SU-D-BRB-03: Prediction of Tumor Outcomes Through Wavelet Image Fusion and Texture Analysis of PET/MR Imaging

Medical Physics
M VallièresI El Naqa

Abstract

To investigate the combination of PET/MR image features for the early prediction of tumor metastases to the lungs in soft-tissue sarcoma (STS) cancer. A dataset of 24 patients with histologically proven STS was used in this study. All patients underwent pre-treatment FDG-PET and MR scans, which comprised of T1 and T2-fat suppression weighted (T2FS) sequences. The patients had a median follow-up period of 36 months (range: 6-69 months). Eight patients developed metastases to the lungs.Tumors were contoured on the T2FS scans by an expert physician. Fusion of the co-registered FDG-PET/MR scans was performed using a wavelet transform technique. A SUV feature (SUVmax) from the FDG-PET scans and 6 texture features from the co-occurrence matrix of the fused scans were extracted from the tumor region and correlation with the clinical endpoint of metastases to the lungs was investigated. Statistical analysis was performed using Spearman's rank correlation (rs) and multivariable logistic regression. The highest univariate prediction was found on FDG-PET/T2FS fused scans analyzed using the texture features "Sum-Mean" and "Variance". These two fused scan-texture feature combinations reached rs = -0.6838 (p = 0.0003). In comparison, SUVmax ...Continue Reading

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