James M Franklin1, Benjamin Irving2, Bartlomiej W Papiez2, Jesper F Kallehauge2, Lai Mun Wang3, Robert D Goldin4, Adrian L Harris5, Ewan M Anderson6, Julia A Schnabel7, Michael A Chappell2, Michael Brady5, Ricky A Sharma8, Fergus V Gleeson6. 1. Institute of Medical Imaging and Visualisation, Bournemouth University, UK; Radiology Department, Royal Bournemouth and Christchurch Hospitals NS Foundation Trust, UK. Electronic address: jfranklin@bournemouth.ac.uk. 2. Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK. 3. Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, UK. 4. Centre for Pathology, Imperial College, London, UK. 5. Department of Oncology, University of Oxford, UK. 6. Radiology Department, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, UK. 7. Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK. 8. NIHR University College London Hospitals Biomedical Research Centre, UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6DD, UK.
Abstract
PURPOSE: To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions. MATERIALS AND METHODS: This ethically-approved prospective study recruited patients with CRC and ≥1 hepatic metastases scheduled for hepatic resection. Patients underwent DCE-MRI pre-metastasectomy. Histological sections of resection specimens were spatially matched to DCE-MRI acquisitions and used to define histological subregions of viable and non-viable tumour. A semi-automated voxel-wise image segmentation algorithm based on the DCE-MRI contrast-uptake curves was used to define imaging subregions of viable and non-viable tumour. Overlap of histologically-defined and imaging subregions was compared using the Dice similarity coefficient (DSC). DCE-MRI PK parameters were compared for the whole tumour and histology-defined and imaging-derived subregions. RESULTS: Fourteen patients were included in the analysis. Direct histological comparison with imaging was possible in nine patients. Mean DSC for viable tumour subregions defined by imaging and histology was 0.738 (range 0.540-0.930). There were significant differences between Ktrans and kep for viable and non-viable subregions (p < 0.001) and between whole lesions and viable subregions (p < 0.001). CONCLUSION: We demonstrate good concordance of viable tumour segmentation based on pre-operative DCE-MRI with a post-operative histological gold-standard. This can be used to extract viable tumour-specific values from quantitative image analysis, and could improve treatment response assessment in clinical practice.
PURPOSE: To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions. MATERIALS AND METHODS: This ethically-approved prospective study recruited patients with CRC and ≥1 hepatic metastases scheduled for hepatic resection. Patients underwent DCE-MRI pre-metastasectomy. Histological sections of resection specimens were spatially matched to DCE-MRI acquisitions and used to define histological subregions of viable and non-viable tumour. A semi-automated voxel-wise image segmentation algorithm based on the DCE-MRI contrast-uptake curves was used to define imaging subregions of viable and non-viable tumour. Overlap of histologically-defined and imaging subregions was compared using the Dice similarity coefficient (DSC). DCE-MRI PK parameters were compared for the whole tumour and histology-defined and imaging-derived subregions. RESULTS: Fourteen patients were included in the analysis. Direct histological comparison with imaging was possible in nine patients. Mean DSC for viable tumour subregions defined by imaging and histology was 0.738 (range 0.540-0.930). There were significant differences between Ktrans and kep for viable and non-viable subregions (p < 0.001) and between whole lesions and viable subregions (p < 0.001). CONCLUSION: We demonstrate good concordance of viable tumour segmentation based on pre-operative DCE-MRI with a post-operative histological gold-standard. This can be used to extract viable tumour-specific values from quantitative image analysis, and could improve treatment response assessment in clinical practice.
Authors: Mingquan Lin; Jacob F Wynne; Boran Zhou; Tonghe Wang; Yang Lei; Walter J Curran; Tian Liu; Xiaofeng Yang Journal: J Appl Clin Med Phys Date: 2021-06-24 Impact factor: 2.102
Authors: Drew Maclean; Maria Tsakok; Fergus Gleeson; David J Breen; Robert Goldin; John Primrose; Adrian Harris; James Franklin Journal: Front Oncol Date: 2021-12-07 Impact factor: 6.244