Treatment response assessment for bladder cancer on CT based on computerized volume analysis, WHO and RECIST Criteria - Hadjiiski, Lubomir (AJR Am J Roentgenol, 2015)
PURPOSE: To evaluate the accuracy of our Auto-Initialized Cascaded Level Set (AI-CALS) 3D segmentation system, the World Health Organization (WHO), and the Response Evaluation Criteria In Solid Tumors (RECIST) criteria for estimation of treatment response of bladder cancer in CT urography. MATERIALS AND METHODS: Pre- and post- neoadjuvant chemotherapy treatment CT of 18 patients with muscle invasive localized or locally advanced bladder cancers were collected. The disease pathological stage determined by cystectomy after chemotherapy was available as reference standard of treatment response. Two radiologists measured the longest diameter and its perpendicular on the pre- and post-treatment scans. Full 3D contours for all tumors were manually outlined by one radiologist. AI-CALS was used to automatically extract 3D tumor boundary. The prediction accuracy of pT0 stage (complete response) at cystectomy by the manual, AI-CALS, WHO, and RECIST methods was estimated by the area under the receiver operating characteristic curve (AUC). RESULTS: The AUC for prediction of pT0 disease at cystectomy was 0.78±0.11 for AI-CALS compared to 0.82±0.10 for manual segmentation. The difference did not reach statistical significance (p=0.67). The prediction using RECIST criteria by radiologists with AUCs of 0.62±0.16 and 0.71±0.12, respectively, was lower than those of the two 3D methods. The prediction using WHO criteria by radiologists with AUCs of 0.56±0.15 and 0.60±0.13, respectively, was lower than all other methods. CONCLUSIONS: The 3D pre- and post-treatment volume change estimates obtained by radiologist’s manual segmentation and AI-CALS are more accurate for the irregular-shaped 3D tumors compared to the RECIST and WHO estimates.