Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance

Figures & data

Figure 1 Flowchart of study group inclusion process.

Abbreviations: DWI, diffusion-weighted imaging; MR, magnetic resonance; MRI, magnetic resonance imaging; PNETs, pancreatic neuroendocrine tumors.

Figure 2 Example of T2WI images showing segmentation of pancreatic neuroendocrine tumors.

Notes: Manual segmentation of the tumor was delineated by stacking up regions of interest slice-by-slice on the axial T2WI images (A). Histogram of the tumor on T2WI images (B).

Abbreviation: T2WI, T2-weighted imaging.

Table 1 Demographic characteristics of patients with pancreatic neuroendocrine tumors (PNETs)

Characteristics	PNETs			P-value
	Grade 1 (n=31)	Grade 2 (n=29)	Grade 3 (n=17)
Age (years)a	52.5±10.0	53.5±13.4	54.7±12.2	0.611
Sexb				0.296
Male	11 (35.5%)	13 (44.8%)	10 (58.8%)
Female	20 (64.5%)	16 (55.2%)	7 (41.2%)
Tumor locationb				0.206
Head or neck	14 (45.2%)	16 (55.2%)	4 (23.5%)
Body	7 (22.6%)	6 (20.7%)	8 (47.1%)
Tail	10 (32.2%)	7 (24.1%)	5 (29.4%)
Size (cm)a	1.88±0.39 cm	2.52±0.47 cm	3.23±0.43 cm	<0.05
Clinical symptom
Abdominal pain	13 (41.9%)	15 (51.7%)	12 (70.6%)	0.177
Abdominal bloating or diarrhea	5 (16.1%)	5 (17.2%)	2 (11.8%)	0.925
Yellow urine or icterus	3 (9.7%)	4 (13.8%)	1 (5.9%)	0.801
Marasmus	0 (0)	1 (3.4%)	2 (11.8%)	0.175
Others	8 (25.8%)	4 (12.9%)	1 (5.9%)	0.202
Asymptomatic	6 (19.4%)	2 (6.9%)	2 (11.8%)	0.422

Notes:

a Data are mean ± SD. P-value was calculated with one-way analysis of variance.

b Data are number of patients, with the percentage in parentheses. P-value was calculated with chi-squared or Fisher’s exact test.

Figure 3 Magnetic resonance images of a 58-year-old woman with pancreatic neuroendocrine tumor Grade 1.

Notes: Fat-suppressed liver acquisition with volume acceleration sequence. T1-(A) and T2-(B) weighted imaging, a well-defined mass (arrows) located in the uncinate process of pancreas shows hypo-intensity and hyper-intensity, respectively. Diffusion-weighted images shows the tumor (arrow) was isointense (C). The tumor (arrows) shows persistent hyper-enhancement during the arterial (D), portal venous (E), and delayed (F) phase.

Figure 4 Magnetic resonance images of a 64-year-old woman with pancreatic neuroendocrine tumor Grade 3.

Notes: Fat-suppressed liver acquisition with volume acceleration sequence. T1-(A) and T2-(B) weighted imaging, a well-defined mass (arrows) located in the body of pancreas shows hypo-intensity and slight hyper-intensity, respectively. Diffusion-weighted images shows the tumor (arrow) was hyper-intense (C). The tumor (arrows) shows persistent hypo-enhancement during the arterial (D), portal venous (E), and delayed (F) phase.

Table 2 Magnetic resonance imaging findings and signal intensity of pancreatic neuroendocrine tumors based on tumor grade

Features	Grade				P-value
	Total	Grade 1 (n=31)	Grade 2 (n=29)	Grade 3 (n=17)
Tumor margina					0.01
Well-defined	39	22 (71%)	12 (41.4%)	5 (29.4%)
III-defined	38	9 (29%)	17 (58.6%)	12 (70.6%)
Texturea					0.016
Solid	48	24 (77.4%)	18 (62.1%)	6 (35.3%)
Predominantly solid	29	7 (22.6%)	11 (37.9%)	11 (64.7%)
Predominantly cystic	0	0	0	0
Local invasion or metastasesa					<0.05
Present	16	0 (0%)	9 (31%)	7 (41.2%)
Absent	61	31 (100%)	20 (69%)	10 (58.8%)
Pancreatic atrophya					0.162
Present	20	5 (16.1%)	8 (27.6%)	7 (41.2%)
Absent	57	26 (83.9%)	21 (72.4%)	10 (58.8%)
Pancreatic duct dilatationa					0.102
Present	14	3 (9.7%)	5 (17.2%)	6 (35.3%)
Absent	63	28 (90.3%)	24 (82.8%)	11 (64.7%)
T1-weighted imagea					–
Isointense/hypointense	77	31 (100%)	29 (100%)	17 (100%)
Hyperintense	0	0	0	0
T2-weighted imagea					0.950
Hypointense	8	3 (9.7%)	3 (10.3%)	2 (11.8%)
Isointense	10	3 (9.7%)	5 (17.2%)	2 (11.8%)
Hyperintense	59	25 (80.6%)	21 (72.5%)	13 (76.4%)
Tumor enhancement					<0.05
Hyper	39	27 (87.1%)	12 (41.4%)	0 (0)
Hypo	38	4 (12.9%)	17 (58.6%)	17 (100%)
Diffusion restrictiona	27	3 (11.1%)	14 (51.9%)	10 (37%)	<0.05

Notes:

a Data are number of patients, with the percentage in parentheses. P-value was calculated with chi-squared or Fisher’s exact test.

Table 3 Multivariate logistic regression models for prediction of pancreatic neuroendocrine tumor grade with T2WI texture features

	Model	Features	OR (95% CI)	P-value	AUC (95% CI)	Cut-off value (SEN, SPE)
Grade 1 vs Grade 2	Model 1	Inverse difference momenta	1.768 (1.322–2.364)	0.000	0.833 (0.725–0.941)	−0.2 (0.871, 0.759)
Grade 1 vs Grade 3	Model 2	Energya	3.546 (0.554–22.675)	0.052	0.989 (0.970–1.000)	−1.358 (0.935, 1.0)
		Correlationa	2.563 (1.352–4.859)	0.004
		DifferenceEntropya	0.013 (0.001–0.184)	0.001
Grade 2 vs Grade 3	Model 3	DifferenceEntropya	17.166 (2.42–121.764)	0.004	0.813 (0.690–0.937)	−0.711 (0.69, 0.824)

Note:

a Data are adjusted ORs per one SD change.

Abbreviations: SEN, sensitivity; SPE, specificity; T2WI, T2-weighted imaging.

Table 4 Multivariate logistic regression models for prediction of pancreatic neuroendocrine tumor grade with DWI texture features

	Model	Features	OR (95% CI)	P-value	AUC (95% CI)	Cut-off value (SEN, SPE)
Grade 1 vs Grade 2	Model 1	Correlationa	2.473 (1.5–4.078)	0.003	0.841 (0.766–0.949)	−1.027 (0.645, 0.931)
		Contrasta	0.999 (0.996–1.002)	0.031
		Inverse difference momenta	1.989 (1.29–3.066)	0.073
Grade 1 vs Grade 3	Model 2	Maxintensitya	1.012 (1.002–1.021)	0.015	0.962 (0.908–1.0)	−0.792 (0.935, 0.941)
		Entropya	4.342 (0.683–27.578)	0.120
		Inverse difference momenta	2.678 (1.510–4.751)	0.020
Grade 2 vs Grade 3	Model 3	Maxintensitya	0.997 (0.994–1.0)	0.023	0.703 (0.544–0.862)	−0.914 (0.517, 0.824)

Note:

a Data are adjusted ORs per one SD change.

Abbreviations: DWI, diffusion-weighted imaging; SEN, sensitivity; SPE, specificity.

Figure S1 ROC curves of the multivariate logistic regression models for prediction of PNET grade with T2WI (A, Grade 1 vs Grade 2; B, Grade 1 vs Grade 3; C, Grade 2 vs Grade 3) and DWI (D, Grade 1 vs Grade 2; E, Grade 1 vs Grade 3; F, Grade 2 vs Grade 3) texture features. The AUCs are 0.833, 0.989, 0.813, 0.841, 0.962, and 0.703, respectively.

Abbreviations: PNET, pancreatic neuroendocrine tumors; T2WI, T2-weighted imaging; DWI, diffusion-weighted imaging.

Table S1 Comparisons of T2WI texture features among pancreatic neuroendocrine tumors (P-values, Kruskal–Wallis test with Bonferroni correction)

