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- W4214651369 abstract "Abstract In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retro-spective observational study analysed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary centre from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realise a tool to predict 12-month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12-month PFS. Our analysis included n. 64 patients. The attribute core set used to train machine learning algorithms included age, menopause, CA125 value, histotype, FIGO stage and US characteristics such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12-month PFS." @default.
- W4214651369 created "2022-03-02" @default.
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- W4214651369 date "2022-02-28" @default.
- W4214651369 modified "2023-09-27" @default.
- W4214651369 title "A Machine Learning Approach Applied to Gynecological Ultrasound to Predict Progression-Free Survival in Ovarian Cancer Patients" @default.
- W4214651369 doi "https://doi.org/10.21203/rs.3.rs-1382403/v1" @default.
- W4214651369 hasPublicationYear "2022" @default.
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