CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”

World Journal of Surgical Oncology

Kaan Orhan¹  Hilal Özakıncı²  Çağlar Uzun³  Duru Karasoy⁴  Buse Mine Konuk Balcı⁵  Betül Bahar Kazak⁵  Ayten Kayi Cangir⁵  Yusuf Kahya⁵ 

[1] Ankara University Medical Design Application and Research Center (MEDITAM);Department of Pathology, Ankara University Faculty of Medicine;Department of Radiology, Ankara University Faculty of Medicine;Department of Statistics, Hacettepe University Faculty of Science;Department of Thoracic Surgery, İbn-i Sina Hospital, Ankara University Faculty of Medicine;

关键词: Radiomics;    Machine learning;    Thymoma;    Minimally invasive surgery;    Diagnostic tool;   

DOI  :  10.1186/s12957-021-02259-6

来源: DOAJ

【 摘 要 】

Abstract Introduction Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. Materials and methods In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report. Results Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis. Conclusions The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

【 授权许可】

Unknown