The immune system plays an essential role in the development of tumors, which has been demonstrated in multiple types of cancers. Consistent with this, immunotherapies with targets that disrupt these mechanisms and turn the immune system against developing cancers have been proven effective. In neurofibromatosis type 1 (NF1), an autosomal dominant genetic disorder, the understanding of the complex interactions of the immune system is incomplete despite the discovery of the pivotal role of immune cells in the tumor microenvironment. Individuals with NF1 show a loss of the NF1 gene in nonneoplastic cells, including immune cells, and the aberrant immune system exhibits intriguing interactions with NF1. This review aims to provide an update on recent studies showing the bilateral influences of NF1 mutations on immune cells and how the abnormal immune system promotes the development of NF1 and NF1-related tumors. We then discuss the immune receptors major histocompatibility complex class I and II and the PD-L1 mechanism that shield NF1 from immunosurveillance and enable the immune escape of tumor tissues. Clarification of the latest understanding of the mechanisms underlying the effects of the abnormal immune system on promoting the development of NF1 will indicate potential future directions for further studies and new immunotherapies.
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. Wei et al. AI Assists CT-Based NF1 Diagnosis 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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