首页|Radiation Oncology Department Reports Findings in Artificial Intelligence (Artif icial Intelligence-suggested Predictive Model of Survival in Patients Treated Wi th Stereotactic Radiotherapy for Early Lung Cancer)
Radiation Oncology Department Reports Findings in Artificial Intelligence (Artif icial Intelligence-suggested Predictive Model of Survival in Patients Treated Wi th Stereotactic Radiotherapy for Early Lung Cancer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Bergamo, Italy, b y NewsRx journalists, research stated, “Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting.” The news correspondents obtained a quote from the research from Radiation Oncolo gy Department, “Clinical variables of patients treated in three Institutions wit h SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients) . A predictive model was built using Cox regression (CR) and artificial neural n etworks (ANN) on reference cohort A and then tested on comparative cohorts. Coho rt B1 patients were older and with worse chronic obstructive pulmonary disease ( COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charl son Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The mode l was enhanced combining ANN and CR findings. The reference cohort was divided i nto prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model’s pre dictions on OS: grouping was close to statistical significance (p=0.081). One an d 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohor ts, the model successfully predicted two groups of patients with divergent OS tr ends: higher for Group 1 and lower for Group 2. The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when a pplied to different cohorts.”
Bergamo, Italy, Europe, Artificial Intel ligence, Cancer, Drugs and Therapies, Emerging Technologies, Health and Medicine , Lung Cancer, Lung Diseases and Conditions, Lung Neoplasms, Machine Learning, O ncology, Radiotherapy
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