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Differentiation of benign, intermediate, and malignant soft-tissue tumours by using multiple diffusion-weighted imaging models.

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PMID40403342
JournalClinical radiology
Publication Date2025-04-26
Ingested2026-08-02 12:04 AM
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AIM: The aim of this study was to determine whether intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) can differentiate benign, intermediate, and malignant soft-tissue tumours (STTs) of the extremities and trunk. MATERIALS AND METHODS: We prospectively recruited 100 STT patients (32, 15, and 53 patients with benign, intermediate, and malignant tumours, respectively). The patients underwent IVIM and DKI, and the following parameters were measured: standard apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), water diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), mean diffusivity (MD), and mean kurtosis (MK). Statistical analyses were performed using receiver operating characteristic curves, the Kruskal-Wallis H test, and post hoc test with Bonferroni correction. RESULTS: Standard ADC, Dslow, DDC, and MD values gradually decreased from benign to intermediate and malignant STTs. Intermediate STTs displayed a lower f value than benign tumours (P=0.029). The MK value was higher in malignant tumours than in intermediate and benign tumours (P=0.021 and <0.001, respectively). The DDC value best differentiated benign tumours from nonbenign (intermediate and malignant) tumours (area under the curve [AUC] = 0.884, 0853, and 0.892, respectively). The optimal MK cut-off value for differentiating intermediate and malignant tumours was 0.65 (sensitivity: 73.33%, specificity: 81.13%, accuracy: 79.41%). CONCLUSION: IVIM and DKI parameters were helpful for differentiating benign, intermediate, and malignant STTs and can complement conventional MRI, with DDC and MK values showing high diagnostic efficacy.

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Differentiation of benign, intermediate, and malignant soft-tissue tumours by using multiple diffusion-weighted imaging models.

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