摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据NewsRx记者在荷兰鹿特丹的新闻报道，研究表明：“恶性周围神经鞘瘤S(MPNSTs)是一种侵袭性的软组织肿瘤，在1型神经纤维瘤病(NF1)患者中普遍存在，具有明显的转移和复发风险。目前磁共振成像(MRI)成像在鉴别良性周围神经鞘瘤(BPNSTs)和mpnst方面缺乏决定性，需要进行侵入性活检。”新闻记者引用了伊拉斯谟大学医学中心癌症研究所的一句话：“这项研究旨在开发一个放射组学模型，结合定量成像特征和机器学习来区分MPNST与BPNST。MPNST和BPNST患者的临床数据和MRI(2000-2019)在一个三级肉瘤转诊中心收集。MRI扫描上对病灶进行手动和半自动分割。”采用自动机器学习的最优放射组学分类(WORC)算法提取放射组学特征，采用100 x随机交叉验证进行评价，共包括35个MPNST和74个BPNST。T1Weighted(T1w)MRI放射组学模型优于其他模型，CURVE(AUC)以下区域的MRI扫描没有提高性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting from Rotterdam, Netherlands, by NewsRx journalists, research stated, "Malignant peripheral nerve sheath tumor s (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Curre nt magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasi ve biopsies." The news journalists obtained a quote from the research from Erasmus University Medical Center Cancer Institute: "This study aims to develop a radiomics model u sing quantitative imaging features and machine learning to distinguish MPNSTs fr om BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi -automatically segmented on MRI scans, and radiomics features were extracted usi ng the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100 x random-s plit cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1- weighted (T1w) MRI radiomics model outperformed others with an area under the cu rve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance per formance."
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