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Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.
Öztürk, Elif Meltem Aslan; Ünsal, Gürkan; Erisir, Ferhat; Orhan, Kaan.
Afiliación
  • Öztürk EMA; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Lokman Hekim University, Ankara, Turkey. aslan.meltem5@gmail.com.
  • Ünsal G; Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  • Erisir F; Department of Otorhinolaryngology and Head and Neck Surgery, Faculty of Medicine, Near East University, Kyrenia, Cyprus.
  • Orhan K; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey.
Eur Arch Otorhinolaryngol ; 2024 Jul 31.
Article en En | MEDLINE | ID: mdl-39083062
ABSTRACT

OBJECTIVES:

Radiomics, 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-

METHODS:

MRI 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 82 ratio using 815 random seeds.

RESULTS:

The 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.

CONCLUSIONS:

This 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur Arch Otorhinolaryngol Asunto de la revista: OTORRINOLARINGOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur Arch Otorhinolaryngol Asunto de la revista: OTORRINOLARINGOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Alemania