Omics data analysis using deep learning-based framework in differential diagnosis of ovarian cancer
Iurova M.V., Tokareva A.O., Chagovets V.V., Starodutseva N.L., Frankevich V.E.
Relevance: The course of malignant epithelial ovarian tumors is considered to be highly aggressive. Limitations of diagnostic methods are associated with the late detection of tumors at stages III–IV, which is the cause with high mortality.
Objective: To compare the effectiveness of machine learning (ML) methods for minimally invasive diagnosis of early-stage ovarian cancer (OC) using scalable, objective lipid biomarker profile data.
Materials and methods: A single-center observational retrospective cohort clinical study included 239 patients with early-stage high-grade ovarian cancer (HGOC, n=10); with other tumor/proliferative processes (n=203, of which: including 30 cystadenomas, 59 endometrioid cysts, 21 teratomas, 28 borderline tumors; 16 – low-grade ovarian cancer (LSOC), HGOC of III-IV stages and control group women (n=26). Lipid extraction, analysis by high-performance liquid chromatography coupled with electrospray ionization mass spectrometry, and data preprocessing were performed. The SHAP method was used to interpret the predictions generated by building complex models. For multi-class classification, 7 ML methods were tested, including Naive Bayes classification, PLS discriminant analysis, Random Forest, External Gradient Boosting classification, Multilayer Percepton, and Convolutional Network. For binary classification, the following were additionally tested: support vector machine and extreme gradient boosting (Xgboos) classifications.
Results: In Stages I–II HGOC, a decrease in PC O-18:1/18:0, PE P-18:0/18:2, LPC O-16:0, PC 18:0_18:2, OxTG 16:0_18:1_16:1(CHO), OxPC 18:2_16:1(COOH), OxPC 20:4_14:0(COOH) and an increase in PC 16:0_18:0, PC P-18:1/20:4, PC 18:1_18:2, PC 16:0_18:0, PC 18:2_18:2 (compared to the control group) occurred, as well as a decrease in Cer-NS d18:1/22:0, PC P-16:0/18:1, PC P-18:1/20:4, PC P-18:0/18:1, oxidized lipids, carboxy- and carbohydroxy-derivatized and an increase in PC P-18:0/18:2, PC P-20:0/20:4 (compared to patients with OC). The best differentiation ability between the control group and the OC group was demonstrated by OPLS models, as well as random forest, and support vector machine with a radial kernel (90%).
Conclusion: The use of advanced ML methods strengthens the diagnostic potential of omics data and can be applied in gynecological oncology.
Authors’ contributions: Iurova M.V. – study concept, material collection, obtaining funding, project administration, data verification, resource search, supervision, manuscript conduction, literature review, initial draft and editing; Tokareva A.O. – data control, methodology, providing software, resource search, manuscript conduction and editing; Chagovets V.V. – formal analysis, methodology, software providing; Starodubtseva N.L. – methodology, formal analysis, study concept, supervision, manuscript conduction and editing; Frankevich V.E. – formal analysis, supervision, manuscript conduction, review and editing.
Conflicts of interest: The authors declare that the study was conducted in the absence of any commercial or financial relationship that could be interpreted as a potential conflict of interest.
Funding: The study was carried out as part of the Russian Science Foundation (RSF) Grant by agreement dated December 29, 2023 No. 24-25-00407 on the "New approaches to the application of artificial intelligence for the differential diagnosis of benign and malignant ovarian tumors based on the features of the blood metabolome detected using physical methods".
Ethical Approval: The study was approved by the Commission on the Ethics of Biomedical Research of the Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology (Protocol No.10 dated December 5, 2019). The study was initiated after the approval and was carried out in accordance with the Federal Law of the Russian Federation dated July 27, 2006 No.152-FZ (as amended on July 29, 2017) "On Personal Data," the Federal Law of the Russian Federation dated November 21, 2011 No.323-FZ "On the Basics of Health Protection in the Russian Federation" (Article 13 "Confidentiality"), the provisions of the Helsinki Declaration with all subsequent additions and amendments regulating scientific research on human biomaterials, as well as the International ethical guidelines for biomedical research involving human subjects of the Council of International Organizations of Medical Sciences (CIOMS).
