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

• Published in: World journal of surgical oncology Vol. 19; no. 1; p. 147
• Main Authors: Kayi Cangir, Ayten, Orhan, Kaan, Kahya, Yusuf, Özakıncı, Hilal, Kazak, Betül Bahar, Konuk Balcı, Buse Mine, Karasoy, Duru, Uzun, Çağlar
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.05.2021; BioMed Central; BMC
• Subjects: Biomarkers; Biopsy; Chemotherapy; Computed tomography; CT imaging; Decision making; Diagnosis; Diagnostic tool; Entropy; Health risks; Learning algorithms; Machine learning; Magnetic resonance; Medical imaging; Medical prognosis; Medical technology; Minimally invasive surgery; Multilayer perceptrons; Patients; Phenotypes; Positron emission; Positron emission tomography; Radiomics; Risk; Risk groups; Sensors; Statistical analysis; Support vector machines; Thoracic surgery; Thymoma; Tumors; Turkey; Ankara Turkey; Japan; Diagnostic tool; Minimally invasive surgery; Thymoma; Machine learning; Radiomics
• ISSN: 1477-7819; 1477-7819
• DOI: 10.1186/s12957-021-02259-6

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. 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. 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. 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.