Value of Computerized 3D Shape Analysis in Differentiating Encapsulated from Invasive Thymomas

<div><p>Objectives</p><p>To retrospectively investigate the added value of quantitative 3D shape analysis in differentiating encapsulated from invasive thymomas.</p><p>Materials and Methods</p><p>From February 2002 to October 2013, 53 patients (25 men and 28 women; mean age, 53.94 ± 13.13 years) with 53 pathologically-confirmed thymomas underwent preoperative chest CT scans (slice thicknesses ≤ 2.5 mm). Twenty-three tumors were encapsulated thymomas and 30 were invasive thymomas. Their clinical and CT characteristics were evaluated. In addition, each thymoma was manually-segmented from surrounding structures, and their 3D shape features were assessed using an in-house developed software program. To evaluate the added value of 3D shape features in differentiating encapsulated from invasive thymomas, logistic regression analysis and receiver-operating characteristics curve (ROC) analysis were performed.</p><p>Results</p><p>Significant differences were observed between encapsulated and invasive thymomas, in terms of cystic changes (<i>p</i>=0.004), sphericity (<i>p</i>=0.016), and discrete compactness (<i>p</i>=0.001). Subsequent binary logistic regression analysis revealed that absence of cystic change (adjusted odds ratio (OR) = 6.636; <i>p</i>=0.015) and higher discrete compactness (OR = 77.775; <i>p</i>=0.012) were significant differentiators of encapsulated from invasive thymomas. ROC analyses revealed that the addition of 3D shape analysis to clinical and CT features (AUC, 0.955; 95% CI, 0.935–0.975) provided significantly higher performance in differentiating encapsulated from invasive thymomas than clinical and CT features (AUC, 0.666; 95% CI, 0.626–0.707) (<i>p</i><0.001).</p><p>Conclusion</p><p>Addition of 3D shape analysis, particularly discrete compactness, can improve differentiation of encapsulated thymomas from invasive thymomas.</p></div

Univariate analysis of 3D shape features of encapsulated and invasive thymomas.

<p>Note—Data are mean ± standard deviation.</p><p>Univariate analysis of 3D shape features of encapsulated and invasive thymomas.</p

3D shape analysis software program.

<p>Each thymoma was manually segmented from surrounding structures on all CT images and their 3D shape features were automatically calculated using an in-house developed software program.</p

Interobserver variability of shape features of thymomas.

<p>Note—ICCs of less than 0.40 signifies poor agreement; 0.41–0.60, moderate agreement; 0.61–0.80, good agreement; and 0.81 or greater, excellent agreement.</p><p>^†ICC = intraclass correlation coefficients.</p><p>^††CI = confidence interval</p><p>Interobserver variability of shape features of thymomas.</p

Univariate analysis of clinical and CT features of encapsulated and invasive thymomas.

<p>Note—Data are numbers or mean ± standard deviation of each variable.</p><p>Univariate analysis of clinical and CT features of encapsulated and invasive thymomas.</p

ROC plot of binary logistic regression analysis with backward stepwise selection, using leave-one-out cross-validation method.

<p>Receiver operating characteristics (ROC) curve analysis of binary logistic regression models, using leave-one-out cross-validation method, in differentiating encapsulated from invasive thymomas. The graph shows that the combination of 3D shape analysis and CT features (blue line, AUC, 0.955; 95% CI, 0.935–0.975) has significantly higher discriminating performance in differentiating encapsulated from invasive thymomas compared to clinical and CT features (red line, AUC, 0.666; 95% CI, 0.626–0.707) (difference between AUC values, 0.289; <i>p</i><0.001). For reference, ROC analysis with 3D shape analysis alone is also demonstrated (green line, AUC, 0.896; 95% CI,0.868–0.923).</p

An example of texture analysis of a persistent PSN.

<p>(A) Thin-section CT scan shows an 18 mm PSN (arrow) with fissural retraction in the right lower lobe in a 62-year-old male. (B) Texture analysis of the PSN shows high mean attenuation and low negative skewness (−305.5 Hounsfield units and −0.378, respectively). As this PSN was persistent, he underwent lobectomy and was diagnosed as having adenocarcinoma.</p

CT features in 77 Transient and Persistent PSNs.

<p>Note: Except where indicated, data are numbers of nodules.</p><p>Data are means ± standard deviations.</p><p>Calculated with the independent sample <i>t</i> test.</p><p>Calculated with the Pearson <b>χ²</b> test.</p><p>PSNs  =  part-solid nodules.</p

The percentile CT numbers in 77 PSNs.

<p>Data are means ± standard deviations of PSNs' attenuation values.</p><p>^a Independent sample <i>t</i> test</p><p>PSNs  =  part-solid nodules.</p

Results of Logistic Regression Analysis for Clinical, Thin-Section CT and Texture analysis of Transient and Persistent PSNs.

<p>Whole PSN data.</p><p>PSNs  =  part-solid nodules</p><p>CI  =  confidence interval.</p