Assessing the risk of ovarian cancer relapse with special software: a clinical case

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Abstract

The article presents a clinical observation of a patient with ovarian cancer, stage IIIA according to FIGO (International Federation of Obstetrics and Gynecology), after completing the first-line combination therapy for whom we determined the risk of recurrence using a special software.

The early prediction of the ovarian cancer relapse was based on calculated ARRNO index (Assessment of Risk of Relapse of Neoplasm of Ovary). As initial data the following characteristics were inserted into the program: disease stage according to FIGO, tumor differentiation stage (Grade), hystotype, state of residual tissue on ultrasound examination after the treatment, levels of СА-125 before the treatment, levels of НЕ-4 after the treatment. The ARRNO software calculated the individual risk of relapse in 3 limits: low (0 - 0,39), moderate (0,40 - 0,85) and high (0,86 - 1,0).

Conclusion. The special software for assessing the risk of relapse of ovarian neoplasm proved to be simple to operate and allowed to predict the relapse with high probability.

About the authors

Ilgiz G. Gataullin

Kazan State Medical Academy

Email: ilgizg@list.ru
ORCID iD: 0000-0001-5115-6388

PhD, Professor of the Department of Oncology, radiology and palliative care

Russian Federation, Kazan

Aigul R. Savinova

Tatarstan Regional Clinical Cancer Center

Author for correspondence.
Email: aigulkazan@mail.ru
ORCID iD: 0000-0001-7048-4125

oncologist of the Department of Oncology №10

Russian Federation, Kazan

References

  1. Liest AL, Omran AS, Mikiver R, et al. RMI and ROMA are equally effective in discriminating between benign and malignant gynecological tumors: A prospective population-based study. Acta Obstetricia et Gynecologica Scandinavica. 2019;1(98):24–33. doi: 10.1111/aogs.13462
  2. Ionescu CA, Matei A, Navolan D, et al. Correlation of ultrasound features and the Risk of Ovarian Malignancy Algorithm score for different histopathological subtypes of benign adnexal masses. Medicine (Baltimore). 2018;97(31):e11762. doi: 10.1097/MD.0000000000011762
  3. Salim E, Zubairi AM, Danish SH, Ali U. Diagnostic Accuracy of Risk of Ovarian Malignancy Algorithm (ROMA) in Post-Menopausal Patients with Ovarian Mass. J Coll Physicians Surg Pak. 2018;28(6):440–444. doi: 10.29271/jcpsp.2018.06.44
  4. Calester B, Van Hoorde K, Froyman W, et al. Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors. Facts Views Vis Obgyn. 2015;7(1):32–41. PMID: 25897370
  5. Schneider S, Armbrust R, Spies C, et al. Prehabilitation programs and ERAS protocols in gynecological oncology: a comprehensive review. Arch Gynecol Obstet. 2020;301(2):315–326. doi: 10.1007/s00404-019-05321-7
  6. Devlin MJ, Ledermann JA, Lockley M, et al. 975P Clear cell ovarian cancer (CCOC): Predicting risk of relapse (ROR). Annals of Oncology. 2018;29(8)։7–12. doi: 10.1093/annonc/mdy285.182
  7. Narasimhulu DM, Kumar A, Weaver AL, et al. Using an evidence-based triage algorithm to reduce 90-day mortality after primary debulking surgery for advanced epithelial ovarian cancer. Gynecol Oncol. 2019;155(1):58–62. doi: 10.1016/j.ygyno.2019.08.004
  8. Savinova AR, Gataullin IG, Shakirov RR. Method of assessment of the risk of relapse of ovarian cancer. Patent of Russian Federation No. 2251692, published on 10.05.2015, Bulletin No. 13. (In Russ.). [Савинова А.Р., Гатауллин И.Г., Шакиров Р.Р. Способ оценки риска рецидивирования рака яичников. Патент Российской Федерации № 2251692, опубликованный 10.05.2015, бюллетень № 13].
  9. Savinova AR, Gataullin IG. Relapse of ovarian neoplasm: individual risk assessment algorithm. Science and Innovations in Medicine. 2019;4(3):65–68. (In Russ.). [Савинова А.Р., Гатауллин И.Г. Алгоритм оценки индивидуального риска рецидивирования рака яичников. Наука и инновации в медицине. 2019;4(3):65–68]. doi: 10.35693/2500-1388-2019-4-3-65-68
  10. Savinova AR, Gataullin IG, Shakirov RR, Yarovaya EYu. Computer program for the realization of the algorithm of risk of relapse of ovarian neoplasm. Certificate of the State Registration of the Computer program № 2017619452 from 24.08.2017;1–12. (In Russ.). [Савинова А.Р., Гатауллин И.Г., Шакиров Р.Р., Яровая Е.Ю. Программа для реализации алгоритма оценки индивидуального риска рецидивирования рака яичников. Свидетельство о государственной регистрации программы для ЭВМ № 2017619452 от 24.08.2017 г.;1–12].

Supplementary files

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2. Figure 1. An ultrasound image of a patient with suspected cancer of the right ovary.

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3. Figure 2. Calculation of the ARRNO index in a patient with ovarian cancer after completion of the first-line combination therapy.

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4. Figure 3 (A–F). Step-by-step process of entering risk factors data into the ARRNO software.

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Copyright (c) 2021 Gataullin I.G., Savinova A.R.

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