title: A multi-Gaussian model for apparent diffusion coefficient histogram analysis of Wilms' tumour subtype and response to chemotherapy.
abstract: Wilms' tumours (WTs) are large heterogeneous tumours, which typically consist of a mixture of histological cell types, together with regions of chemotherapy-induced regressive change and necrosis. The predominant cell type in a WT is assessed histologically following nephrectomy, and used to assess the tumour subtype and potential risk. The purpose of this study was to develop a mathematical model to identify subregions within WTs with distinct cellular environments in vivo, determined using apparent diffusion coefficient (ADC) values from diffusion-weighted imaging (DWI). We recorded the WT subtype from the histopathology of 32 tumours resected in patients who received DWI prior to surgery after pre-operative chemotherapy had been administered. In 23 of these tumours, DWI data were also available prior to chemotherapy. Histograms of ADC values were analysed using a multi-Gaussian model fitting procedure, which identified 'subpopulations' with distinct cellular environments within the tumour volume. The mean and lower quartile ADC values of the predominant viable tissue subpopulation (ADC(1MEAN), ADC(1LQ)), together with the same parameters from the entire tumour volume (ADC(0MEAN), ADC(0LQ)), were tested as predictors of WT subtype. ADC(1LQ) from the multi-Gaussian model was the most effective parameter for the stratification of WT subtype, with significantly lower values observed in high-risk blastemal-type WTs compared with intermediate-risk stromal, regressive and mixed-type WTs (p < 0.05). No significant difference in ADC(1LQ) was found between blastemal-type and intermediate-risk epithelial-type WTs. The predominant viable tissue subpopulation in every stromal-type WT underwent a positive shift in ADC(1MEAN) after chemotherapy. Our results suggest that our multi-Gaussian model is a useful tool for differentiating distinct cellular regions within WTs, which helps to identify the predominant histological cell type in the tumour in vivo. This shows potential for improving the risk-based stratification of patients at an early stage, and for guiding biopsies to target the most malignant part of the tumour.
authors.affiliation: Developmental Imaging and Biophysics Section, Institute of Child Health, University College London, London, UK.
authors.forename: Patrick W
authors.initials: PW
authors.lastname: Hales
authors.affiliation: Radiology Department, Great Ormond Street Hospital, London, UK.
authors.forename: Øystein E
authors.initials: ØE
authors.lastname: Olsen
authors.affiliation: Developmental Biology and Cancer, Institute of Child Health, University College London, London, UK.
authors.forename: Neil J
authors.initials: NJ
authors.lastname: Sebire
authors.affiliation: Developmental Biology and Cancer, Institute of Child Health, University College London, London, UK.
authors.forename: Kathy
authors.initials: K
authors.lastname: Pritchard-Jones
authors.affiliation: Developmental Imaging and Biophysics Section, Institute of Child Health, University College London, London, UK.
authors.forename: Chris A
authors.initials: CA
authors.lastname: Clark
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use*; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D016903:Drug Monitoring / Q000379:methods; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007223:Infant; D007680:Kidney Neoplasms / Q000145:classification / Q000188:drug therapy* / Q000473:pathology*; D008297:Male; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome; D009396:Wilms Tumor / Q000145:classification / Q000188:drug therapy* / Q000473:pathology*
chemical_list: D000970:Antineoplastic Agents
keywords: Wilms’ tumour; apparent diffusion coefficient (ADC); diffusion-weighted imaging (DWI); histogram analysis