Objective: To analyze the frequency and co-occurrence of dermoscopic patterns in BCC lesions and their relationship with histopathologic subtypes, using statistical analysis and Information Theory tools such as entropy, conditional entropy, mutual information, and Hamming weight. Methods: A total of 223 dermoscopic images (256x256 pixels) of histologically confirmed BCC lesions from Hospital Universitario Virgen Macarena (Seville, Spain) were analyzed. Each image was multilabel-annotated for the presence of nine dermoscopic patterns and categorized into one of four BCC subtypes: superficial, nodular, infiltrative, or micronodular. Statistical and information-theoretic methods were applied, including co-occurrence matrices, Bayesian conditional probabilities, and entropy-based metrics. Mutual information quantified the predictive value of individual and paired patterns, and decision trees were built based on diagnostic informativeness. Results: Nodular BCC was highly associated with most dermoscopic patterns, particularly arborizing telangiectasia and blue-gray ovoid nests. Superficial BCC showed stronger associations with maple leaf-like structures and shiny white-red areas. Some patterns, like spoke-wheel areas and white streaks, showed low discriminative power. Mutual information and conditional probabilities identified meaningful pattern-pair dependencies for each subtype. Decision trees revealed that subsets of patterns could enhance subtype classification by accumulating diagnostic information. Conclusions: Information Theory enables a quantitative understanding of dermoscopic patterns and their relationship with BCC subtypes. This framework highlights key diagnostic features, aids in differentiating complex cases, and supports the development of automated, pattern-based diagnostic tools. Further research with larger, more balanced datasets is encouraged.

翻译：目的：利用统计分析及信息论工具（包括熵、条件熵、互信息与汉明权重），分析基底细胞癌（BCC）皮损中皮肤镜模式的频率与共现情况，及其与组织病理学亚型的关系。方法：分析来自西班牙塞维利亚Hospital Universitario Virgen Macarena的223张经组织学确诊的BCC皮肤镜图像（256x256像素）。每张图像均经多标签标注，包含九种皮肤镜模式的存在情况，并归类为四种BCC亚型之一：浅表型、结节型、浸润型或微结节型。应用统计与信息论方法进行分析，包括共现矩阵、贝叶斯条件概率及基于熵的度量。互信息用于量化单个及成对模式的预测价值，并基于诊断信息量构建决策树。结果：结节型BCC与大多数皮肤镜模式高度相关，尤其是树枝状毛细血管扩张与蓝灰色卵圆形巢。浅表型BCC则与枫叶样结构及亮白-红色区域有更强关联。部分模式（如轮辐状区域与白色条纹）的鉴别能力较低。互信息与条件概率识别出各亚型中有意义的模式对依赖关系。决策树显示，通过累积诊断信息，模式的子集可提升亚型分类效能。结论：信息论为定量理解皮肤镜模式及其与BCC亚型的关系提供了框架。该方法突出了关键诊断特征，有助于鉴别复杂病例，并支持开发基于模式的自动化诊断工具。建议未来采用更大规模、更平衡的数据集进行进一步研究。