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

作者：Chengjiang Wei, Cheng Yan, Yan Tang, Wei Wang, Yihui Gu, Jie-Yi Ren, Xi-Wei Cui, Xiang Lian, Jin Liu, Huijing Wang, Bin Gu, Tao Zan, Qingfeng Li, Zhichao Wang · 发表于：Frontiers in Oncology · 年份：2020 · DOI：10.3389/fonc.2020.01192 · 被引用次数：15 · 研究领域：Neurofibromatosis and Schwannoma Cases、Meningioma and schwannoma management、Bone Tumor Diagnosis and Treatments

Background: Since 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 6 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 (8 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, resp...