Background An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task.

Objectives The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility.

Methods In this paper, we propose a novel feature selection algorithm for the identification of the margin of NPC image, named as modified random forest recursive feature selection (MRF-RFS). Specifically, to obtain a more discriminative feature subset for segmentation, a modified recursive feature selection method is applied to the original handcrafted feature set. Moreover, we combine the proposed feature selection method with the classical random forest (RF) in the training stage to take full advantage of its intrinsic property (i.e., feature importance measure).

Results To evaluate the segmentation performance, we verify our method on the T1-weighted MRI images of 18 NPC patients. The experimental results demonstrate that the proposed MRF-RFS method outperforms the baseline methods and deep learning methods on the task of segmenting NPC images.

Conclusion The proposed method could be effective in NPC diagnosis and useful for guiding radiation therapy.

背景 准确且可重复地描绘肿瘤边缘的方法在临床诊断和治疗中具有重要意义。在鼻咽癌 (NPC) 中，由于呈现软组织的高变异性、低对比度和不连续边界等限制，在磁共振成像 (MRI) 中很难识别肿瘤边缘，这增加了 NPC 分割任务的挑战。

目标 这项工作的目的是开发一种半自动算法，用于人为干预最少的 NPC 图像分割，同时它还能够以高精度和可重复性描绘肿瘤边缘。

方法 在本文中，我们提出了一种新的特征选择算法来识别NPC图像的边缘，称为改进的随机森林递归特征选择（MRF-RFS）。具体来说，为了获得更具辨别力的特征子集进行分割，将修改后的递归特征选择方法应用于原始手工特征集。此外，我们在训练阶段将所提出的特征选择方法与经典随机森林（RF）相结合，以充分利用其内在属性（即特征重要性度量）。

结果 为了评估分割性能，我们在 18 名 NPC 患者的 T1 加权 MRI 图像上验证了我们的方法。实验结果表明，所提出的 MRF-RFS 方法在分割 NPC 图像的任务上优于基线方法和深度学习方法。

结论 该方法可有效诊断鼻咽癌，对指导放射治疗有一定的指导意义。