Work

Brain cortical structural differences between non-central nervous system cancer patients treated with and without chemotherapy compared to non-cancer controls: a cross-sectional pilot MRI study using clinically indicated scans

Year: 2017

Type: conference-paper

Abstract: Background: Increases in cancer survival have made understanding the basis of cancer-related cognitive impairment (CRCI) more important. CRCI neuroimaging studies have traditionally used dedicated research brain MRIs in breast cancer survivors with small sample sizes; little is known about other non-CNS cancers. However, there is a wealth of unused data from clinically-indicated MRIs that could be used to study CRCI. Objective: Evaluate brain cortical structural differences in those with non-CNS cancers using clinically-indicated MRIs. Design: Cross-sectional Patients: Adult non-CNS cancer and non-cancer control (C) patients who underwent clinically-indicated MRIs. Methods: Brain cortical surface area and thickness were measured using 3D T1-weighted images. An age-adjusted linear regression model was used and the Benjamini and Hochberg false discovery rate (FDR) corrected for multiple comparisons. Group comparisons were: cancer cases with chemotherapy (Ch+), cancer cases without chemotherapy (Ch-) and subgroup of lung cancer cases with and without chemotherapy vs C. Results: Sixty-four subjects were analyzed: 22 Ch+, 23 Ch- and 19 C patients. Subgroup analysis of 16 LCa was also performed. Statistically significant decreases in either cortical surface area or thickness were found in multiple ROIs primarily within the frontal and temporal lobes for all comparisons. Limitations: Several limitations were apparent including a small sample size that precluded adjustment for other covariates. Conclusions: Our preliminary results suggest that various types of non-CNS cancers, both with and without chemotherapy, may result in brain structural abnormalities. Also, there is a wealth of untapped clinical MRIs that could be used for future CRCI studies.

Source: International Symposium on Medical Information Processing and Analysis (SIPAIM)

DOI: 10.1117/12.2285971