title: Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients.
abstract: BACKGROUND
  Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers. The aim was to identify clusters reflecting treatment resistance that could be used for targeted radiotherapy with a dose-painting approach.
  
  
  METHODS
  Eighty-one patients with locally advanced cervical cancer underwent DCE-MRI prior to chemoradiotherapy. The resulting image time series were fitted to two pharmacokinetic models, the Tofts model (yielding parameters Ktransand νe) and the Brix model (ABrix, kepand kel). K-means clustering was used to group similar voxels based on either the pharmacokinetic parameter maps or the relative signal increase (RSI) time series. The associations between voxel clusters and treatment outcome (measured as locoregional control) were evaluated using the volume fraction or the spatial distribution of each cluster.
  
  
  RESULTS
  One voxel cluster based on the RSI time series was significantly related to locoregional control (adjusted p-value 0.048). This cluster consisted of low-enhancing voxels. We found that tumors with poor prognosis had this RSI-based cluster gathered into few patches, making this cluster a potential candidate for targeted radiotherapy. None of the voxels clusters based on Tofts or Brix parameter maps were significantly related to treatment outcome.
  
  
  CONCLUSIONS
  We identified one group of tumor voxels significantly associated with locoregional relapse that could potentially be used for dose painting. This tumor voxel cluster was identified using the raw MRI time series rather than the pharmacokinetic maps.
authors: a Department of Mathematical Sciences and Technology , Norwegian University of Life Sciences , Ås , Norway.
authors: Turid
authors: T
authors: Torheim
authors: a Department of Mathematical Sciences and Technology , Norwegian University of Life Sciences , Ås , Norway.
authors: Aurora R
authors: AR
authors: Groendahl
authors: b Department of Radiology , Sørlandet Sykehus HF , Kristiansand , Norway.
authors: Erlend K F
authors: EK
authors: Andersen
authors: c Department of Radiation Biology , Oslo University Hospital , Oslo , Norway.
authors: Heidi
authors: H
authors: Lyng
authors: d Department of Medical Physics , Oslo University Hospital , Oslo , Norway.|e Department of Physics , University of Oslo , Oslo , Norway.
authors: Eirik
authors: E
authors: Malinen
authors: a Department of Mathematical Sciences and Technology , Norwegian University of Life Sciences , Ås , Norway.
authors: Knut
authors: K
authors: Kvaal
authors: a Department of Mathematical Sciences and Technology , Norwegian University of Life Sciences , Ås , Norway.
authors: Cecilia M
authors: CM
authors: Futsaether
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000970:Antineoplastic Agents / Q000627:therapeutic use; D001918:Brachytherapy; D059248:Chemoradiotherapy; D002945:Cisplatin / Q000627:therapeutic use; D016000:Cluster Analysis; D003287:Contrast Media; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D016896:Treatment Outcome; D002583:Uterine Cervical Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*
chemical_list: D000970:Antineoplastic Agents; D003287:Contrast Media; D002945:Cisplatin