文章：

基于高光谱成像组织指数图像的头颈部癌症皮肤病变分类

Skin Lesion Classification in Head and Neck Cancers Using Tissue Index Images Derived from Hyperspectral Imaging

原文发布日期：11 May 2025

DOI: 10.3390/cancers17101622

类型: Article

开放获取: 是

英文摘要：

Background: Skin lesions associated with head and neck carcinomas present a diagnostic challenge. Conventional imaging methods, such as dermoscopy and RGB imaging, often face limitations in providing detailed information about skin lesions and accurately differentiating tumor tissue from healthy skin. Methods: This study developed a novel approach utilizing tissue index images derived from hyperspectral imaging (HSI) in combination with machine learning (ML) classifiers to enhance lesion classification. The primary aim was to identify essential features for categorizing tumor, peritumor, and healthy skin regions using both RGB and hyperspectral data. Detailed skin lesion images of 16 patients, comprising 24 lesions, were acquired using HSI. The first- and second-order statistics radiomic features were extracted from both the tissue index images and RGB images, with the minimum redundancy–maximum relevance (mRMR) algorithm used to select the most relevant ones that played an important role in improving classification accuracy and offering insights into the complexities of skin lesion morphology. We assessed the classification accuracy across three scenarios: using only RGB images (Scenario I), only tissue index images (Scenario II), and their combination (Scenario III). Results: The results indicated an accuracy of 87.73% for RGB images alone, which improved to 91.75% for tissue index images. The area under the curve (AUC) for lesion classifications reached 0.85 with RGB images and over 0.94 with tissue index images. Conclusions: These findings underscore the potential of utilizing HSI-derived tissue index images as a method for the non-invasive characterization of tissues and tumor analysis.

摘要翻译： 

背景：头颈部癌相关皮肤病变的诊断具有挑战性。传统成像方法（如皮肤镜检查和RGB成像）在提供皮肤病变详细信息及准确区分肿瘤组织与健康皮肤方面常存在局限。方法：本研究开发了一种新方法，利用高光谱成像（HSI）衍生的组织指数图像结合机器学习（ML）分类器以提升病变分类效果。主要目标是通过RGB和高光谱数据识别区分肿瘤、瘤周及健康皮肤区域的关键特征。使用HSI获取了16名患者（共24处病变）的详细皮肤病变图像。从组织指数图像和RGB图像中提取了一阶和二阶统计放射组学特征，并采用最小冗余-最大相关性（mRMR）算法筛选出对提升分类准确率、揭示皮肤病变形态复杂性具有重要作用的最相关特征。我们评估了三种场景下的分类准确率：仅使用RGB图像（场景I）、仅使用组织指数图像（场景II）以及两者结合（场景III）。结果：仅使用RGB图像的准确率为87.73%，而仅使用组织指数图像时提升至91.75%。病变分类的曲线下面积（AUC）在RGB图像中达到0.85，在组织指数图像中超过0.94。结论：这些发现凸显了利用HSI衍生的组织指数图像作为无创组织表征和肿瘤分析方法的潜力。

原文链接：

Skin Lesion Classification in Head and Neck Cancers Using Tissue Index Images Derived from Hyperspectral Imaging