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Journals / Balıkesir Sağlık Bilimleri Dergisi / 2026 / Volume: 15 Issue: 3

Diagnostic Performance of Machine Learning Models Based on ADC Maps in Parotid Gland Tumors

Emrah Akay(BALIKESIR UNIVERSITY, SCHOOL OF MEDICINE)0000-0002-8586-3571
Sercan Tosun(Clevland Clinic)0000-0002-5988-7086
Gülen Demirpolat(BALIKESİR ÜNİVERSİTESİ)0000-0002-9639-2672
Bahar Yanik Keyik(BALIKESİR ÜNİVERSİTESİ)0000-0003-3358-7330
Erdoğan Bülbül(BALIKESİR ÜNİVERSİTESİ)0000-0002-1136-9407
Pages
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Publication Date
2026-10-08
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

ADC-based radiomics and machine learning may provide noninvasive diagnostic support; however, these methods should be considered adjuncts to, rather than replacements for, histopathological diagnosis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: This study aims to evaluate the diagnostic performance of radiomics features extracted from ADC maps for differentiating benign from malignant parotid tumors using machine learning models, demonstrating their potential as supplementary diagnostic tools.
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Key Findings & Data:Materials and Methods: Forty-six patients who underwent diffusion-weighted imaging (DWI) between January 2018 and December 2022 and had histopathologically confirmed parotid tumors were retrospectively analyzed.
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Conclusion & Impact:ADC-based radiomics and machine learning may provide noninvasive diagnostic support; however, these methods should be considered adjuncts to, rather than replacements for, histopathological diagnosis.
🔬Design:Modeling & Simulation
📍Scope:2018 Verileri
⚙️Variables:Cancer of Head and Neck · Parotid Cancer · Supervised Machine Learning

Abstract

Objective: This study aims to evaluate the diagnostic performance of radiomics features extracted from ADC maps for differentiating benign from malignant parotid tumors using machine learning models, demonstrating their potential as supplementary diagnostic tools. Materials and Methods: Forty-six patients who underwent diffusion-weighted imaging (DWI) between January 2018 and December 2022 and had histopathologically confirmed parotid tumors were retrospectively analyzed. Patients were classified as pleomorphic adenoma (n=15), Warthin tumor (n=19), and malignant tumor (n=12). Lesions were manually segmented on ADC maps to construct a radiomics dataset, and various regression methods and ML algorithms were applied for both benign–malignant differentiation and subtype classification. Results: Stable radiomics features selected by the Fast Correlation-Based Filter (FCBF) method were used to assess the performance of multiple ML algorithms. The Artificial Neural Network (ANN) achieved the highest accuracy in distinguishing benign from malignant tumors, demonstrating the potential of ML models like Neural Networks and SVM to support diagnostic decisions. Conclusion: The ANN algorithm showed promising performance in differentiating parotid tumor groups. ADC-based radiomics and machine learning may provide noninvasive diagnostic support; however, these methods should be considered adjuncts to, rather than replacements for, histopathological diagnosis.

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Cite This Article

Akay, E., Tosun, S., Demirpolat, G., Keyik, B. Y., Bülbül, E. (2026). Diagnostic Performance of Machine Learning Models Based on ADC Maps in Parotid Gland Tumors. Balıkesir Sağlık Bilimleri Dergisi, Volume: 15 Issue: 3. https://doi.org/10.53424/balikesirsbd.1816011

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