Machine learning-based analysis of MRI radiomics in the discrimination of classical and non-classical polycystic over syndrome

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RONA G., Zengin Fıstıkçıoğlu N., Tekin A., Arifoglu M., Duzkalir H. G., EVRİMLER Ş., ...Daha Fazla

Cukurova Medical Journal, cilt.49, sa.1, ss.89-96, 2024 (ESCI, TRDizin)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 49 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.17826/cumj.1393084
  • Dergi Adı: Cukurova Medical Journal
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Academic Search Premier, Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.89-96
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Süleyman Demirel Üniversitesi Adresli: Evet

Özet

Purpose: The aim of this study is to investigate the value of radiomics analysis on T2-weighted Magnetic Resonance imaging (MRI) images in differentiating classical and non-classical polycystic ovary syndrome (PCOS). Materials and Methods: A total of 202 ovaries from 101 PCOS patients (mean age of 23±4 years) who underwent pelvic MRI between 2014 and 2022, were included in the study. Of the patients, 53 (52.5%) were phenotype A, 12 (11.9%) were phenotype B, 25 were phenotype C (25.1%), and 11 were phenotype D (10.9%). 130 (64.4%) of the ovaries were classical PCOS, 72 (35.6%) were non-classical PCOS. The ovaries were manually segmented in all axial sections using the 3D Slicer program. A total of 851 features were extracted. Python 2.3, Pycaret library was used for machine learning (ML) analysis. Datasets were randomly divided into train (70 %, 141) and test (30 %, 61) datasets. The performances of ML algorithms were compared with AUC, accuracy, recall, precision and F1 scores. Results: Accuracy and AUC values in the training set ranged from 57%-73% and 0.50-0.73, respectively. The two best ML algorithms were Random Forest (rf) (AUC:0.73, accuracy:73%) and Gradient Boosting Classifier (gbc) (AUC:0.71, accuracy:70%). AUC, accuracy, recall and precision values and F1 score of the blend model obtained from these two models were 0.70, 73 %, 56 %, 66%, 58%, respectively. Conclusion: Radiomic features obtained from T2W MRI are successful in distinguishing between classical and non-classical PCOS.