Data_Sheet_1_Computed Tomography–Based Differentiation of Benign and Malignant Craniofacial Lesions in Neurofibromatosis Type I Patients: A Machine Le.docx (16.45 kB)

Data_Sheet_1_Computed Tomography–Based Differentiation of Benign and Malignant Craniofacial Lesions in Neurofibromatosis Type I Patients: A Machine Learning Approach.docx

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posted on 31.07.2020, 09:37 by Cheng-Jiang Wei, Cheng Yan, Yan Tang, Wei Wang, Yi-Hui Gu, Jie-Yi Ren, Xi-Wei Cui, Xiang Lian, Jin Liu, Hui-Jing Wang, Bin Gu, Tao Zan, Qing-Feng Li, Zhi-Chao Wang

Background: Because neurofibromatosis type I (NF1) is a cancer predisposition disease, it is important to distinguish between benign and malignant lesions, especially in the craniofacial area.

Purpose: The purpose of this study is to improve effectiveness in the diagnostic performance in discriminating malignant from benign craniofacial lesions based on computed tomography (CT) using a Keras-based machine-learning model.

Methods: The Keras-based machine learning technique, a neural network package in the Python language, was used to train the diagnostic model on CT datasets. Fifty NF1 patients with benign craniofacial neurofibromas and six NF1 patients with malignant peripheral nerve sheath tumors (MPNSTs) were selected as the training set. Three validation cohorts were used: validation cohort 1 (random selection of 90% of the patients in the training cohort), validation cohort 2 (an independent cohort of 9 NF1 patients with benign craniofacial neurofibromas and 11 NF1 patients with MPNST), and validation cohort 3 (eight NF1 patients with MPNST, not restricted to the craniofacial area). Sensitivity and specificity were tested using validation cohorts 1 and 2, and generalizability was evaluated using validation cohort 3.

Results: A total of 59 NF1 patients with benign neurofibroma and 23 NF1 patients with MPNST were included. A Keras-based machine-learning model was successfully established using the training cohort. The accuracy was 96.99 and 100% in validation cohorts 1 and 2, respectively, discriminating NF1-related benign and malignant craniofacial lesions. However, the accuracy of this model was significantly reduced to 51.72% in the identification of MPNSTs in different body regions.

Conclusion: The Keras-based machine learning technique showed the potential of robust diagnostic performance in the differentiation of craniofacial MPNSTs and benign neurofibromas in NF1 patients using CT images. However, the model has limited generalizability when applied to other body areas. With more clinical data accumulating in the model, this system may support clinical doctors in the primary screening of true MPNSTs from benign lesions in NF1 patients.

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