Value of Computerized 3D Shape Analysis in Differentiating Encapsulated from Invasive Thymomas.
OBJECTIVE
To retrospectively investigate the added value of quantitative 3D shape analysis in differentiating encapsulated from invasive thymomas.


METHODS
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.


RESULTS
Significant differences were observed between encapsulated and invasive thymomas, in terms of cystic changes (p=0.004), sphericity (p=0.016), and discrete compactness (p=0.001). Subsequent binary logistic regression analysis revealed that absence of cystic change (adjusted odds ratio (OR) = 6.636; p=0.015) and higher discrete compactness (OR = 77.775; p=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) (p<0.001).


CONCLUSIONS
Addition of 3D shape analysis, particularly discrete compactness, can improve differentiation of encapsulated thymomas from invasive thymomas.
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
Jong Hyuk
JH
Lee
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea.
Chang Min
CM
Park
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea.
Sang Joon
SJ
Park
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
Jae Seok
JS
Bae
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
Sang Min
SM
Lee
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea.
Jin Mo
JM
Goo
D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D013945:Thymoma / Q000145:classification / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed