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

Lee JH, Park CM, Park SJ, Bae JS, Lee SM, Goo JM - PLoS ONE (2015)

Bottom Line: Their clinical and CT characteristics were evaluated.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.

ABSTRACT

Objectives: To retrospectively investigate the added value of quantitative 3D shape analysis in differentiating encapsulated from invasive thymomas.

Materials and 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).

Conclusion: Addition of 3D shape analysis, particularly discrete compactness, can improve differentiation of encapsulated thymomas from invasive thymomas.

No MeSH data available.


Related in: MedlinePlus

3D shape analysis software program.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.
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pone.0126175.g001: 3D shape analysis software program.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.

Mentions: Characteristic CT image features of thymomas were reviewed in terms of tumor size, cystic change, and calcification. Cystic change was designated when there were definite focal circumscribed areas of low attenuation within tumors on CTs [11, 12]. In addition, each thymoma was manually segmented from surrounding structures on all CT images containing tumors, and their 3D shape features were automatically calculated using an in-house developed software program (Fig 1) [16]. Manual segmentation of the 53 thymomas was performed by one radiologist (J. H. L with 2 years of experience in chest CT) and confirmed by one chest radiologist (C. M. P. with 15 years of experience in chest CT). Quantitative 3D shape features included: (a) log_volume, (b) surface area (cm2), (c) sphericity, (d) discrete compactness, and (e) 3D roundness. We used natural logarithms (log_volume) of tumor volume instead of tumor volume as the data of natural logarithms of tumor volume follows a normal distribution according to the Kolmogorov-Smirnov test. The formulas as well as further detailed explanation of these 3D shape features are provided in S1 File.


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

Lee JH, Park CM, Park SJ, Bae JS, Lee SM, Goo JM - PLoS ONE (2015)

3D shape analysis software program.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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4418613&req=5

pone.0126175.g001: 3D shape analysis software program.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.
Mentions: Characteristic CT image features of thymomas were reviewed in terms of tumor size, cystic change, and calcification. Cystic change was designated when there were definite focal circumscribed areas of low attenuation within tumors on CTs [11, 12]. In addition, each thymoma was manually segmented from surrounding structures on all CT images containing tumors, and their 3D shape features were automatically calculated using an in-house developed software program (Fig 1) [16]. Manual segmentation of the 53 thymomas was performed by one radiologist (J. H. L with 2 years of experience in chest CT) and confirmed by one chest radiologist (C. M. P. with 15 years of experience in chest CT). Quantitative 3D shape features included: (a) log_volume, (b) surface area (cm2), (c) sphericity, (d) discrete compactness, and (e) 3D roundness. We used natural logarithms (log_volume) of tumor volume instead of tumor volume as the data of natural logarithms of tumor volume follows a normal distribution according to the Kolmogorov-Smirnov test. The formulas as well as further detailed explanation of these 3D shape features are provided in S1 File.

Bottom Line: Their clinical and CT characteristics were evaluated.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.

ABSTRACT

Objectives: To retrospectively investigate the added value of quantitative 3D shape analysis in differentiating encapsulated from invasive thymomas.

Materials and 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).

Conclusion: Addition of 3D shape analysis, particularly discrete compactness, can improve differentiation of encapsulated thymomas from invasive thymomas.

No MeSH data available.


Related in: MedlinePlus