Texture features on T2WI		Median (P-value)
		Grade 1 vs Grade 2	Grade 1 vs Grade 3	Grade 2 vs Grade 3
F1	Volume count	367 vs 2202 (<0.001)	367 vs 3770 (<0.001)	2202 vs 3770 (0.192)
F2	Voxel value sum (×10^5)	1.89 vs 14.98 (<0.001)	1.89 vs 20.72 (<0.001)	14.98 vs 20.72 (0.148)
F3	Relative deviation	−185.5 vs −1081.4 (0.131)	−185.5 vs −2013.8 (<0.001)	−1081.4 vs −2013.8 (0.012)
F4	Kurtosis	2.8 vs 3.3 (0.002)	2.8 vs 4.0 (<0.001)	3.3 vs 4.0 (0.09)
F5	Frequency size	366 vs 2200 (<0.001)	366 vs 3769 (<0.001)	2200 vs 3769 (0.192)
F6	Glcm total frequency	1540 vs 8350 (<0.001)	1540 vs 14494 (<0.001)	8350 vs 14494 (0.216)
F7	Energy (×10^3)	1.66 vs 0.47 (<0.001)	1.66 vs 0.57 (0.003)	0.47 vs 0.57 (0.691)
F8	Inertia	734.5 vs 241.2 (<0.001)	734.5 vs 149.1 (<0.001)	241.2 vs 149.1 (0.044)
F9	Correlation (×10^3)	0.38 vs 0.65 (0.001)	0.38 vs 1.17 (<0.001)	0.65 vs 1.17 (0.021)
F10	Inverse difference moment (10^2)	5.85 vs 10.29 (<0.001)	5.85 vs 15.32 (<0.001)	10.29 vs 15.32 (0.034)
F11	Cluster prominence (×10^8)	2.00 vs 0.87 (0.001)	2.00 vs 0.51 (<0.001)	0.87 vs 0.51 (0.072)
F13	Difference variance (×10^3)	2.26 vs 1.47 (0.221)	2.26 vs 0.92 (<0.001)	1.47 vs 0.92 (0.006)
F14	DifferenceEntropy	0.53 vs 0.48 (0.063)	0.53 vs 0.43 (<0.001)	0.48 vs 0.43 (0.001)
F15	Inverse difference moment (×10^2)	5.58 vs 7.37 (0.037)	5.58 vs 10.24 (<0.001)	7.37 vs 10.24 (0.001)
F16	Short run emphasis (×10^3)	99.79 vs 99.96 (<0.001)	0.99.79 vs 0.99.98 (<0.001)	99.96 vs 99.98 (0.116)
F17	Long run emphasis	1.01 vs 1.00 (0.001)	1.01 vs 1.00 (<0.001)	1.00 vs 1.00 (0.121)
F18	Gray-level non-uniformity	3.43 vs 14.19 (<0.001)	3.43 vs 43.51 (<0.001)	14.19 vs 43.51 (0.118)
F19	Run length non-uniformity	361.26 vs 2125 (<0.001)	361.26 vs 3604 (<0.001)	2125 vs 3604 (0.192)
F20	Low gray-level run emphasis (10^3)	5.95 vs 1.26 (<0.001)	5.95 vs 0.75 (<0.001)	1.26 vs 0.75 (0.165)
F21	Short run low gray-level emphasis (10^3)	3.87 vs 0.85 (<0.001)	3.87 vs 0.54 (<0.001)	0.85 vs 0.54 (0.209)
F22	Long run low gray-level emphasis (10^2)	1.43 vs 0.29 (<0.001)	1.43 vs 0.16 (<0.001)	0.293 vs 0.16 (0.160)

Abbreviations: Glcm, Gray-Level Co-occurrence Matrix; T2WI, T2-weighted imaging.

Table S2 Comparisons of DWI texture features among pancreatic neuroendocrine tumors (P-values, Kruskal–Wallis test with Bonferroni correction)

