Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study
To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder.
Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient’s lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test.
Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening.
The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer.
• Heterogeneity is an intrinsic characteristic of tumoral tissue.
• Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy.
• Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity.
• The quantification helps differentiate malignant from benign urinary bladder tissue.
KeywordsBladder malignancy Tumour heterogeneity Apparent Diffusion Coefficient Histogram analysis K-means clustering
The scientific guarantor of this publication is Dr. Michael V. Knopp at The Ohio State University. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. The DCE-MRI data of the patient cohort were reported in Investigative Radiology and the Journal of Magnetic Resonance Imaging. Methodology: prospective, diagnostic or prognostic study, performed at one institution.
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