首页|First Affiliated Hospital of Xiamen University Reports Findings in Lung Cancer ( Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis)
First Affiliated Hospital of Xiamen University Reports Findings in Lung Cancer ( Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis)
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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 from Fujian, People's R epublic of China, by NewsRx journalists, research stated, "We performed this sys tematic review and meta-analysis to investigate the performance of ML in detecti ng genetic mutation status in NSCLC patients. We conducted a systematic search o f PubMed, Cochrane, Embase, and Web of Science up until July 2023."The news correspondents obtained a quote from the research from the First Affili ated Hospital of Xiamen University, "We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR. We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data . From a genetic mutation perspective, 121 studies focused on EGFR mutation stat us analysis. In the validation set, for the detection of EGFR mutation status, t he aggregated c-index was 0.760 (95 %CI: 0.706-0.814) for clinical f eature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiom ics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics mode ls, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics model s. When combined with clinical features, the aggregated c-index was 0.807 (95% CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773 -0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.8 54) for PET-CT-based radiomics models. In the validation set, the aggregated c-i ndexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7 . The use of radiomicsbased methods for early discrimination of EGFR mutation s tatus in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process."
FujianPeople's Republic of ChinaAsiaCancerCyborgsEmerging TechnologiesGeneticsHealth and MedicineLung Ca ncerLung Diseases and ConditionsLung NeoplasmsMachine LearningOncology
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