Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer Disease: Do Regions Outside the Hippocampus Matter?

Article Figures & Data

Download figure
Open in new tab
Download powerpoint
Fig 1.
Example of NeuroQuant (A, left) and Neuroreader (A, right) color segmentations in the same subject and hippocampal scatter (B, left) and Bland-Altman plots (B, right). Note the high correlation (r = 0.79, P < .05) of hippocampal volumetrics between software packages across all subjects and the small underestimation bias (−0.12 mL, P < .05) of NR with respect to NQ.

Table 1:

Characteristics of study populations^{^a}

Characteristic	All Patients	MCI (Nonconverter)	MCI to AD (Converter)	P Value
No.	192	107	85
Age (yr)	74.8 ± 7.3	74.7 ± 7.6	75.0 ± 6.9	.7604
Sex				.3262
Female	75 (39)	38 (36)	37 (44)
Male	117 (61)	69 (64)	48 (56)
Education (yr)	15.7 ± 2.9	15.7 ± 3.0	15.6 ± 2.8	.7637
ADAS-13 (baseline)	17.7 ± 6.4	15.2 ± 6.1	20.9 ± 5.3	<.0001
MMSE (baseline)	27.1 ± 1.7	27.6 ± 1.7	26.5 ± 1.6	<.0001

Note:—MMSE indicates Mini-Mental State Examination.
↵a MCI or AD status was determined at 3-year follow-up by the ADNI criteria. Data are means. Percentages listed in parentheses are rounded to the nearest percentage.

Table 2:

AUC values for different brain regions separated by software package (NQ, NR) and method of analysis (univariable-multivariable)

Table 3:

Pearson correlation coefficients for NQ and NR volumetrics in regions with the same name

Feature	Pearson Coefficient (r)	95% CI	P Value
Thalamus	0.60	0.51–0.69	<.001
Putamen	0.61	0.51–0.69	<.001
Lateral ventricles	0.99	0.99–0.99	<.001
Hippocampus	0.79	0.72–0.83	<.001
Cerebellum	0.87	0.83–0.90	<.001
Amygdala	0.71	0.64–0.78	<.001