The value of radiomics based on T2WI and DWI of MRI in preoperative prediction of extramural vascular invasion in rectal cancer

doi:10.16150/j.1671-2870.2024.01.007

Abstract

Abstract:

Objective To investigate the diagnostic performance of radiomics based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) of MRI for preoperative prediction of extramural vascular invasion (EMVI) in rectal cancer. Methods A total of 168 patients with pathology-confirmed rectal adenocarcinoma were enrolled during January 2010 to June 2023. The patients underwent preoperative rectal MRI scans, and they were randomly divided into training set and validation set at a 7∶3 ratio. Radiomic features from T2WI and DWI were extracted and selected by dimensionality reduction using the maximum relevance minimum redundancy (mRMR) method and the least absolute shrinkage and selection operator (LASSO) regression analysis with ten-fold cross-validation. The radiomic total score (Radscore) for each patient was calculated to make radiomics model. The training set enrolled three clinical features [gender, age and preoperative level of carcinoembryonic antigen (CEA)] and six magnetic resonance imaging features [ADC value, depth of infiltration, tumor length, tumor location, T staging and magnetic resonance imaging-defined extramural vascular invasion (mrEMVI)].The clinical model was established through univariable and multivariable logistic regression analysis based on above clinical and imaging features， and the clinical-radiomics model (combined model) was established with Radscore and independent risk factors from the clinical model. The diagnostic efficacy of each model was assessed using receiver operating characteristic (ROC) curve. The differences in performance among the models were compared using the DeLong test. The Calibration curves were employed to evaluate the consistence between the preoperative predictive results obtained from the nomogram and the postoperative pathological results. Additionally, decision curve analysis (DCA) was applied to evaluate the clinical utility of the three models. Results The area under the curve (AUC) of the ROC curve for the combined model, clinical model, and radiomics model in the training were 0.926, 0.888, 0.756, and were 0.917, 0.896, 0.782 in validation sets, respectively. The DeLong test showed that the diagnostic efficacy of combined model was higher than that of radiomics model in both training and validation sets (P<0.05). The diagnostic efficacy of combined model was better than that of clinical model in the training set (P<0.05). The calibration curve showed the consistency between the preoperative predictive results obtained from the nomogram and the postoperative pathological findings was satisfied. The DCA showed that the risk threshold probabilities between 0.24 and 0.77, the clinical benefit of combined model was higher than those of clinical model and the radiomics model. Conclusions For preoperative prediction of EMVI in rectal cancer,the radiomics model based on T2WI and DWI of MRI has a satisfied diagnostic efficiency, while the clinical-radiomics model (combined model) may further enhance the diagnostic efficiency.

Key words: Rectal cancer, Extramural vascular invasion, Magnetic resonance imaging, Radiomics, Prediction model, Nomogram

CLC Number:

DING Jingfeng, AO Weiqun, ZHU Zhen, SUN Jing, XU Lianggen, ZHENG Shibao, YU Jingjing, HU Jinwen. The value of radiomics based on T2WI and DWI of MRI in preoperative prediction of extramural vascular invasion in rectal cancer[J]. Journal of Diagnostics Concepts & Practice, 2024, 23(01): 46-56.

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References 29

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Equipment	Parameters Plane	T2WI			DWI Axial
Equipment	Parameters Plane	Axial oblique	Sagittal	Coronal	DWI Axial
Siemens Verio 3.0T	TR/TE,ms	4 000/97	4 000/97	4 000/97	9 700/93
	FOV,mm	240×240	220×220	220×220	280×350
	Thickness,mm	3	3	3	3
	b values	-	-	-	0,800,1 500
Siemens Avanto 1.5T	TR/TE,ms	4 120/97	3 940/85	4 990/96	4 912/95
	FOV,mm	200×200	250×250	240×240	250×250
	Thickness,mm	2.5	4	4	5
	b values	-	-	-	50,400,800