Patient Consent for Publication: All patients provided informed consent for the publication of their data.
Authors' Data Sharing Statement: The data supporting the findings of this study are available on request from the corresponding author after approval from the principal investigator.
For citation: Iurova M.V., Tokareva A.O., Chagovets V.V., Starodutseva N.L., Frankevich V.E.
Omics data analysis using deep learning-based framework in differential diagnosis of ovarian cancer.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (10): 117-127 (in Russian)
https://dx.doi.org/10.18565/aig.2025.222
Keywords
artificial intelligence
lipidome
machine learning
metabolome
ovarian tumor
ovarian cancer
References
- Feng Y., Yang W., Zhu J., Wang S., Wu N., Zhao H. et al. Clinical utility of various liquid biopsy samples for the early detection of ovarian cancer: a comprehensive review. Front. Oncol. 2025; 15: 1594100. https://dx.doi.org/10.3389/fonc.2025.1594100
- Cancer Research UK. Health inequalities: breaking down barriers to cancer screening. Available at: https://news.cancerresearchuk.org/2022/09/23/health-inequalities-breaking-down-barriers-to-cancer-screening/ (accessed on August 13, 2025)
- Mikami M., Tanabe K., Imanishi T., Ikeda M., Hirasawa T., Yasaka M. et al. Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer. Sci. Rep. 2024; 14(1): 20000. https://dx.doi.org/10.1038/s41598-024-70228-6
- Юрова М.В., Токарева А.О., Чаговец В.В., Стародубцева Н.Л., Франкевич В.Е. Дифференциальная диагностика злокачественных новообразований яичников на ранней стадии на основании биоинформационного исследования метаболома крови. Акушерство и гинекология. 2024; 12: 118-26. [Iurova M.V., Tokareva A.O., Chagovets V.V., Starodubtseva N.L., Frankevich V.E. Differential diagnosis of early-stage ovarian cancer based on the bioinformatic analysis of the blood metabolome. Obstetrics and Gynecology. 2024; (12): 118-26 (in Russian)]. https://dx.doi.org/10.18565/aig.2024.283
- Tokareva A., Iurova M., Starodubtseva N., Chagovets V., Novoselova A., Kukaev E. et al. Machine learning framework for ovarian cancer diagnostics using plasma lipidomics and metabolomics. Int. J. Mol. Sci. 2025; 26(14): 6630. https://dx.doi.org/10.3390/ijms26146630
- Iurova M.V., Chagovets V.V., Pavlovich S.V., Starodubtseva N.L., Khabas G.N., Chingin K.S. et al. Lipid alterations in early-stage high-grade serous ovarian cancer. Front. Mol. Biosci. 2022; 9: 770983. https://dx.doi.org/10.3389/fmolb.2022.770983
- Prat J.; FIGO Committee on Gynecologic Oncology. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int. J. Gynaecol. Obstet. 2014; 124(1): 1-5. https://dx.doi.org/10.1016/j.ijgo.2013.10.001
- Liang D., Yi B., Cao W., Zheng Q. Exploring ensemble oversampling method for imbalanced keyword extraction learning in policy text based on three-way decisions and SMOTE. Expert Systems with Applications. 2022; 188(1): 116051. https://dx.doi.org/10.1016/j.eswa.2021.116051
- Lundberg S.M., Erion G., Chen H., DeGrave A., Prutkin J.M., Nair B. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2020; 2(1): 56-67. https://dx.doi.org/10.1038/s42256-019-0138-9
- Юрова М.В., Франкевич В.Е., Павлович С.В., Чаговец В.В., Стародубцева Н.Л., Хабас Г.Н., Ашрафян Л.А., Сухих Г.Т. Диагностика серозного рака яичников высокой степени злокачественности Iа–Iс стадии по липидному профилю сыворотки крови. Гинекология. 2021; 23(4): 335-40. [Iurova M.V., Frankevich V.E., Pavlovich S.V., Chagovets V.V., Starodubtseva N.L., Khabas G.N., Ashrafyan L.A., Sukhikh G.T. Diagnosis of Ia–Ic stages of serous highgrade ovarian cancer by the lipid profile of blood serum. Gynecology. 2021; 23(4): 335-40 (in Russian)]. https://dx.doi.org/10.26442/20795696.2021.4.200911
