title: Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry?
abstract: OBJECTIVE
  Diffusion-weighted imaging (DWI) tumor volumetry is promising for rectal cancer response assessment, but an important drawback is that manual per-slice tumor delineation can be highly time consuming. This study investigated whether manual DWI-volumetry can be reproduced using a (semi)automated segmentation approach.
  
  
  METHODS
  Seventy-nine patients underwent magnetic resonance imaging (MRI) that included DWI (highest b value [b1000 or b1100]) before and after chemoradiation therapy (CRT). Tumor volumes were assessed on b1000 (or b1100) DWI before and after CRT by means of (1) automated segmentation (by 2 inexperienced readers), (2) semiautomated segmentation (manual adjustment of the volumes obtained by method 1 by 2 radiologists), and (3) manual segmentation (by 2 radiologists); this last assessment served as the reference standard. Intraclass correlation coefficients (ICC) and Dice similarity indices (DSI) were calculated to evaluate agreement between different methods and observers. Measurement times (from a radiologist's perspective) were recorded for each method.
  
  
  RESULTS
  Tumor volumes were not significantly different among the 3 methods, either before or after CRT (P=.08 to .92). ICCs compared to manual segmentation were 0.80 to 0.91 and 0.53 to 0.66 before and after CRT, respectively, for the automated segmentation and 0.91 to 0.97 and 0.61 to 0.75, respectively, for the semiautomated method. Interobserver agreement (ICC) pre and post CRT was 0.82 and 0.59 for automated segmentation, 0.91 and 0.73 for semiautomated segmentation, and 0.91 and 0.75 for manual segmentation, respectively. Mean DSI between the automated and semiautomated method were 0.83 and 0.58 pre-CRT and post-CRT, respectively; DSI between the automated and manual segmentation were 0.68 and 0.42 and 0.70 and 0.41 between the semiautomated and manual segmentation, respectively. Median measurement time for the radiologists was 0 seconds (pre- and post-CRT) for the automated method, 41 to 69 seconds (pre-CRT) and 60 to 67 seconds (post-CRT) for the semiautomated method, and 180 to 296 seconds (pre-CRT) and 84 to 91 seconds (post-CRT) for the manual method.
  
  
  CONCLUSIONS
  DWI volumetry using a semiautomated segmentation approach is promising and a potentially time-saving alternative to manual tumor delineation, particularly for primary tumor volumetry. Once further optimized, it could be a helpful tool for tumor response assessment in rectal cancer.
authors.affiliation: Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Surgery, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Miriam M
authors.initials: MM
authors.lastname: van Heeswijk
authors.affiliation: Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. Electronic address: d.lambregts@nki.nl.
authors.forename: Doenja M J
authors.initials: DM
authors.lastname: Lambregts
authors.affiliation: GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Joost J M
authors.initials: JJ
authors.lastname: van Griethuysen
authors.affiliation: Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Stanley
authors.initials: S
authors.lastname: Oei
authors.affiliation: Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Sheng-Xiang
authors.initials: SX
authors.lastname: Rao
authors.affiliation: Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Carla A M
authors.initials: CA
authors.lastname: de Graaff
authors.affiliation: Atrium Medical Centre Parkstad/Zuyderland Medical Centre, Heerlen, The Netherlands.
authors.forename: Roy F A
authors.initials: RF
authors.lastname: Vliegen
authors.affiliation: GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Geerard L
authors.initials: GL
authors.lastname: Beets
authors.affiliation: Laboratory of Computational Medicine, Institute of Computer Science, FORTH, Heraklion, Crete, Greece.
authors.forename: Nikos
authors.initials: N
authors.lastname: Papanikolaou
authors.affiliation: GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Regina G H
authors.initials: RG
authors.lastname: Beets-Tan
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology* / Q000628:therapy*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D059248:Chemoradiotherapy; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012004:Rectal Neoplasms / Q000473:pathology* / Q000628:therapy*; D015203:Reproducibility of Results; D013997:Time Factors; D047368:Tumor Burden