[OA049] Tumour segmentation in longitudinal diffusion weighted MRI of rectal cancer patients receiving radiotherapy. (August 2018)

• Record Type: Journal Article
• Authors: Byskov, Camilla Skinnerup; Kallehauge, Jesper Folsted; Harbøll, Anja; Pedersen, Erik Morre; Spindler, Karen-Lise Garm; Muren, Ludvig Paul
• Is Part Of: Physica medica. Volume 52(2018)Supplement 1
• Page Start: 20
• Page End: 21
• Publication Date: 2018-08
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.06.121
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Purpose: Target volume delineation is a fundamental step in radiotherapy and is subject to considerable inter-observer variation. Diffusion weighted magnetic resonance imaging (DW-MRI) is being investigated for tumour definition in locally advanced rectal cancer (LARC), offering high contrast between volumes with different diffusion characteristics. Segmentation algorithms such as k-means clustering have the potential to improve consistency of target delineation. In this study we have evaluated the performance of two segmentation algorithms for target definition by comparing them to expert delineations on longitudinal DW-MRIs for patients with LARC. Methods: Weekly repeat DW-MRI scans (b-values: 0, 150, 1000 s/mm 2 were recorded on a 1.5T Philips Ingenia MR scanner (Best, NL) for five LARC patients treated with neo-adjuvant radiotherapy (50.4 Gy, 28 fractions). Initially, feature scaling was performed on the b = 1000 s/mm 2 image set as well as the calculated Apparent Diffusion Coefficient (ADC) maps. Tumour segmentation was performed using i) k-means clustering and ii) Gaussian mixture segmentation, with three classes reflecting tumour, air and normal tissue. Tumour volumes were selected as the classes having highest mean signal intensity at b = 1000 s/mm 2 and lowest mean ADC value. Manual delineations were performed by two observers until consensus was reached, using primarily the b = 1000 s/mm 2 image set (with the ADC maps taken into consideration). Manual delineations were compared to automatic delineations using the DICE similarity coefficient (DSC), Hausdorff Distance (HD) and mean distance to agreement (DTA). Results: For k-means clustering DSC was 0.8 (median; range: 0.1–0.9), HD was 10 mm (median; range: 4.4–26 mm) and DTA was 1.5 mm (median; range: 0.1–0.9 mm). Corresponding results for Gaussian mixture segmentation were 0.7 (median; range: 0.2–0.9), 8.5 mm (median; range: 4.1–28 mm) and 1.7 mm (median; range: 0.8–11 mm). DSC decreased during treatment course for both segmentation algorithms (p < 0.05). Conclusions: Tumour segmentation with k-means clustering and Gaussian mixture model based on DW-MRI and ADC maps perform similar and may improve consistency in target volume delineation for LARC patients.