CAJ | 학술논문

目的探讨利用纳米传感器(亦称纳米鼻)气体识别技术筛查卵巢癌( OC)的可行性。方法收集48例OC患者和48例健康对照者的呼气标本，利用纳米传感器对以上两组受试者呼气标本进行气体指纹图谱的分析，通过判别因子分析( DFA)模式进行鉴别区分。结果OC患者和健康对照者之间达到良好的区分(89%的准确度)；早期OC患者与健康对照者之间达到75%的准确度。结论纳米传感器呼气分析技术作为一种非侵入性的筛查方法，对于OC的早期诊断具有潜在的应用价值。
목적탐토이용납미전감기(역칭납미비)기체식별기술사사란소암( OC)적가행성。방법수집48례OC환자화48례건강대조자적호기표본，이용납미전감기대이상량조수시자호기표본진행기체지문도보적분석，통과판별인자분석( DFA)모식진행감별구분。결과OC환자화건강대조자지간체도량호적구분(89%적준학도)；조기OC환자여건강대조자지간체도75%적준학도。결론납미전감기호기분석기술작위일충비침입성적사사방법，대우OC적조기진단구유잠재적응용개치。
Objective To investigate the feasibility of odor prints for discriminating the ovarian cancer patients with nanomaterial-based sensors . Methods Alveolar exhaled breath samples were collected from 96 volunteers, na-nose were used to discriminate the odor prints from 48 patients with ovarian cancer ( OC ) and 48 healthy controls by loading discriminant factor analysis ( DFA) pattern recognition. Results The blind DFA models showed: An ex-cellent discrimination between the ovarian cancer patients and the healthy controls ( 89% accuracy ); early stage versus healthy controls showed 75% accuracy. Conclusion The primary results can lead for a new non-invasive promising screening method for diagnoses early of OC of early stages and thus lowering the mortality of OC.