Contrast-enhanced computed tomography-based radiomics models for the risk categorization of thymoma

doi:10.19401/j.cnki.1007-3639.2024.06.006

Abstract

Abstract:

Background and purpose: Preoperative risk categorization of thymoma is useful for treatment decisions but remains challenging. This study focused on training radiomics models using contrast-enhanced computed tomography (CECT) images for thymoma risk categorization and validating the model's performance, reliability and generalizability in a relatively large cohort. Methods: This retrospective cohort study analyzed the clinical data of thymoma patients (Masaoka Koga Ⅰ-Ⅲ) who underwent thymectomy surgery at the Affiliated Chest Hospital of Shanghai Jiao Tong University School of Medicine from January 2008 to December 2017. The cohort was divided into a training group (80%) and a test group (20%) using stratified random selection. The gold standard for histologic types was based on surgically resected specimens. Low-risk histologic types included A, AB and B1. High-risk histologic types included B2 and B3. Radiomics features were extracted from manually segmented regions of interest on preoperative CECT images. Interobserver correlation and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection. Model performance metrics included area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. Clinical characteristics were added to the combined model. Results: A total of 478 patients (mean age 51.3±12.3 years, 48.1% was male) were included. The AUC of the clinical model, the CECT-based model, and the model using both clinical and CECT features on the test set were 0.666, 0.831 and 0.850, respectively. The best performing model had a sensitivity of 0.829 and a specificity of 0.764. Conclusion: CECT-based radiomics models showed good performance in risk categorization of thymomas.

Key words: Mediastinal, Thymoma, Computed tomography, Machine learning

CLC Number:

R734.2

JIANG Tiaoyan, JIA Tianying, ZHANG Qin. Contrast-enhanced computed tomography-based radiomics models for the risk categorization of thymoma[J]. China Oncology, 2024, 34(6): 581-589.

Figures/Tables 6

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Characteristics	Low-risk thymomas (n=273)	High-risk thymomas (n=205)	P value
Gender n (%)
Male	122 (44.69)	107 (52.2)	0.10
Female	151 (55.31)	98 (47.8)
Age/year
x±s	54.5±10.8	50.9±13.7	0.01
Maximal diameter D/cm
x±s	6.18±2.97	6.07±2.57	0.98
Comorbidity n(%)
Present	9 (3.3)	36 (17.56)	<0.001
Absent	264 (96.70)	169 (82.44)
Masaoka-Koga stage n (%)
Ⅰ	110 (40.29)	28 (13.66)	<0.001
Ⅱ	148 (54.21)	117 (57.07)
Ⅲ	15 (5.49)	60 (29.27)

Characteristics	Training set (n=382)	Test set (n=96)	P value
Gender
Male	218 (57.07)	55 (57.29)	0.97
Female	164 (42.93)	41 (42.71)
Age/year
x±s	52.9±12.3	53.1±12.0	0.66
Maximal diameter D/cm
x±s	6.03±2.74	6.36±3.09	0.38
Concomitant disease n (%)
Present	35 (9.16)	10 (10.42)	0.71
Absent	347 (90.84)	86 (89.58)
Masaoka-Koga stage n(%)
Ⅰ	111 (29.06)	27 (28.13)	0.91
Ⅱ	211 (55.24)	54 (56.25)
Ⅲ	60 (15.71)	15 (15.63)
Risk category n (%)
Low risk	218 (57.07)	55 (57.29)	0.97
High risk	164 (42.93)	41 (42.71)

Data	AUC	95% CI	P value	Sensitivity	Specificity	Accuracy
Clinical	0.666	0.554-0.777	0.0028	0.341	0.945	0.688
CECT-1^*	0.800	0.711-0.890	<0.001	0.707	0.782	0.750
CECT-2^**	0.831	0.747-0.914	<0.001	0.756	0.818	0.792
Combined^***	0.850	0.773-0.927	<0.001	0.829	0.764	0.792