Dergiler / Cyprus Journal of Medical Sciences / 2020 / Cilt: 5 - Sayı: 1

Discriminating Borderline Ovarian Tumors from Ovarian Cancer: Focus on Systemic İnflammatory Response Markers

Sayfa
36–40
DOI
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CONCLUSION This data will strengthen the discrimination of malignant tumors from BOTs and facilitate the decision-making on surgical radicality and may also be used in combination with imaging strategies, tumor markers, and frozen section to increase diagnostic accuracy.

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Amaç & Hipotez:BACKGROUND/AIMS This study aims to investigate the preoperative diagnostic accuracy of systemic inflammatory response (SIR) markers, including neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), in discriminating borderline ovarian tumors (BOTs) from malignancy and, thereby, prevent over- or underdiagnosis in the management of BOTs and ovarian malignancy.
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Temel Bulgular & Veri:MATERIAL and METHODS Medical records of 99 patients who underwent surgical treatment and had confirmed histopathologic diagnosis of primary malignant or BOT were retrospectively analyzed.
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Sonuç & Katkı:CONCLUSION This data will strengthen the discrimination of malignant tumors from BOTs and facilitate the decision-making on surgical radicality and may also be used in combination with imaging strategies, tumor markers, and frozen section to increase diagnostic accuracy.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

BACKGROUND/AIMS This study aims to investigate the preoperative diagnostic accuracy of systemic inflammatory response (SIR) markers, including neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), in discriminating borderline ovarian tumors (BOTs) from malignancy and, thereby, prevent over- or underdiagnosis in the management of BOTs and ovarian malignancy. MATERIAL and METHODS Medical records of 99 patients who underwent surgical treatment and had confirmed histopathologic diagnosis of primary malignant or BOT were retrospectively analyzed. The recommended cut-off values for preoperative NLR and PLR were determined using receiver operating characteristic. The associations of NLR and PLR with tumors’ malignancy potentials were analyzed using the Chi-square test or the Fisher’s exact test. RESULTS The mean NLR and PLR were significantly lower (p=0.002 and p=0.006, respectively) in BOTs group than in the epithelial ovarian carcinoma (EOC) group. Optimal cut-off points of NLR and PLR for discriminating BOTs and EOC group was 2.42 and 169.2, respectively. The likelihood of malignancy increased in group with NLR values >2.42 (p 169.2 (p<0.001; OR, 3.6, 95%; CI, 1.48–8.76). Most importantly, both NLR and PLR values were above the cut-off point, and the malignancy risk had a 12-fold increase (p<0.001; OR, 12.15, 95%; CI, 1.78–82.6). CONCLUSION This data will strengthen the discrimination of malignant tumors from BOTs and facilitate the decision-making on surgical radicality and may also be used in combination with imaging strategies, tumor markers, and frozen section to increase diagnostic accuracy.

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Bu Makaleye Atıf Yap

AKÇAY, G., TAŞ, E. E., KILIÇ, G., KESKİN, H. L., YAVUZ, A. F. (2020). Discriminating Borderline Ovarian Tumors from Ovarian Cancer: Focus on Systemic İnflammatory Response Markers. Cyprus Journal of Medical Sciences, Cilt: 5 - Sayı: 1, 36–40. https://ajindex.com/articles/204089

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Kaynakça (13)

  1. Kwon BS, Jeong DH, Byun JM, Lee TH, Choi KU, Song YJ, et al. Prognostic value of preoperative lymphocyte-monocyte ratio in patients with ovarian clear cell carcinoma. J Cancer 2018
  2. 9(7): 1127-34. [CrossRef] 2. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013
  3. 63(1): 11-30 [CrossRef] 3. Nomelini RS, de Carvalho Oliveira LJ, Tavares-Murta BM, Murta EFC. Parameters of blood count and tumor markers: a retrospective analysis and relation to prognostic factors in ovarian cancer. Eur J Gynaecol Oncol 2017
  4. 38(3): 364-7. 4. Mironov S, Akin O, Pandit-Taskar N, Hann LE. Ovarian Cancer. Radiol Clin North Am 2007
  5. : 149-66. [CrossRef] 5. Zhao Z, Xinrui Z, Jingjing L, Xue J, Peishu L, Mao H. Prognostic roles of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in ovarian cancer: a meta-analysis of retrospective studies. Arch of Gynecol Obstet 2018
  6. 297(4): 849-57. [CrossRef] 6. Miao Y, Yan Q, Li S, Li B, Feng Y. Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio are predictive of chemotherapeutic response and prognosis in epithelial ovarian cancer patients treated with platinum-based chemotherapy. Cancer Biomark 2016
  7. 17(1): 33- 40. [CrossRef] 7. Seidman JD, Kurman RJ. Pathology of ovarian carcinoma. Hematol Oncol Clin North Am 2003
  8. 17(4): 909-25. [CrossRef] 8. Skírnisdóttir I, Garmo H, Wilander E, Holmberg L. Borderline ovarian tumors in Sweden 1960-2005: trends in incidence and age at diagnosis compared to ovarian cancer. Int J Cancer 2008
  9. 123(8): 1897- 901. [CrossRef] 9. Ayhan A, Celik H, Taskiran C, Bozdag G, Aksu T, et al. Oncologic and reproductive outcome after fertility-saving surgery in ovarian cancer. Eur J Gynaecol Oncol 2003
  10. 24(3-4): 223-32. 10. Daraï E, Fauvet R, Uzan C, Gouy S, Duvillard P, Morice P, et al. Fertility and borderline ovarian tumor: a systematic review of conservative management, risk of recurrence and alternative options. Hum Reprod Update 2013
  11. 19(2): 151-66. [CrossRef] 11. Suh-Burgmann E. Long-term outcomes following conservative surgery for borderline tumor of the ovary: a large population-based study. Gynecol Oncol 2006
  12. 103(3): 841-7. [CrossRef] 12. Orditura M, Galizia G, Diana A, Saccone C, Cobellis L, Ventriglia J, et al. Neutrophil to lymphocyte ratio (NLR) for prediction of distant metastasis-free survival (DMFS) in early breast cancer: a propensity score-matched analysis. ESMO Open 2016
  13. 1(2): e000038. [CrossRef] 13. Liang HG, Gao K, Jia R, Li J, Wang C. Prognosti