CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor

Zhang, Chengzhou; Yang, Qinglin; Lin, Fan; Ma, Heng; Zhang, Haicheng; Zhang, Ran; Wang, Ping; Mao, Ning

doi:10.3389/fonc.2021.744021

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Despite the known limitations of imaging, recent advances in technology such as “radiomics” have proven to be valuable to assist in the accurate diagnosis of cysts by using computer analysis of acquired images ( 53 ). Magnetic resonance imaging (MRI), has often been described as an additional adjunctive imaging modality with cysts to aid in diagnosis ( 19 , 43 ).…”

Section: Diagnosismentioning

confidence: 99%

Mediastinal thymic cysts: a narrative review

Cooley-Rieders

Haren

2022

Mediastinum

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Background and Objective Mediastinal thymic cysts are a relatively rare pathology. With the expansion of eligible individuals screened with cross-sectional imaging for lung cancer, it is likely that there will be an increase in the number of individuals presenting with these cysts. Understanding this rare pathology will become more important when this incidental pathology is encountered. Methods Search of PubMed was undertaken using keywords “mediastinal”, “mediastinum”, “thymic”, “thymus”, “cyst”. Relevant literature was reviewed and selected for this comprehensive narrative review, including case reports, case series, and retrospective reviews. Key Content and Findings Thymic cysts in the mediastinum can be classified into two broad categories, congenital and inflammatory. Accurate diagnosis by imaging is challenging and the majority of patients are asymptomatic. Literature suggests that the majority of cysts are benign, however an unknown percentage may harbor neoplastic processes and over time can cause significant compressive symptoms. Definitive treatment and diagnosis is surgical, with overall excellent outcomes. The decision to pursue surgical treatment versus surveillance requires a shared decision making approach with patients. Conclusions Given the scarcity of available high quality evidence regarding the management of mediastinal thymic cysts, this review provides practitioners a broad knowledge base to guide patients to make informed decisions.

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Section: Diagnosismentioning

confidence: 99%

Mediastinal thymic cysts: a narrative review

Cooley-Rieders

Haren

2022

Mediastinum

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“…In recent years, radiomics models have been developed in multiple solid tumors to predict pathological type, clinical stage, and prognosis ( 10 - 15 ). In TETs, radiomics analysis has been able to predict TNM stage and differentiate the type of thymic cyst ( 16 - 23 ). However, currently, no radiomics model exists that can predict surgically inseparable TETs from the great vein.…”

Section: Introductionmentioning

confidence: 99%

The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein

Li,

Wang,

Zhang

et al. 2023

Quant Imaging Med Surg

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Background The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists. Methods Patients who underwent thymectomy at our hospital from 2009 to 2017 were included in the screening process. After the selection of patients according to the inclusion and exclusion criteria, the cohort was randomly divided into training and testing groups, and CT images of these patients were collected. Subsequently, two-dimensional (2D) and three-dimensional (3D) regions of interest were labelled using ITK-SNAP 3.8.0 software, and Radiomics features were extracted using Python software (Python Software Foundation) and selected through the least absolute shrinkage and selection operator (LASSO) regression model. To construct the classifier, a support vector machine (SVM) was employed, and a nomogram was created using logistic regression to predict vascular inseparable TETs based on the radiomics score (radscore) and image features. To assess the accuracy of these models, area under receiver operating characteristic (ROC) curves of these models were calculated, and differences among the models were identified using the Delong test. Results In this retrospective study, 204 patients with TETs were included, among whom 21 were diagnosed with surgical vascularly inseparable TETs. The area under ROC curve (AUC) of the 2D model, 3D model, 2D + 3D model, and radiologist diagnoses were 0.94, 0.92, 0.95, and 0.87 in the training cohort and 0.95, 0.92, 0.98, and 0.78 in testing cohort, respectively. The Delong test revealed a significant improvement in the performance of the radiomics models compared to radiologists’ diagnoses. The logistic regression selected 3 image features, namely maximum diameter of the tumor, degree of abutment of vessel circumference >50%, and absence of the mediastinal fat layer or space between the tumor and surrounding structures. These features, along with the radscore, were included to develop a nomogram. The AUCs of this nomogram were 0.99 in both the training set and testing set, and the Delong test did not find a significant difference between ROC plots of the nomogram and radiomics models. Conclusions The proposed radiomics model could accurately predict surgical vascularly inseparable TETs preoperatively and was shown to have a higher predictive value than the radiologists.

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