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

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 30, Issue 25

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (640)

All Articles published online

The chart showing PDF series, HTML series, Figures (1-5) series, Tables (1-2) series.

Item

Count

PDF

HTML

283

Figures (1-5)

Tables (1-2)

Sum=407

Featured Article

The chart showing Browse series, Download series.

Item

Count

Browse

Download

Sum=180

Publishing Process of This Article

Item

Count

Browse

Download

Sum=19

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

Times Cited of This Article

Times Cited (0)

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

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

Retrospective Study

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.

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