Segmentation results per subject for different approaches using MRI, CTA and distance features.^*

<p>The results are compared to relative component volumes in histology.</p><p>* C = calcification, F = fibrous tissue, LRNC = lipid-rich necrotic core, Method 1 = blurring and Dice weighting, Method 2 = weighting by contour distance, Method 3 = Gaussian outlier rejection.</p

The relation between household income and surgical outcome in the Dutch setting of equal access to and provision of healthcare

<div><p>Background</p><p>The impact of socioeconomic disparities on surgical outcome in the absence of healthcare inequality remains unclear. Therefore, we set out to determine the association between socioeconomic status (SES), reflected by household income, and overall survival after surgery in the Dutch setting of equal access and provision of care. Additionally, we aim to assess whether SES is associated with cause-specific survival and major 30-day complications.</p><p>Methods</p><p>Patients undergoing surgery between March 2005 and December 2006 in a general teaching hospital in the Netherlands were prospectively included. Adjusted logistic and cox regression analyses were used to assess the independent association of SES–quantified by gross household income–with major 30-day complications and long-term postoperative survival.</p><p>Results</p><p>A total of 3929 patients were included, with a median follow-up of 6.3 years. Low household income was associated with worse survival in continuous analysis (HR: 1.05 per 10.000 euro decrease in income, 95% CI: 1.01–1.10) and in income quartile analysis (HR: 1.58, 95% CI: 1.08–2.31, first [i.e. lowest] quartile relative to the fourth quartile). Similarly, low income patients were at higher risk of cardiovascular death (HR: 1.26 per 10.000 decrease in income, 95% CI: 1.07–1.48, first income quartile: HR: 3.10, 95% CI: 1.04–9.22). Household income was not independently associated with cancer-related mortality and major 30-day complications.</p><p>Conclusions</p><p>Low SES, quantified by gross household income, is associated with increased overall and cardiovascular mortality risks among surgical patients. Considering the equality of care provided by this study setting, the associated survival hazards can be attributed to patient and provider factors, rather than disparities in healthcare. Increased physician awareness of SES as a risk factor in preoperative decision-making and focus on improving established SES-related risk factors may improve surgical outcome of low SES patients.</p></div

Comparison to previous studies.

<p>Comparison to previous studies.</p

Different ways of handling registration accuracy in training.

<p>A. Soft labels for each class are derived by blurring the original segmentations. In this example  = 0.5 mm and the soft labels of the three classes sum to the Dice overlap between histology and <i>in vivo</i> data in each voxel (0.93 in this slice). In the hard segmentation dark gray is calcification (C), light gray fibrous tissue (F) and white lipid-rich necrotic tissue (N). B. Sample weights are determined by the distance between lumen and outer wall contours in histology (white line) and the <i>in vivo</i> data (blue dashed line). C. Outlier rejection: Based on 10% outlier rejection on the combination of all 13 vessels, the black areas in the right bottom figure would be rejected as outliers. In this slice mainly lipid/necrotic voxels from the right half of the section are considered outliers in feature space.</p

Two registered image slices.

<p>Examples are presented before and after applying the final deformation step as depicted in the right column of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094840#pone-0094840-g001" target="_blank">Figure 1</a>. In yellow the deformed histology vessel wall is shown, in red the <i>in vivo</i> vessel wall overlaid on the postcontrast MRI scan. In the orange regions they overlap. The Dice overlap for the top image increases from 0.59 to 0.86, for the bottom image from 0.28 to 0.44.</p

Segmentation results when only MRI, or only CTA, and distance features are used.

<p>Results are obtained including outlier rejection. White = LRNC, light gray = fibrous tissue and dark gray = calcification.</p

Settings for the different registration steps.^*

<p>* For registration the inverse transformation of steps 4 and 5 was applied to the <i>ex vivo</i> MRI. Def. model = deformation model, Comp. time = computation time, MI = Mutual Information.</p

Segmentation results when only MRI, CTA or distance features are used.^*

<p>Outlier rejection was performed before classifier training.</p><p>* C = calcification, F = fibrous tissue, LRNC = lipid-rich necrotic core.</p

MRI settings.

<p>MRI settings.</p

The association between household income and overall mortality.

<p>The association between household income and overall mortality.</p