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

• Published in: Physics in medicine & biology 2016-01, Vol.61 (2), p.906-922
• Main Authors: Yip, Stephen S F, Coroller, Thibaud P, Sanford, Nina N, Huynh, Elizabeth, Mamon, Harvey, Aerts, Hugo J W L, Berbeco, Ross I
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; contour propagation; deformable registration; esophageal cancer; Esophageal Neoplasms - diagnostic imaging; Esophageal Neoplasms - drug therapy; Esophageal Neoplasms - radiotherapy; Female; Humans; Male; Middle Aged; Multimodal Imaging - methods; PET; Positron-Emission Tomography - methods; Tomography, X-Ray Computed - methods; treatment response prediction
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/0031-9155/61/2/906

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  =  0.71‒0.73, p