Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis
- PMID: 39064078
- PMCID: PMC11277332
- DOI: 10.3390/jcm13144038
Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis
Abstract
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.
Keywords: computed tomography; computer-assisted diagnosis; oral cancer; radiomics; tumor.
Conflict of interest statement
The authors declare no conflicts of interest.
Figures


Similar articles
-
A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.Acad Radiol. 2023 Aug;30(8):1591-1599. doi: 10.1016/j.acra.2022.11.007. Epub 2022 Nov 30. Acad Radiol. 2023. PMID: 36460582
-
CT Texture Analysis-Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas.Front Oncol. 2019 May 28;9:444. doi: 10.3389/fonc.2019.00444. eCollection 2019. Front Oncol. 2019. PMID: 31192138 Free PMC article.
-
CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades.Front Oncol. 2019 Aug 30;9:821. doi: 10.3389/fonc.2019.00821. eCollection 2019. Front Oncol. 2019. PMID: 31544063 Free PMC article.
-
Approximation of head and neck cancer volumes in contrast enhanced CT.Cancer Imaging. 2015 Sep 29;15:16. doi: 10.1186/s40644-015-0051-3. Cancer Imaging. 2015. PMID: 26419914 Free PMC article.
-
[Correlations of 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging Parameters with the Pathological Differentiation of Head and Neck Squamous Cell Carcinoma and Their Diagnostic Efficiencies].Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2018 Apr 28;40(2):242-249. doi: 10.3881/j.issn.1000-503X.2018.02.015. Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2018. PMID: 29724315 Chinese.
Cited by
-
CT Texture Patterns Reflect HPV Status but Not Histological Differentiation in Oropharyngeal Squamous Cell Carcinoma.Cancers (Basel). 2025 Jul 11;17(14):2317. doi: 10.3390/cancers17142317. Cancers (Basel). 2025. PMID: 40723201 Free PMC article.
-
AI in Medical Imaging and Image Processing.J Clin Med. 2025 Jun 11;14(12):4153. doi: 10.3390/jcm14124153. J Clin Med. 2025. PMID: 40565899 Free PMC article.
-
Development and validation of an MRI radiomics-based interpretable machine learning model for predicting the progression-free survival in locally advanced nasopharyngeal carcinoma.Quant Imaging Med Surg. 2025 Jun 6;15(6):5347-5361. doi: 10.21037/qims-24-1860. Epub 2025 May 27. Quant Imaging Med Surg. 2025. PMID: 40606323 Free PMC article.
-
A dual approach to third molar complexity: correlating fractal analysis with the pederson difficulty index - non-clinical research article.BMC Oral Health. 2025 Jul 2;25(1):1073. doi: 10.1186/s12903-025-06520-2. BMC Oral Health. 2025. PMID: 40604724 Free PMC article.
References
-
- Panarese I., Aquino G., Ronchi A., Longo F., Montella M., Cozzolino I., Roccuzzo G., Colella G., Caraglia M., Franco R. Oral and Oropharyngeal squamous cell carcinoma: Prognostic and predictive parameters in the etiopathogenetic route. Expert Rev. Anticancer Ther. 2019;19:105–119. doi: 10.1080/14737140.2019.1561288. - DOI - PubMed
-
- Broders A.C. The microscopic grading of cancer. Surg. Clin. N. Am. 1941;21:947–962.
Grants and funding
LinkOut - more resources
Full Text Sources