Courtenay Lloyd A, Barbero-García Innes, Aramendi Julia, González-Aguilera Diego, Rodríguez-Martín Manuel, Rodríguez-Gonzalvez Pablo, Cañueto Javier, Román-Curto Concepción
Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain.
Deptartment of Geology, Facultad de Ciencia y Tecnología, Universidad del País Vasco-Euskal Herriko Unibertsitatea (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Spain.
J Clin Med. 2022 Jul 28;11(15):4392. doi: 10.3390/jcm11154392.
The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions' shape should be integrated as a fundamental part of future detection techniques in clinical screening.
非黑色素瘤皮肤癌（NMSC）的早期检测对于实现最佳治疗效果至关重要。形状被认为是检测某些类型皮肤癌（如黑色素瘤）的主要参数之一。对于NMSC，形状作为视觉检测参数的重要性尚未得到充分研究。分析了一个包含993张标准相机图像的数据集，这些图像包含不同类型的NMSC和良性皮肤病变。对于每张图像，提取病变边界。经过对齐和缩放后，计算每个病变边界的椭圆傅里叶分析（EFA）系数。还计算了病变的不对称性。然后，采用多元统计进行降维，最后采用计算学习分类来评估类别的可分离性。在大多数情况下，恶性和良性样本之间的分离是成功的。表现最佳的方法是EFA系数和不对称性的组合。EFA和不对称性的组合产生了0.786的平衡准确率和0.735的曲线下面积。在区分良性和恶性病变时，EFA和不对称性的组合用于病变分类产生了显著的成功率。鉴于这些结果，皮肤病变的形状应作为未来临床筛查检测技术的基本组成部分加以整合。