Texture features on DWI		Median (P-value)
		Grade 1 vs Grade 2	Grade 1 vs Grade 3	Grade 2 vs Grade 3
F1	Maxintensity	270 vs 411 (0.003)	270 vs 628 (<0.001)	411 vs 628 (0.034)
F2	Median Intensity	213 vs 264 (0.029)	213 vs 370 (<0.001)	264 vs 370 (0.021)
F3	Mean value	201 vs 261 (0.020)	201 vs 373 (<0.001)	261 vs 373 (0.019)
F4	Standard deviation	35.5 vs 56.4 (0.018)	35.5 vs 67.2 (<0.001)	56.4 vs 67.2 (0.049)
F5	Variance	1260 vs 3185 (0.018)	1260 vs 2553 (<0.001)	3185 vs 2553 (0.049)
F6	Volume count	89 vs 382 (<0.001)	89 vs 1017 (<0.001)	382 vs 1017 (0.271)
F7	Voxel value sum	19661 vs 153737 (<0.001)	19661 vs 444309 (<0.001)	153737 vs 444309 (0.155)
F8	RMS	205 vs 266 (0.019)	205 vs 379 (<0.001)	266 vs 379 (0.019)
F9	Range	163 vs 291 (0.002)	163 vs 422 (<0.001)	291 vs 422 (0.062)
F10	Mean deviation	54.3 vs −5.6 (0.02)	54.3 vs −117.7 (<0.001)	−5.6 vs −117.7 (0.019)
F11	Energy (10)	0.19 vs 0.10 (<0.001)	0.19 vs 0.09 (<0.001)	0.10 vs 0.09 (0.119)
F12	Entropy	5.90 vs 6.88 (<0.001)	5.90 vs 7.06 (<0.001)	6.88 vs 7.06 (0.129)
F13	Frequency size	88 vs 381 (<0.001)	88 vs 1016 (<0.001)	381 vs 1016 (0.271)
F14	Quantile5	138 vs 168 (0.087)	138 vs 260 (<0.001)	168 vs 260 (0.007)
F15	Quantile10	150 vs 182 (0.045)	150 vs 290 (<0.001)	182 vs 290 (0.011)
F16	Quantile25	175 vs 213 (0.036)	175 vs 324 (<0.001)	213 vs 324 (0.017)
F17	Quantile50	212 vs 264 (0.027)	212 vs 370 (<0.001)	264 vs 370 (0.021)
F18	Quantile75	222 vs 295 (0.021)	222 vs 422 (<0.001)	295 vs 422 (0.019)
F19	Quantile90	246 vs 335 (0.015)	246 vs 465 (<0.001)	335 vs 465 (0.02)
F20	Quantile95	255 vs 360 (0.011)	255 vs 481 (<0.001)	360 vs 481 (0.025)
F21	Glcm total frequency	272 vs 1748 (<0.001)	272 vs 3786 (<0.001)	1748 vs 3786 (0.297)
F22	Correlation (×10^3)	0.17 vs 0.38 (<0.001)	0.17 vs 0.42 (<0.001)	0.38 vs 0.42 (0.41)
F23	Inverse difference moment	0.03 vs 0.05 (<0.001)	0.03 vs 0.06 (<0.001)	0.05 vs 0.06 (0.305)
F24	Haralick correlation (10^8)	3.13 vs 6.37 (0.001)	3.13 vs 7.43 (<0.001)	6.37 vs 7.43 (0.343)
F25	Haralick entropy	0.49 vs 0.62 (<0.001)	0.49 vs 0.67 (<0.001)	0.62 vs 0.67 (0.320)
F26	Angular second moment (10^3)	4.50 vs 1.14 (<0.001)	4.50 vs 0.71 (<0.001)	1.14 vs 0.71 (0.325)
F27	Contrast (×10^3)	5.48 vs 2.94 (0.005)	5.48 vs 2.25 (<0.001)	2.94 vs 2.25 (0.101)
F28	Sum entropy	0.71 vs 0.81 (<0.001)	0.71 vs 0.82 (<0.001)	0.81 vs 0.82 (0.640)
F29	Difference variance (10^3)	8.36 vs 4.50 (0.004)	8.36 vs 3.61 (<0.001)	4.50 vs 3.61 (0.110)
F30	Inverse difference moment (10^2)	2.12 vs 4.09 (<0.001)	2.12 vs 4.15 (<0.001)	4.09 vs 4.15 (0.149)
F31	Short run emphasis	0.99 vs 1.00 (<0.001)	0.99 vs 1.00 (<0.001)	1.00 vs 1.00 (0.233)
F32	Long run emphasis	1.04 vs 1.01 (<0.001)	1.04 vs 1.00 (<0.001)	1.01 vs 1.00 (0.231)
F33	Gray-level non-uniformity	1.71vs 4.82 (<0.001)	1.71 vs 7.58 (<0.001)	4.82 vs 7.58 (0.313)
F34	Run length non-uniformity	85.52 vs 372.26 (<0.001)	85.52 vs 996.25 (<0.001)	372.26 vs 996.25 (0.272)
F35	Low gray-level run emphasis (10^2)	2.54 vs 0.62 (<0.001)	2.54 vs 0.24 (<0.001)	0.62 vs 0.24 (0.259)
F36	Short run low gray-level emphasis (10^3)	1.64 vs 0.39 (<0.001)	1.64 vs 0.18 (<0.001)	0.39 vs 0.18 (0.327)
F37	Long run low gray-level emphasis (10^3)	6.54 vs 1.50 (<0.001)	6.54 vs 0.52 (<0.001)	1.50 vs 0.52 (0.232)

Abbreviations: DWI, diffusion-weighted imaging; Glcm, Gray-Level Co-occurrence Matrix; RMS, root mean square.