-0.8598	0.614	0.863	0.794	0.721	0.794	0.614	0.692	0.501	Test

F1-score = 2 × (precision × recall)/(precision + recall).

ACC: Accuracy; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; CI: Confidence interval.