文章：

基于TC影像组学的机器学习模型预测转移性黑色素瘤预后的初步研究

Metastatic Melanoma Prognosis Prediction Using a TC Radiomic-Based Machine Learning Model: A Preliminary Study

原文发布日期：10 July 2025

DOI: 10.3390/cancers17142304

类型: Article

开放获取: 是

英文摘要：

Background/Objective: The approach to the clinical management of metastatic melanoma patients is undergoing a significant transformation. The availability of a large amount of data from medical images has made Artificial Intelligence (AI) applications an innovative and cutting-edge solution that could revolutionize the surveillance and management of these patients. In this study, we develop and validate a machine-learning model based on radiomic data extracted from a computed tomography (CT) analysis of patients with metastatic melanoma (MM). This approach was designed to accurately predict prognosis and identify the potential key factors associated with prognosis.Methods: To achieve this goal, we used radiomic pipelines to extract the quantitative features related to lesion texture, morphology, and intensity from high-quality CT images. We retrospectively collected a cohort of 58 patients with metastatic melanoma, from which a total of 60 CT series were used for model training, and 70 independent CT series were employed for external testing. Model performance was evaluated using metrics such as sensitivity, specificity, and AUC (area under the curve), demonstrating particularly favorable results compared to traditional methods.Results: The model used in this study presented a ROC-AUC curve of 82% in the internal test and, in combination with AI, presented a good predictive ability regarding lesion outcome.Conclusions: Although the cohort size was limited and the data were collected retrospectively from a single institution, the findings provide a promising basis for further validation in larger and more diverse patient populations. This approach could directly support clinical decision-making by providing accurate and personalized prognostic information.

摘要翻译： 

背景/目的：转移性黑色素瘤患者的临床管理方法正在经历重大变革。医学影像产生的大量数据使人工智能应用成为一种创新且前沿的解决方案，可能彻底改变这类患者的监测与管理模式。本研究基于转移性黑色素瘤患者计算机断层扫描（CT）影像提取的放射组学数据，开发并验证了一种机器学习模型。该模型旨在精准预测患者预后，并识别与预后相关的潜在关键因素。 方法：为实现这一目标，我们采用放射组学流程从高质量CT影像中提取与病灶纹理、形态和强度相关的定量特征。研究回顾性收集了58例转移性黑色素瘤患者的临床资料，其中60个CT序列用于模型训练，70个独立CT序列用于外部测试。通过敏感性、特异性及曲线下面积（AUC）等指标评估模型性能，结果显示相较于传统方法具有显著优势。 结果：本研究构建的模型在内部测试中ROC-AUC曲线达到82%，结合人工智能技术展现出良好的病灶转归预测能力。 结论：尽管研究队列规模有限且数据来源于单一机构的回顾性收集，但该发现为在更大规模、更多样化患者群体中进行进一步验证提供了良好基础。该方法通过提供精准、个性化的预后信息，有望为临床决策提供直接支持。

原文链接：

Metastatic Melanoma Prognosis Prediction Using a TC Radiomic-Based Machine Learning Model: A Preliminary Study