Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model

Ozturk E. M. A., Unsal G., Erisir F., ORHAN K.

EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY, cilt.281, sa.12, ss.6585-6597, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 281 Sayı: 12
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00405-024-08862-z
  • Dergi Adı: EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database
  • Sayfa Sayıları: ss.6585-6597
  • Anahtar Kelimeler: Bone invasion, Machine learning, Magnetic resonance imaging, Oral squamous cell carcinoma
  • Ankara Üniversitesi Adresli: Evet

Özet

ObjectivesRadiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI).Materials-methodsMRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds.ResultsThe results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy.ConclusionsThis study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings.