인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Purpose We compared the predictive and prognostic values of leukocyte differential counts, systemic inflammatory (SIR) markers and cancer antigen 125 (CA-125) levels, and identified the most useful marker in patients with ovarian clear cell carcinoma (OCCC).
Materials and Methods The study included 109 patients with OCCC who did not have any inflammatory conditions except endometriosis, and underwent primary debulking surgery between 1997 and 2012. Leukocyte differential counts (neutrophil, lymphocyte, monocyte, eosinophil, basophil, and platelet), SIR markers including neutrophil to lymphocyte ratio (NLR), monocyte to lympho- cyte ratio (MLR), and platelet to lymphocyte ratio (PLR), and CA-125 levels were estimated to select potential markers for clinical outcomes.
Results Among potential markers (neutrophil, monocyte, platelet, NLR, MLR, PLR, and CA-125 levels) selected by stepwise comparison, CA-125 levels were best at predicting advanced stage disease, suboptimal debulking and platinum-resistance (cut-off values, ! 46.5, ! 11.45, and ! 66.4 U/mL; accuracies, 69.4%, 78.7%, and 68.5%) while PLR ! 205.4 predicted non- complete response (CR; accuracy, 71.6%) most accurately. Moreover, PLR < 205.4 was an independent factor for the reduced risk of non-CR (adjusted odds ratio, 0.17; 95% confi- dence interval [CI], 0.04 to 0.69), and NLR < 2.8 was a favorable factor for improved progression-free survival (PFS; adjusted hazard ratio, 0.49; 95% CI, 0.25 to 0.99) despite lack of a marker for overall survival among the potential markers.
Conclusion CA-125 levels may be the most useful marker for predicting advanced-stage disease. Suboptimal debulking and platinum-resistance, and PLR and NLR may be most effective to predict non-CR and PFS in patients with OCCC.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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