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迈向鼻窦肿瘤的精准医学:从MRI中提取低维放射组学特征

Towards Precision Medicine in Sinonasal Tumors: Low-Dimensional Radiomic Signature Extraction from MRI.

作者信息

Biondi Riccardo, Gravante Giacomo, Remondini Daniel, Peluso Sara, Cominetti Serena, D'Amore Francesco, Bignami Maurizio, Arosio Alberto Daniele, Curti Nico

机构信息

IRCCS Istituto delle Scienze Neurologiche di Bologna, Data Science and Bioinformatics Laboratory, 40139 Bologna, Italy.

Division of Otorhinolaryngology, Department of Biotechnology and Life Sciences, University of Insubria, Ospedale di Circolo, 21100 Varese, Italy.

出版信息

Diagnostics (Basel). 2025 Jun 30;15(13):1675. doi: 10.3390/diagnostics15131675.

Abstract

Sinonasal tumors are rare, accounting for 3-5% of head and neck neoplasms. Machine learning (ML) and radiomics have shown promise in tumor classification, but current models lack detailed morphological and textural characterization. This study analyzed MRI data from 145 patients (76 malignant and 69 benign) across multiple centers. Radiomic features were extracted from T1-weighted (T1-w) images with contrast and T2-weighted (T2-w) images based on manually annotated tumor volumes. A dedicated ML pipeline assessed the effectiveness of different radiomic features and their integration with clinical variables. The DNetPRO algorithm was used to extract signatures combining radiomic and clinical data. The results showed that ML classification using both data types achieved a median Matthews Correlation Coefficient (MCC) of 0.60 ± 0.07. The best-performing DNetPRO models reached an MCC of 0.73 (T1-w + T2-w) and 0.61 (T1-w only). Key clinical features included symptoms and tumor size, while radiomic features provided additional diagnostic insights, particularly regarding gray-level distribution in T2-w and texture complexity in T1-w images. Despite its potential, ML-based radiomics faces challenges in clinical adoption due to data variability and model diversity. Standardization and interpretability are crucial for reliability. The DNetPRO approach helps explain feature importance and relationships, reinforcing the clinical relevance of integrating radiomic and clinical data for sinonasal tumor classification.

摘要

鼻窦肿瘤较为罕见,占头颈部肿瘤的3%-5%。机器学习(ML)和放射组学在肿瘤分类方面已显示出前景,但目前的模型缺乏详细的形态学和纹理特征描述。本研究分析了来自多个中心的145例患者(76例恶性和69例良性)的MRI数据。基于手动标注的肿瘤体积,从有对比剂的T1加权(T1-w)图像和T2加权(T2-w)图像中提取放射组学特征。一个专门的ML流程评估了不同放射组学特征的有效性及其与临床变量的整合。使用DNetPRO算法提取结合放射组学和临床数据的特征。结果表明,使用这两种数据类型的ML分类的马修斯相关系数(MCC)中位数为0.60±0.07。表现最佳的DNetPRO模型的MCC分别达到0.73(T1-w+T2-w)和0.61(仅T1-w)。关键临床特征包括症状和肿瘤大小,而放射组学特征提供了额外的诊断见解,特别是关于T2-w图像中的灰度分布和T1-w图像中的纹理复杂性。尽管基于ML的放射组学具有潜力,但由于数据变异性和模型多样性,在临床应用上面临挑战。标准化和可解释性对于可靠性至关重要。DNetPRO方法有助于解释特征的重要性和关系,加强了整合放射组学和临床数据用于鼻窦肿瘤分类的临床相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78dd/12248528/0f49264ecfc1/diagnostics-15-01675-g001.jpg

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