The Investigation of Radiation-Induced Optic Neuropathy Following Radiotherapy of Head and Neck Tumors Using Visual Evoked Potential and Ct Scan Images; A Machine Learning Approach

سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 106

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شناسه ملی سند علمی:

RSACONG02_038

تاریخ نمایه سازی: 20 مهر 1401

چکیده مقاله:

Introduction: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy (RION) in patients who had treated brain and head and neck cancers with radiation or chemo radiation therapy.Methods: The visual evoked potential values were obtained in the case (۵۲ patients) and control (۵۲ patients) groups. The radiomics features were extracted from the segmented area including right and left nerve optic and optic chiasm using ۳D-Slicer software. We implemented ۵-fold cross-validation to evaluate ۵ supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on ۴ input datasets to predict radiation induced visual complications. The F۱ score, accuracy, sensitivity, specificity, and area under the ROC curve were the evaluation criteria.Results: RION affected ۳۱% of the patients. ۹۰۵ radiomic characteristics were extracted from each segmented area. Gradient Boosting Decision Trees was the most powerful algorithm to predict RION and had the highest AUC among the five classifiers with AUC ≥ ۹۸%. Chiasm dataset can predict RION better than right or left nerve optic or combination of features from all radiomics datasets.Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict RION after radiation treatment with high accuracy. To acquire more reliable results, it is suggested VEP is conducted before and after radiation therapy, with multiple follow-up courses, more additional optometric tests, and more patients.

نویسندگان

Elham Raiesi Nafchi

Department of Radiation Sciences, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran

Pedram Fadavi

Department of Radiation Oncology, School of Medicine, Iran University of Medical Science, Tehran, Iran

Sepideh Amiri

Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

Susan Cheraghi

Department of Radiation Sciences, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran .Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran

Maryam Garousi

Department of Radiation Oncology, School of Medicine, Iran University of Medical Science, Tehran, Iran

Mansoureh Nabavi

Radiation Oncology Research Center (RORC), Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran