Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

Stephen S.F. Yip, Thibaud P. Coroller, Nina N. Sanford, Elizabeth Huynh, Harvey Mamon, Hugo J.W.L. Aerts, Ross I. Berbeco

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI = 58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI = 69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC = 0.58, q-value = 0.33), while the differentiation was significant with other textures (AUC = 0.710.73, p < 0.009). Among the deformable algorithms, fast-demons (AUC = 0.680.70, q-value < 0.03) and fast-free-form (AUC = 0.690.74, q-value < 0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC = 0.720.78, q-value < 0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

Original languageEnglish (US)
Article number906
Pages (from-to)906-922
Number of pages17
JournalPhysics in medicine and biology
Volume61
Issue number2
DOIs
Publication statusPublished - Jan 7 2016
Externally publishedYes

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Keywords

  • contour propagation
  • deformable registration
  • esophageal cancer
  • PET
  • treatment response prediction

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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