皮肤黑素瘤(CM)是最常见的皮肤恶性肿瘤。我们的研究旨在通过结合病理学和基因组学，探讨病理组学特征对CM的预后价值。本研究的目的是探讨病理组学特征的潜在应用价值。从The Cancer Genome Atlas (TCGA)数据库下载了CM患者的病理全扫描、临床信息和基因组数据。采用探索性数据分析(EDA)对患者特征进行可视化。通过差异分析筛选出与预后较差相关的基因。进行生存分析评估基因和病理组学特征的预后价值。基于病理组学特征和基因的人工神经网络(ANN)模型预测预后。采用相关性分析探索病理组学特征和基因之间的关联。从TCGA数据库获取了468个CM样本的临床特征和471个CM样本的基因组数据和病理图像。EDA结果结合多个机器学习(ML)模型表明，重要性排名前5的临床特征分别为年龄、活检部位、T分期、N分期和总体疾病分期，八个ML模型的精度低于0.56。通过比较测序数据获得了60个差异表达基因。利用CellProfile软件获取了每个病理图像的413个可用定量特征。基于病理组学特征的二元分类模型的准确率为0.99，损失值为1.7119e-04.基于差异表达基因的二元分类模型的准确率为0.98，损失值为0.1101。基于病理组学特征和差异表达基因的二元分类模型的准确率为0.97，损失值为0.2088。生存分析显示，基于基因表达和病理组学特征的高风险组的生存率明显低于低风险组。对222个病理组学特征和51个差异表达基因进行的生存分析的p值小于0.05。一些病理组学特征与ANO2、LINC00158、NDNF、ADAMTS15和ADGRB3等差异基因表达之间存在一定的相关性。结论：本研究评估了CM患者中病理组学特征和差异表达基因的预后意义。开发了三个ANN模型，所有模型的准确率均高于97%。特别是基于病理组学特征的ANN模型保持了99%的显著准确性。这些发现突出了CellProfile + ANN模型作为CM患者预后预测的优秀选择。此外，我们的相关性分析实验证明了疾病量化和定性变化之间的初步联系。在CM患者中，除了M分期和接受治疗等各种特征外，应特别关注年龄、活检部位、T分期、N分期和总体疾病分期等特征。© 2023年美国医学物理学家协会。

Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.The purpose of this study was to explore the potential application value of pathomics signatures.Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links.The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.© 2023 American Association of Physicists in Medicine.