- Sharma A., Vans E., Shigemizu D., Boroevich K.A., Tsunoda T. DeepInsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci. Rep. 2019; 9(1): 11399. https://dx.doi.org/10.1038/s41598-019-47765-6
- Fan L., Yin M., Ke C., Ge T., Zhang G., Zhang W. et al. Use of plasma metabolomics to identify diagnostic biomarkers for early stage epithelial ovarian cancer. J. Cancer. 2016; 7(10): 1265-72. https://dx.doi.org/10.7150/jca.15074
- Li J., Wang Z., Liu W., Tan L., Yu Y., Liu D. et al. Identification of metabolic biomarkers for diagnosis of epithelial ovarian cancer using internal extraction electrospray ionization mass spectrometry (iEESI-MS). Cancer Biomark. 2023; 37(2): 67-84. https://dx.doi.org/10.3233/CBM-220250
- Garcia E., Andrews C., Hua J., Kim H.L., Sukumaran D.K., Szyperski T. et al. Diagnosis of early stage ovarian cancer by 1H NMR metabonomics of serum explored by use of a microflow NMR probe. J. Proteome Res. 2011; 10(4):1765-71. https://dx.doi.org/10.1021/pr101050d
- Ke C., Hou Y., Zhang H., Fan L., Ge T., Guo B. et al. Large-scale profiling of metabolic dysregulation in ovarian cancer. Int. J. Cancer. 2015; 136(3): 516-26. https://dx.doi.org/10.1002/ijc.29010
- Chistyakov D.V., Guryleva M.V., Stepanova E.S., Makarenkova L.M., Ptitsyna E.V., Goriainov S.V. et al. Multi-omics approach points to the importance of oxylipins metabolism in early-stage breast cancer. Cancers. 2022; 14(8): 2041. https://dx.doi.org/10.3390/cancers14082041
- Gaul D.A., Mezencev R., Long T.Q., Jones C.M., Benigno B.B., Gray A. et al. Highly-accurate metabolomic detection of early-stage ovarian cancer. Sci. Rep. 2015; 5: 16351. https://dx.doi.org/10.1038/srep16351
- Ban D., Housley S.N., Matyunina L.V., McDonald L.D., Bae-Jump V.L., Benigno B.B. et al. A personalized probabilistic approach to ovarian cancer diagnostics. Gynecol. Oncol. 2024; 182: 168-75. https://dx.doi.org/10.1016/j.ygyno.2023.12.030
- Gaillard D.H.K., Lof P., Sistermans E.A., Mokveld T., Horlings H.M., Mom C.H. et al. Evaluating the effectiveness of pre-operative diagnosis of ovarian cancer using minimally invasive liquid biopsies by combining serum human epididymis protein 4 and cell-free DNA in patients with an ovarian mass. Int. J. Gynecol. Cancer. 2024; 34(5): 713-21. https://dx.doi.org/10.1136/ijgc-2023-005073
Received 19.08.2025
Accepted 23.10.2025
About the Authors
Mariia V. Iurova, PhD, obstetrician-gynecologist, oncologist, Senior Researcher at the Scientific Polyclinic Department, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, hi5melisa@gmail.com, https://orcid.org/0000-0002-0179-7635Alisa O. Tokareva, PhD (Physico-Mathematical Sciences), Specialist at the Laboratory of Clinical Proteomics, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, +7(495)531-44-44 (ext. 3113), alisa.tokareva@phystech.edu, https://orcid.org/0000-0001-5918-9045
Vitaliy V. Chagovets, PhD (Physico-Mathematical Sciences), Head of the Laboratory of Metabolomics and Bioinformatics, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, +7(495)438-21-98, vvchagovets@gmail.com
Natalia L. Starodubtseva, PhD (Bio), Head of the Laboratory of Clinical Proteomics, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow,
Ac. Oparin str., 4, n_starodubtseva@oparina4.ru, https://orcid.org/0000-0001-6650-5915
Vladimir E. Frankevich, Dr. Sci. (Physico-Mathematical Sciences), Deputy Director of the Institute of Translational Medicine, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, v_frankevich@oparina4.ru
Corresponding author: Mariia V. Iurova, hi5melisa@gmail.com
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