Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients

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dc.contributor.authorSenturk, Tugba
dc.contributor.authorLatifoglu, Fatma
dc.contributor.authorAltintop, Cigdem Guluzar
dc.contributor.authorYay, Arzu
dc.contributor.authorGonen, Zeynep Burcin
dc.contributor.authorOnder, Gozde Ozge
dc.contributor.authorMat, Ozge Cengiz
dc.date.accessioned2026-04-04T13:31:14Z
dc.date.available2026-04-04T13:31:14Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractCisplatin, a widely used chemotherapeutic agent, is highly effective in treating various cancers, including ovarian and lung cancers, but it often causes ovarian tissue damage and impairs reproductive health. Exosomes derived from mesenchymal stem cells are believed to possess reparative effects on such damage, as suggested by previous studies. This study aims to evaluate the reparative effects of cisplatin and exosome treatments on ovarian tissue damage through the analysis of histopathological images and machine learning (ML)-based classification techniques. Five experimental groups were examined: Control, cisplatin-treated (Cis), exosome-treated (Exo), exosome-before-cisplatin (ExoCis), and cisplatin-before-exosome (CisExo). A set of 177 Local Binary Pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). The CisExo group exhibited the most homogeneous texture, suggesting effective tissue recovery, whereas the ExoCis group demonstrated greater heterogeneity, possibly indicating incomplete recovery. KNN and ANN classifiers achieved the highest accuracy, particularly in comparisons between the Control and CisExo groups, reaching an accuracy of 87%. The highest classification accuracy was observed for the Control vs. Cis groups (approximately 91%), reflecting distinct features, whereas the Control vs. Exo groups demonstrated lower accuracy (around 68%) due to feature similarity. Exosome treatments, particularly when administered post-cisplatin, significantly improve ovarian tissue recovery. This study highlights the potential of ML-based classification as a robust tool for evaluating therapeutic outcomes. Additionally, it underscores the promise of exosome therapy in mitigating chemotherapy-induced ovarian damage and preserving reproductive health. Further research is warranted to validate these findings and optimize treatment protocols.
dc.description.sponsorshipErciyes University Scientific Research Projects Unit
dc.description.sponsorshipThis study was supported by the Erciyes University Scientific Research Projects Unit under the project number TSG-2021-11527. The authors sincerely thank the Erciyes University Scientific Research Projects Unit for their financial support and encouragement throughout this research.
dc.identifier.doi10.3390/app15041984
dc.identifier.issn2076-3417
dc.identifier.issue4
dc.identifier.orcid0000-0002-0515-9286
dc.identifier.orcid0000-0001-8632-3385
dc.identifier.orcid0000-0003-2725-9330
dc.identifier.orcid0000-0002-1323-5752
dc.identifier.orcid0000-0002-4212-5763
dc.identifier.scopus2-s2.0-85218440116
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app15041984
dc.identifier.urihttps://hdl.handle.net/11616/108666
dc.identifier.volume15
dc.identifier.wosWOS:001429978800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectcisplatin
dc.subjectexosome
dc.subjectlocal binary pattern
dc.subjectk-nearest neighbors
dc.subjectsupport vector machine
dc.subjectartificial neural network
dc.subjectdecision tree
dc.titleHistopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients
dc.typeArticle

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