首页|Qilu Hospital of Shandong University Reports Findings in Lung Cancer (Machine le arning based on clinical information and integrated CT radiomics to predict loca l recurrence of stage Ia lung adenocarcinoma after microwave ablation)
Qilu Hospital of Shandong University Reports Findings in Lung Cancer (Machine le arning based on clinical information and integrated CT radiomics to predict loca l recurrence of stage Ia lung adenocarcinoma after microwave ablation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Lung Cancer is the subject of a report. According to news reporting out of Jinan, People’s Republic of China, by NewsRx editors, research stated, “To develop and compare t hree different machine learning-based models of clinical information and integra ted radiomics features predicting the local recurrence of stage Ia lung adenocar cinoma after microwave ablation (MWA) for assisting clinical decision-making. Th e data of 360 patients with stage Ia lung adenocarcinoma receiving MWA were incl uded in the training (n = 208), internal test (n = 90), and external test (n = 6 4) sets based on the inclusion and exclusion criteria.” Our news journalists obtained a quote from the research from the Qilu Hospital o f Shandong University, “The predictors associated with local recurrence were ide ntified using univariate and multivariate analyses of clinical information. The integrated radiomics features were extracted from pre-MWA and post-MWA (scanned immediately after the ablation) CT images, and ten radiomics features were selec ted by the t-test and least absolute shrinkage and selection operator (LASSO). T he L2-logistic regression of machine learning was applied for the clinical model , CT radiomics model and combined model including clinical predictors and radiom ics features. Model performance was evaluated by the receiver operating characte ristic (ROC) and decision curve analysis (DCA). The ablative margin was an indep endent clinical predictor (p = 0.001, odds ratio [OR] = 0.46, 95%CI: 0.29, 0.73). The combined model showed the highest a rea under the curve (AUC) value among the three models (training: 0.86, 95% CI: 0.81, 0.91; internal test: 0.93, 95%CI: 0.87, 0.98; external te st: 0.89, 95%CI: 0.79, 0.96).”
JinanPeople’s Republic of ChinaAsiaAdenocarcinomaCancerCyborgsEmerging TechnologiesHealth and MedicineLu ng CancerLung Diseases and ConditionsMachine LearningOncology
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