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Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study
Aslan, Özge; Oktay, Aysenur; Katuk, Basak; Erdur, Riza Cenk; Dikenelli, Oguz; Yeniay, Levent; Zekioglu, Osman; Özbek, Süha Süreyya.
Afiliação
  • Aslan Ö; Department of Radiology, Ege University Faculty of Medicine, Izmir, Turkey
  • Oktay A; Department of Radiology, Ege University Faculty of Medicine, Izmir, Turkey
  • Katuk B; Department of Computer Engineering, Ege University, Izmir, Turkey
  • Erdur RC; Department of Computer Engineering, Ege University, Izmir, Turkey
  • Dikenelli O; Department of Computer Engineering, Ege University, Izmir, Turkey
  • Yeniay L; Department of General Surgery, Ege University Faculty of Medicine, Izmir, Turkey
  • Zekioglu O; Department of Medical Pathology, Ege University Faculty of Medicine, Izmir, Turkey
  • Özbek SS; Department of Radiology, Ege University Faculty of Medicine, Izmir, Turkey
Diagn Interv Radiol ; 29(2): 260-267, 2023 03 29.
Article em En | MEDLINE | ID: mdl-36987868
ABSTRACT

PURPOSE:

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy.

METHODS:

A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed.

RESULTS:

Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant.

CONCLUSION:

Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Inteligência Artificial Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Revista: Diagn Interv Radiol Assunto da revista: DIAGNOSTICO POR IMAGEM / RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Turquia

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Inteligência Artificial Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Revista: Diagn Interv Radiol Assunto da revista: DIAGNOSTICO POR IMAGEM / RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Turquia