Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from 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 using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split 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 curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.
恶性外周神经鞘瘤（MPNSTs）是神经纤维瘤病1型（NF1）患者中常见的侵袭性软组织肿瘤，具有较高的转移和复发风险。目前磁共振成像（MRI）在区分良性外周神经鞘瘤（BPNSTs）与MPNSTs方面缺乏决定性，仍需依赖侵入性活检。本研究旨在利用定量影像特征和机器学习构建影像组学模型，以区分MPNSTs与BPNSTs。研究收集了一家三级肉瘤转诊中心2000年至2019年间MPNST和BPNST患者的临床数据及MRI影像。通过人工与半自动方式对MRI扫描图像进行病灶分割，并采用基于自动化机器学习的“最优影像组学分类工作流”（WORC）算法提取影像组学特征，通过100次随机分割交叉验证进行评估。研究共纳入35例MPNSTs和74例BPNSTs。T1加权（T1w）MRI影像组学模型表现最优，曲线下面积（AUC）达0.71；增加其他MRI序列并未提升模型性能。结合T1w MRI与临床特征后AUC提升至0.74。经验丰富的放射科医师判读AUC分别为0.75和0.66。基于T1w MRI影像组学特征结合临床数据的方法显示出一定的MPNSTs与BPNSTs鉴别能力，可能为这类肿瘤的临床管理提供辅助。
Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics
肿瘤(癌症)患者之家