Journal of Shanghai Jiao Tong University (Medical Science) ›› 2025, Vol. 45 ›› Issue (9): 1202-1213.doi: 10.3969/j.issn.1674-8115.2025.09.012

• Clinical research • Previous Articles     Next Articles

Using diffusion-relaxation correlation spectroscopic imaging to assess the heterogeneity of head and neck tumors and identify occult lymph node metastasis

LI Siyu1, CHEN Ya1, HU Wentao2, DAI Yongming3, WU Yingwei1()   

  1. 1.Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
    2.Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
    3.School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
  • Received:2025-03-06 Accepted:2025-03-25 Online:2025-09-28 Published:2025-09-30
  • Contact: WU Yingwei E-mail:wuyw0103@hotmail.com
  • Supported by:
    National Natural Science Foundation of China(82373114);Cross Disciplinary Research Fund of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine(JYJC202107);“Two-Hundred Talents”Program of Shanghai Jiao Tong University School of Medicine(20191815)

Abstract:

Objective ·To evaluate the feasibility and diagnostic performance of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) in assessing the heterogeneity of benign and malignant head and neck tumors, as well as in identifying occult lymph node metastasis (OLNM). Methods ·A prospective study was conducted from January to December 2024 at Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, enrolling patients with suspected head and neck tumors who were scheduled for surgery and had a confirmed pathological diagnosis. All patients underwent preoperative routine head and neck plain and contrast-enhanced magnetic resonance imaging (MRI) examinations, including DR-CSI sequence. Conventional imaging parameters, including maximal diameter (MD), depth of invasion (DOI) for tumors, and MD and short diameter (SD) for lymph nodes, were obtained. Post-processing was performed to obtain apparent diffusion coefficient (ADC), T2 value, and D-T2 spectra for all lesions. The compartment segmentation strategy was optimized based on the spectral peak distribution characteristics of different diseases, and the volume fraction (V i ) of each compartment was obtained. Independent sample t-tests, Mann-Whitney U tests, chi-square tests, or Fisher's exact tests were used to compare intergroup differences in clinical data and imaging metrics. Principal component analysis (PCA) and Adonis analysis were employed to evaluate the discriminative ability of imaging metrics among different subtypes of benign tumors. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of univariate and multivariable models to characterize the malignancy of head and neck squamous cell carcinoma (HNSCC) and identify OLNM. Results ·A total of 97 cases were collected, including 28 benign tumors and 69 HNSCCs. Fifteen pathologically confirmed OLNMs and 20 benign lymph nodes (BLNs) were also enrolled. Among the 28 benign tumors, there were 6 cases of pleomorphic adenoma stroma-rich (PA stroma-rich), 9 cases of pleomorphic adenoma stroma-poor (PA stroma-poor), 9 cases of Warthin's tumor (WT), and 4 cases of basal cell adenoma (BCA). Statistically significant differences were observed in certain imaging parameters (ADC, T2, and DR-CSI V i ) among benign tumor subtypes. PCA analysis demonstrated a strong discriminative ability of imaging parameters in distinguishing pathological subtypes of benign tumors (R²=0.64, P<0.001). Among the 69 HNSCCs, 47 were classified as Grade 1 (well/moderately well-differentiated) and 22 as Grade 2 (moderately/poorly differentiated). Compared to Grade 1, Grade 2 showed lower ADC and higher T2 values, though differences were not statistically significant. As HNSCC malignancy increased, VA4 decreased and VB4increased significantly. OLNM showed a significant increase in SD and VA4 compared to BLNs. The combination of SD and VA4 for preoperative OLNM identification achieved a diagnostic efficiency of 0.843. Conclusion ·DR-CSI can analyze diffusion and relaxation characteristics at the sub-voxel level, offering valuable insights for characterizing benign head and neck tumor subtypes, assessing HNSCC malignancy, and identifying OLNMs. Compared to traditional parameters like ADC or T2, DR-CSI provides enhanced tissue microstructure analysis.

Key words: diffusion-relaxation correlation spectroscopic imaging, pleomorphic adenoma, Warthin's tumor, basal cell adenoma, head and neck squamous cell carcinoma, occult lymph node metastasis

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