Clinical radiology · 2024
Characterization of parotid gland tumors using diffusion-relaxation correlation spectrum imaging: a preliminary study.
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Chen W, Geng D, Xu X-Q, Hu W-T, Dai Y-M, Wu F-Y, Zhu L-N
Аннотация
AIM: To assess the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of parotid gland tumors.
MATERIALS AND METHODS: Twenty-five pleomorphic adenomas (PA) patients, 9 Warthin's tumors (WT) patients and 7 malignant tumors (MT) patients were prospectively recruited. DR-CSI (7 b-values combined with 5 TEs, totally 35 diffusion-weighted images) was scanned for pre-treatment assessment. Diffusion (D)-T2 signal spectrum summating all voxels were built for each patient, characterized by D-axis with range 0∼5 × 10-3 mm2/s, and T2-axis with range 0∼300ms. With boundaries of 0.5 and 2.5 × 10-3 mm2/s for D, all spectra were divided into three compartments labeled A (low D), B (mediate D) and C (high D). Volume fractions acquired from each compartment (VA, VB, VC) were compared among PA, WT and MT. Diagnostic performance was assessed using receiver operating characteristic analysis and area under the curve (AUC).
RESULTS: Each subtype of parotid tumors had their specific D-T2 spectrum. PA showed significantly lower VA (8.85 ± 4.77% vs 20.68 ± 10.85%), higher VB (63.40 ± 8.18% vs 43.05 ± 7.16%), and lower VC (27.75 ± 8.51% vs 36.27 ± 11.09) than WT (all p<0.05). VB showed optimal diagnostic performance (AUC 0.969, sensitivity 92.00%, specificity 100.00%). MT showed significantly higher VA (21.23 ± 12.36%), lower VB (37.09 ± 6.43%), and higher VC (41.68 ± 13.72%) than PA (all p<0.05). Similarly, VB showed optimal diagnostic performance (AUC 0.994, sensitivity 96.00%, specificity 100.00%). No significant difference of VA, VB and VC was found between WT and MT.
CONCLUSIONS: DR-CSI might be a promising and non-invasive way for characterizing parotid gland tumors.
MATERIALS AND METHODS: Twenty-five pleomorphic adenomas (PA) patients, 9 Warthin's tumors (WT) patients and 7 malignant tumors (MT) patients were prospectively recruited. DR-CSI (7 b-values combined with 5 TEs, totally 35 diffusion-weighted images) was scanned for pre-treatment assessment. Diffusion (D)-T2 signal spectrum summating all voxels were built for each patient, characterized by D-axis with range 0∼5 × 10-3 mm2/s, and T2-axis with range 0∼300ms. With boundaries of 0.5 and 2.5 × 10-3 mm2/s for D, all spectra were divided into three compartments labeled A (low D), B (mediate D) and C (high D). Volume fractions acquired from each compartment (VA, VB, VC) were compared among PA, WT and MT. Diagnostic performance was assessed using receiver operating characteristic analysis and area under the curve (AUC).
RESULTS: Each subtype of parotid tumors had their specific D-T2 spectrum. PA showed significantly lower VA (8.85 ± 4.77% vs 20.68 ± 10.85%), higher VB (63.40 ± 8.18% vs 43.05 ± 7.16%), and lower VC (27.75 ± 8.51% vs 36.27 ± 11.09) than WT (all p<0.05). VB showed optimal diagnostic performance (AUC 0.969, sensitivity 92.00%, specificity 100.00%). MT showed significantly higher VA (21.23 ± 12.36%), lower VB (37.09 ± 6.43%), and higher VC (41.68 ± 13.72%) than PA (all p<0.05). Similarly, VB showed optimal diagnostic performance (AUC 0.994, sensitivity 96.00%, specificity 100.00%). No significant difference of VA, VB and VC was found between WT and MT.
CONCLUSIONS: DR-CSI might be a promising and non-invasive way for characterizing parotid gland tumors.