Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease

doi:10.3748/wjg.v30.i25.3155

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Jul 7, 2024 (publication date) through Jul 22, 2024

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Jul 7, 2024; 30(25): 3155-3165
Published online Jul 7, 2024. doi: 10.3748/wjg.v30.i25.3155

Table 1 The median value of selected radiomics feature

Datasets		Training set			Internal validation set			External validation set
Classifications		CD	PIL	P value	CD	PIL	P value	CD	PIL	P value
Phases	Features
N	OSV	0.58	0.38	0.00^b	0.63	0.36	0.00^b	0.60	0.39	0.00^b
	LSGL	-0.19	-0.28	0.00^b	-0.23	-0.28	0.11	-0.25	-0.30	0.10
	WLLHGR	0.81	0.68	0.00^b	0.83	0.70	0.00^b	0.70	0.61	0.09
	WHLLFM	-2.61	-0.60	0.00^b	-2.99	-0.64	0.00^b	-1.52	-0.86	0.01^a
	WHHHGL	2.35	3.21	0.00^b	2.21	2.89	0.00^b	2.65	3.28	0.00^b
A	OSV	0.58	0.37	0.00^b	0.62	0.39	0.00^b	0.62	0.40	0.00^b
	WHLLFM	-2.96	-0.65	0.00^b	-4.42	-0.85	0.00^b	-1.77	-0.80	0.00^b
	WLLLGR	0.84	0.73	0.00^b	0.85	0.72	0.00^b	0.77	0.69	0.00^b
V	OSV	0.55	0.37	0.00^b	0.70	0.37	0.00^b	0.59	0.40	0.00^b
	LSGL	-0.18	-0.28	0.00^b	-0.20	-0.25	0.03^a	-0.23	-0.30	0.00^b
	WHLLFM	-2.90	-0.77	0.00^b	-5.08	-0.68	0.00^b	-1.93	-1.05	0.00^b
	WLLLGA	0.17	0.10	0.00^b	0.18	0.10	0.00^b	0.14	0.08	0.00^b
	WLLLGR	0.84	0.74	0.00^b	0.85	0.71	0.00^b	0.79	0.69	0.00^b

^aP < 0.05.

^bP < 0.01.

PIL: Primary intestinal lymphoma; CD: Crohn's disease; N: Non-contrast enhanced; A: Arterial; V: Venous; OSV: Original shape voxel volume; LSGL: Log sigma 3-0-mm-3D glcm lmc2; WLLHGR: Wavelet LLH glrlm run percentage; WHLLFM: Wavelet HLL firstorder minimum; WHHHGL: Wavelet HHH glrlm long run high gray level emphasis; WLLLGR: Wavelet LLL glrlm run percentage; WLLLGA: Wavelet LLL glcm autocorrelation.

Table 2 The performance of models in the internal/external validation set

Dataset	Score	Radiomics (All phase)	Combined (radiomics + clinical)
Internal validation set	AUC	0.96 (95%CI: 0.90-1.00)	0.99 (95%CI: 0.96-1.00)
	Accuracy	0.92 (95%CI: 0.78-0.98)	0.94 (95%CI: 0.81-1.00)
	Sensitivity	0.93 (95%CI: 0.68-1.00)	1.00 (95%CI: 0.78-1.00)
	Specificity	0.91 (95%CI: 0.70-1.00)	0.91 (95%CI: 0.70-1.00)
External validation set	AUC	0.98 (95%CI: 0.94-1.00)	1.00 (95%CI: 0.99-1.00)
	Accuracy	0.93 (95%CI: 0.82-0.98)	1.00 (95%CI: 0.93-1.00)
	Sensitivity	0.96 (95%CI: 0.79-1.00)	1.00 (95%CI: 0.86-1.00)
	Specificity	0.90 (95%CI: 0.73-0.98)	1.00 (95%CI: 0.88-1.00)

AUC: Area under the curve; CI: Confidence interval.

Citation: Xiao MJ, Pan YT, Tan JH, Li HO, Wang HY. Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease. World J Gastroenterol 2024; 30(25): 3155-3165
URL: https://www.wjgnet.com/1007-9327/full/v30/i25/3155.htm
DOI: https://dx.doi.org/10.3748/wjg.v30.i25.3155