de Oliveira Lays Assolini Pinheiro, Lopes Diana Lorena Garcia, Gomes João Pedro Perez, da Silveira Rafael Vinicius, Nozaki Daniel Vitor Aguiar, Santos Lana Ferreira, Castellano Gabriela, de Castro Lopes Sérgio Lúcio Pereira, Costa Andre Luiz Ferreira
Department of Anesthesiology, Oncology and Radiology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas 13084-971, SP, Brazil.
Postgraduate Program in Dentistry, Dentomaxillofacial Radiology and Imaging Laboratory, Department of Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil.
J Clin Med. 2024 Jul 10;13(14):4038. doi: 10.3390/jcm13144038.
This study explores the efficacy of texture analysis by using preoperative multi-slice spiral computed tomography (MSCT) to non-invasively determine the grade of cellular differentiation in head and neck squamous cell carcinoma (HNSCC). In a retrospective study, MSCT scans of patients with HNSCC were analyzed and classified based on its histological grade as moderately differentiated, well-differentiated, or poorly differentiated. The location of the tumor was categorized as either in the bone or in soft tissues. Segmentation of the lesion areas was conducted, followed by texture analysis. Eleven GLCM parameters across five different distances were calculated. Median values and correlations of texture parameters were examined in relation to tumor differentiation grade by using Spearman's correlation coefficient and Kruskal-Wallis and Dunn tests. Forty-six patients were included, predominantly female (87%), with a mean age of 66.7 years. Texture analysis revealed significant parameter correlations with histopathological grades of tumor differentiation. The study identified no significant age correlation with tumor differentiation, which underscores the potential of texture analysis as an age-independent biomarker. The strong correlations between texture parameters and histopathological grades support the integration of this technique into the clinical decision-making process.
本研究探讨了术前多排螺旋计算机断层扫描（MSCT）进行纹理分析以无创确定头颈部鳞状细胞癌（HNSCC）细胞分化程度的疗效。在一项回顾性研究中，对HNSCC患者的MSCT扫描进行分析，并根据其组织学分级分为中度分化、高分化或低分化。肿瘤位置分为骨内或软组织内。对病变区域进行分割，然后进行纹理分析。计算了五个不同距离的11个灰度共生矩阵（GLCM）参数。使用Spearman相关系数以及Kruskal-Wallis和Dunn检验，研究纹理参数的中位数和相关性与肿瘤分化程度的关系。纳入46例患者，以女性为主（87%），平均年龄66.7岁。纹理分析显示参数与肿瘤分化的组织病理学分级存在显著相关性。该研究未发现年龄与肿瘤分化有显著相关性，这突出了纹理分析作为一种与年龄无关的生物标志物的潜力。纹理参数与组织病理学分级之间的强相关性支持将该技术纳入临床决策过程。
