T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

Table 1 Clinical features and baseline characteristics of patients in the cohorts

Variable	Training cohort		P value	Validation cohort		P value
	Poor	Well/moderate		Poor	Well/moderate
Age (yr)	59.41 ± 11.26	63.38 ± 11.07	0.072	58.12 ± 9.63	61.95 ± 11.79	0.148
CEA (ng/mL)	16.65 ± 30.56	20.94 ± 61.37	0.319	24.13 ± 51.84	20.94 ± 75.57	0.446
CA199 (U/mL)	55.95 ± 101.64	41.13 ± 115.48	0.174	28.74 ± 36.85	26.32 ± 58.41	0.349
Size (cm)	4.26 ± 1.90	4.65 ± 1.59	0.252	4.24 ± 2.23	4.88 ± 1.85	0.067
Gender			0.963			0.777
Male	18 (56.25)	110 (58.51)		9 (52.94)	47 (60.26)
Female	14 (43.75)	78 (41.49)		8 (47.06)	31 (39.74)
Location			0.24			0.947
Left	11 (34.38)	43 (22.87)		4 (23.53)	15 (19.23)
Right	21 (65.62)	145 (77.13)		13 (76.47)	63 (80.77)
T stage			0.496			0.343
T1	Null	4 (2.13)		Null	2 (2.56)
T2	4 (12.50)	22 (11.70)		1 (5.88)	8 (10.26)
T3	22 (68.75)	142 (75.53)		11 (64.71)	58 (74.36)
T4	6 (18.75)	20 (10.64)		5 (29.41)	10 (12.82)
N stage			< 0.001			0.047
N0	9 (28.12)	96 (51.06)		6 (35.29)	43 (55.13)
N1	6 (18.75)	56 (29.79)		5 (29.41)	26 (33.33)
N2	17 (53.12)	36 (19.15)		6 (35.29)	9 (11.54)
Circumference			0.021			0.702
≤ 1/2	6 (18.75)	79 (42.02)		8 (47.06)	30 (38.46)
> 1/2	26 (81.25)	109 (57.98)		9 (52.94)	48 (61.54)
Neural invasion			0.033			0.81
Absent	12 (37.50)	112 (59.57)		8 (47.06)	42 (53.85)
Present	20 (62.50)	76 (40.43)		9 (52.94)	36 (46.15)
Vascular invasion			0.014			0.177
Absent	11 (34.38)	112 (59.57)		8 (47.06)	53 (67.95)
Present	21 (65.62)	76 (40.43)		9 (52.94)	25 (32.05)

CA199: Carbohydrate antigen 199; CEA: Carcinoembryonic antigen; Right: Ascending and transverse colon; Left: Descending colon, sigmoid colon, and rectum.

Table 2 Comparative analysis of machine learning modeling of radiomics

	AUC	95%CI	Sensitivity	Specificity	Accuracy	PPV	PPV
Training cohort
LR	0.737	0.656-0.818	0.527	0.875	0.577	0.961	0.239
SVM	0.986	0.973-0.999	0.947	1.000	0.955	1.000	0.762
KNN	0.880	0.835-0.924	0.649	1.000	0.700	1.000	0.327
RF	1.000	0.999-1.000	0.989	1.000	0.991	1.000	0.941
ET	1.000	1.000-1.000	1.000	1.000	1.000	1.000	1.000
XGBoost	1.000	1.000-1.000	1.000	1.000	1.000	1.000	1.000
LightGBM	0.972	0.953-0.992	0.910	0.969	0.918	0.994	0.646
MLP	0.796	0.723-0.869	0.660	0.812	0.682	0.954	0.289
Validation cohort
LR	0.728	0.586-0.870	0.692	0.765	0.577	0.931	0.351
SVM	0.684	0.527-0.841	0.756	0.588	0.955	0.894	0.345
KNN	0.629	0.485-0.772	0.628	1.000	0.700	0.875	0.256
RF	0.597	0.442-0.752	0.872	0.417	0.991	0.850	0.333
ET	0.620	0.497-0.743	0.423	1.000	1.000	0.943	0.250
XGBoost	0.594	0.430-0.758	0.808	0.471	1.000	0.875	0.348
LightGBM	0.601	0.464-0.739	0.372	0.882	0.918	0.935	0.234
MLP	0.735	0.604-0.866	0.641	0.824	0.682	0.943	0.333

AUC: Area under the curve; ET: Extra trees; LightGBM: Light gradient boosting machine; KNN: K-nearest neighbor; LR: Logistic regression; MLP: Multilayer perceptron; NPV: Negative predictive value; PPV: Positive predictive value; RF: Random forest; SVM: Support vector machine; XGBoost: Extreme gradient boosting.

Table 3 The prediction performance of the three models in the training group and the test group

Model	Training cohort				Validation cohort
	AUC	95%CI	Sensitivity	Specificity	AUC	95%CI	Sensitivity	Specificity
clinical	0.751	0.661-0.842	0.660	0.719	0.676	0.525-0.827	0.731	0.647
Radiomics	0.796	0.723-0.869	0.660	0.812	0.735	0.604-0.866	0.641	0.824
Radiomics-clinical model	0.862	0.796-0.927	0.777	0.812	0.761	0.635-0.887	0.705	0.765

AUC: Area under the curve.