Differentiation Between Dementia With Lewy Bodies And Alzheimer’s Disease Using Voxel-Based Morphometry Of Structural MRI: A Multicenter Study

Figures & data

Table 1 Subjects Data

Center	Number Of Patients	DLB				AD				MR Scanner
		Men	Women	Age (Years)	MMSE	Men	Women	Age (Years)	MMSE
Tokyo (Shinjuku)	60	15	18	78.5±5.8	20.6±6.0	8	19	78.8±5.3	21.3±3.8	Siemens Avanto 1.5T
Sakura	84	30	29	75.9±6.5	21.6±3.8	6	19	80.4±3.8	19.2±3.4	Philips Gyroscan 1.5T
Yamagata	270	24	36	81.5±6.4	17.7±5.2	43	167	80.5±5.4	18.1±4.7	Siemens Symphony 1.5T
Tokyo (Kodaira)	51	11	16	76.4±6.6	18.2±5.7	9	15	75.3±7.8	22.0±3.8	Philips Achieva 3.0T, Siemens Verio 3.0T
Obu	45	7	9	78.2±7.4	20.1±7.1	12	17	79.1±6.6	17.9±5.4	Philips Ingenia 1.5T
Tsukuba	7	3	0	76.0±8.9	24.0±1.7	1	3	73.0±8.1	13.5±6.0	Siemens Avanto 1.5T
Tokyo (Itabashi)	8	2	3	78.2±4.0	20.6±6.3	2	1	83.3±4.5	23.0±1.7	GE Signa HDxt 3.0T, GE Genesis Signa 1.5T
Tokyo (Oota)	51	7	15	77.5±7.3	20.0±5.3	8	21	77.8±7.7	19.2±5.0	TOSHIBA Excelart Vantage 1.5T
Kawasaki	27	4	5	75.8±4.6	20.8±5.4	6	12	81.8±7.3	18.6±5.2	Philips Gyroscan Intera 1.5T
Chiba	21	4	1	73.0±6.2	22.6±1.9	4	12	69.9±9.4	18.2±5.7	Philips Gyroscan Intera 1.5T
Total	624	107	132	78.0±6.7	19.8±5.4	99	286	79.3±6.5	18.7±4.8

Abbreviations: DLB, dementia with Lewy bodies; AD, Alzheimer’s disease.

Figure 1 VOIs in the VSRAD® software program. (A) VOI in medial temporal lobe structures. (B) VOI in the dorsal brain stem.

Table 2 Comparison Of Single Index Values For Characterizing Atrophy In The Target VOIs Between DLB And AD

Index	DLB (n=239)	AD (n=385)
MTL_Z	1.73±0.88	2.44±1.05**
DBS_Z_gray	0.57±0.23*	0.53±0.25
DBS_Z_white	0.67±0.57	0.65±0.72
DBS_Z_gray_ratio	0.44±0.32**	0.28±0.20
DBS_Z_white_ratio	0.47±0.48**	0.30±0.34

Notes: *P<0.05, **P<0.001.

Abbreviations: VOI, volume of interest; DLB, dementia with Lewy bodies; AD, Alzheimer’s disease; MTL, medial temporal lobe; DBS, dorsal brain stem.

Table 3 Discrimination Performance Of DLB And AD Using Single Indices

Index	DLB Cutoff	Training Data (n=414)				Test Data (n=210)
		AUC	Sensitivity	Specificity	Accuracy	Sensitivity	Specificity	Accuracy
MTL_Z	<2.19	0.72	75.7%	59.2%	65.2%	75.9%	43.9%	57.1%
DBS_Z_gray	≥0.34	0.54	87.5%	22.5%	46.4%	86.2%	17.9%	46.2%
DBS_Z_white	≥0.32	0.56	69.1%	46.2%	54.6%	72.4%	30.9%	48.1%
DBS_Z_gray_ratio	≥0.27	0.68	68.4%	60.7%	63.5%	70.1%	50.4%	58.6%
DBS_Z_white_ratio	≥0.33	0.66	50.7%	75.6%	66.4%	59.8%	55.3%	57.1%

Table 4 Discrimination Performance Of DLB And AD Using Multiple Indices

DLB Cutoff	Training Data (n=414)			Test Data (n=210)
	Sensitivity	Specificity	Accuracy	Sensitivity	Specificity	Accuracy
MTL_Z < 2.185 & DBS_Z_gray_ratio ≥ 0.195 & DBS_Z_white_ratio ≥ 0.195	57.2%	82.8%	73.4%	56.3%	68.3%	63.3%
MTL_Z < 2.0 & DBS_Z_gray_ratio ≥ 0.2 & DBS_Z_white_ratio ≥ 0.2	52.0%	84.4%	72.5%	50.6%	75.6%	65.2%

Figure 2 Decision tree for differentiation of DLB and AD using VBM results in the training data set. Below each node, the numbers of AD (left) and DLB (right) patients are shown. The class of AD or DLB as the classification result is shown at the center of each node. In this decision tree, the condition classified as DLB corresponds to the terminal node on the right end, and “MTL_Z <2.185 and DBS_Z_white_ratio ≥0.195 and DBS_Z_gray_ratio” is the cutoff.

Table 5 Association Of Single Index Values With Core Clinical Features In DLB

Index	Core Clinical Features
	Fluctuating Cognition		Visual Hallucination		Parkinsonism		REM Sleep Behaviour Disorder
	Positive (n=56)	Negative (n=64)	Positive (n=96)	Negative (n=24)	Positive (n=72)	Negative (n=48)	Positive (n=55)	Negative (n=65)
MTL_Z	1.75±0.94	1.63±0.77	1.71±0.78	1.58±1.12	1.74±0.86	1.59±0.85	1.63±0.96	1.73±0.77
DBS_Z_gray	0.59±0.24	0.59±0.25	0.57±0.23	0.65±0.30	0.62±0.25	0.54±0.22	0.57±0.22	0.60±0.27
DBS_Z_white	0.79±0.64	0.60±0.63	0.66±0.62	0.80±0.70	0.71±0.67	0.64±0.58	0.65±0.59	0.72±0.68
DBS_Z_gray_ratio	0.45±0.33	0.48±0.34	0.42±0.27*	0.64±0.49	0.47±0.34	0.46±0.33	0.48±0.35	0.45±0.32
DBS_Z_white_ratio	0.56±0.64	0.43±0.49	0.43±0.44*	0.71±0.89	0.49±0.60	0.48±0.51	0.48±0.48	0.49±0.64

Note: *P<0.05.