首页|Sichuan University Reports Findings in Thyroid Cancer (Contrastenhanced ultraso und image sequences based on radiomics analysis for diagnosis of metastatic cerv ical lymph nodes from thyroid cancer)
Sichuan University Reports Findings in Thyroid Cancer (Contrastenhanced ultraso und image sequences based on radiomics analysis for diagnosis of metastatic cerv ical lymph nodes from thyroid cancer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Thyroid Can cer is the subject of a report. According to news reporting out of Chengdu, Peop le's Republic of China, by NewsRx editors, research stated, "Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ul trasonography (US) is the first-line imaging method for evaluating the thyroid g land and CLNs." Our news journalists obtained a quote from the research from Sichuan University, "However, this assessment relies mainly on the subjective judgment of the sonog rapher and is very much dependent on the sonographer's experience. This prospect ive study was designed to construct a machine learning model based on contrast-e nhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis i n patients with TC. Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2- minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lym ph node imaging video, three machine learning models, namely, support vector mac hine, linear discriminant analysis (LDA), and decision tree (DT), were construct ed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). A total o f 75 lymph nodes were included in the study, with 42 benign cases and 33 maligna nt cases. Among the machine learning models constructed, the support vector mach ine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%."
ChengduPeople's Republic of ChinaAsi aCancerCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineHemic and Immune SystemsImmunologyLymph NodesLymphoid TissueM achine LearningOncologySupport Vector MachinesSurgeryThyroid CancerThy roid NeoplasmsUltrasoundVector Machines
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