JOURNAL ARTICLE

Detection and characterization of tumor changes in 18F-FDG PET patient monitoring using parametric imaging

Hatem Necib, Camilo Garcia, Antoine Wagner, Bruno Vanderlinden, Patrick Emonts, Alain Hendlisz, Patrick Flamen, Irène Buvat
Journal of Nuclear Medicine 2011, 52 (3): 354-61
21345787

UNLABELLED: In PET-based patient monitoring, metabolic tumor changes occurring between PET scans are most often assessed visually or by measuring only a few parameters (tumor volume or uptake), neglecting most of the image content. We propose and evaluate a parametric imaging (PI) method to assess tumor changes at the voxel level.

METHODS: Seventy-eight pairs of tumor images obtained from baseline and follow-up (18)F-FDG PET/CT for 28 patients with metastatic colorectal cancer were considered. For each pair, after CT-based registration of the PET volumes, the 2 PET datasets were subtracted. A biparametric graph of subtracted voxel values versus voxel values in the first PET scan was obtained. A model-based analysis of this graph was used to identify the tumor voxels in which significant changes occurred between the 2 scans and yielded indices characterizing these changes. The Response Evaluation Criteria in Solid Tumors (RECIST) based on the CT images obtained 5-8 wk after the second PET/CT scan were used to classify tumor masses as responding or progressive. On the basis of this classification, we compared the sensitivity and specificity of PI and an approach based on recommendations from the European Organization for Research and Treatment of Cancer (EORTC).

RESULTS: For tumor-based classification, the EORTC-based approach had a sensitivity and specificity of 85% and 52%, respectively, for detecting responding lesions, whereas PI had a sensitivity and specificity of 100% and 53%, respectively. None of responding tumors using RECIST was classified as progressive with the PI or EORTC-based criteria. Among the 14 progressive lesions according to RECIST, 12 were identified as progressive with PI whereas EORTC-based criteria classified only 1 as progressive and 13 as stable tumors. Considering the patient-based classification, none of the responders according to RECIST was classified as having progressive disease with the PI and EORTC-based criteria. PI has the advantage of showing a parametric image of the patient response to therapy, indicating potential heterogeneity in tumor response.

CONCLUSION: The PI method has been successfully applied to characterize early metabolic tumor changes in 78 lesions from (18)F-FDG PET/CT scans of patients with metastatic colorectal cancer during chemotherapy. The PI findings correlated well with the standard RECIST-based response assessment.

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