Characteristics	Training set （n=123）	Validation set (n=45)	t/χ²	P value
Age（years）	63.82±10.38	64.47±10.96	0.352	0.726
Gender（%）			0.164	0.686
Male	78（63.4）	27（60.0）
Female	45（36.6）	18（40.0）
CEA（%）			0.323	0.570
≤5 ng/mL	77(62.6)	26(57.8)
>5 ng/mL	46(37.4)	19(42.2)
ADC Value （×10^-3mm²/s）	0.81(0.72,0.91)	0.78(0.71,0.87)	-1.091	0.275
Infiltration depth （mm）	15.34±5.73	16.61±8.09	1.130	0.260
Length,（cm）	43.14±15.78	44.50±15.56	0.496	0.621
mrT stage（%）			0.235	0.628
T1~2	46(37.4)	15(33.3)
T3~4	77(62.6)	30(66.7)
mrEMVI（%）			0.362	0.547
Negative	80(65.0)	27(60.0)
Positive	43(35.0)	18(40.0)
pEMVI（%）			0.016	0.900
Negative	89(72.4)	33(73.3)
Positive	34(27.6)	12(26.7)
Radscore	-2.71(-3.87,-1.32)	-2.09(-3.87,-1.20)	-1.067	0.286

Characteristics	Training set			Validation set
Characteristics	EMVI（-）（n=89）	EMVI（+）(n=34)	P value	EMVI（-）(n=33)	EMVI（+）(n=12)	P value
Age（years）	64.31±10.42	62.53±10.30	0.396	63.7±12.1	66.7±6.9	0.423
Gender（%）			0.814			0.063
Male	32(36.0)	13(38.2)		10(30.3)	8(66.7)
Female	57(64.0)	21(61.8)		23(69.7)	4(33.3)
CEA (%)			0.171			0.097
≤5 ng/mL	59(66.3)	18(52.9)		22(66.7)	4(33.3)
>5 ng/mL	30(33.7)	16(47.1)		11(33.3)	8(66.7)
ADC Value（×10^-3mm²/s）	0.85(0.74,0.99)	0.75(0.61,0.80)	<0.001	0.82(0.72,0.93)	0.75(0.68,0.79)	0.066
Infiltration Depth（mm）	14.63±6.03	17.20±4.39	0.011	14.9±5.1	21.4±12.3	0.101
Length,（cm）	41.18±15.51	48.27±15.54	0.025	43.3±14.2	47.9±19.0	0.387
Location (%)			0.683			0.060^#
Upper	27(30.3)	8(23.5)		11(33.3)	0(0.0)
Middle	35(39.3)	16(47.1)		12(36.4)	6(50.0)
Low	27(30.3)	10(29.4)		10(30.3)	6(50.0)
mrT stage（%）			<0.001			0.074
T1-2	42(47.2)	4(11.8)		14(42.4)	1(8.3)
T3-4	47(52.8)	30(88.2)		19(57.6)	11(91.7)
mrEMVI（%）			<0.001			<0.001
Negative	71(79.8)	9(26.5)		26(78.8)	1 (8.3)
Positive	18(20.2)	25(73.5)		7 (21.2)	11(91.7)
Radscore	-3.14(-4.44,-2.08)	-1.40(-2.69,-0.69)	<0.001	-2.46(-4.19,-1.39)	-1.25(-1.74,-0.18)	0.004

Variables	Univariate		Multivariate（clinical model）		Multivariate（combined model）
Variables	OR(95% CI)	P value	OR(95% CI)	P value	OR(95% CI)	P value
Age	0.983（0.946~1.022）	0.393
Gender
Female
Male	0.907（0.401~2.051）	0.814
CEA
≤5 ng/mL
>5 ng/mL	1.748（0.782~3.907）	0.173
ADC	0.000（0.000~0.012）	<0.001	0.000（0.000~0.016）	<0.001	0.000（0.000~0.016）	0.001
Infiltration depth	1.085（1.008~1.167）	0.029
Length	1.030（1.003~1.058））	0.029
Location
upper
middle	1.543（0.576~4.136）	0.389
low	1.250（0.428~3.651）	0.683
mrT stage
T₁~T₂
T₃~T₄	6.702（2.180~20.607）	0.001	2.869(0.778~10.574)	0.113	3.899（0.940~16.172）	0.061
mrEMVI
Negative
Positive	10.957（4.363~27.518）	<0.001	8.643(2.886~25.886)	<0.001	7.928（2.397~26.221）	0.001
Radscore	1.862（1.336~2.596）	<0.001			2.048（1.301~3.226）	0.002

Models	Training set			Validation set
Models	AUC（95% CI）	Sensitivity	Specificity	AUC（95% CI）	Sensitivity	Specificity
Radiomics model	0.756（0.656~0.855）	0.676	0.787	0.782(0.626~0.937）	0.833	0.697
Clinical model	0.888（0.829~0.948）	0.824	0.865	0.896（0.753~1.000）	0.917	0.939
Combined model	0.926（0.879~0.973）	0.882	0.865	0.917（0.813~1.000）	0.917	0.909