Relapse of ovarian neoplasm: individual risk assessment algorithm


Objectives – to develop an algorithm for assessing the individual risk of ovarian cancer recurrence.  

Material and methods. A retrospective analysis of the results of treatment of patients with ovarian cancer in the period 2010–2015 was carried out. Finally, data of 1103 patients was reinvestigated, ovarian cancer relapse was registered in 907 patients (mean age: 58,7±12 years; interquartile range: 50–68 years). 196 patients with ovarian cancer did not have relapse for the mentioned time period (mean age: 63,1±13,6 years; interquartile range: 53–74 years). In the first stage of investigation, a unifactorial analysis of prognostic factors of ovarian cancer relapse was carried out. In the second stage, the most significant factors were analyzed with the aid of binary regression. As a result, a final formula of the assessment of individual risk of ovarian cancer relapse was developed, which we have named ARRNO (Algorithm of the Assessment of Risk of Relapse of Neoplasm of Ovary). 

Results. From 12 prognostic factors, we selected 6 ones with the aid of binary regression: stage, hystotype, tumor differentiation grade, results of post-chemotherapy ultrasound examination, CA 125 pre-treatment levels, HE4 post-treatment levels. The final ARRNO score was developed on the basis of binary regression formula. Depending on the value of ARRNO score the risk could be divided into low (0.00–0.39), moderate (0.40–0.85) and high (0.86–1.00).

Conclusion. The algorithm of the risk assessment of recurrence of ovarian cancer has a high sensitivity and specificity and allows for stratification of patients into groups of high, moderate and low risk. Integration of the ARRNO in the follow-up plan of ovarian cancer patients, after accomplishment of the first-line therapy, could enhance the treatment planning and timely prevention.

Conflict of Interest: nothing to disclose.

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Для цитирования

Savinova AR, Gataullin IG. Relapse of ovarian neoplasm: individual risk assessment algorithm. Science & Innovations inMedicine. 2019;4(3):65-68. doi: 10.35693/2500-1388-2019-4-3-65-68


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