Automated and robust PERCIST-based thresholding framework for whole body PET-CT studies
Lei Bi1, Jinman Kim, Lingfeng Wen
1School of Information Technologies, University of Sydney, Australia. lebi8696@uni.sydney.edu.au
Summary
This study introduces an automated framework for calculating PET Response Evaluation Criteria in Solid Tumors (PERCIST) thresholds using whole-body PET-CT scans. This method enhances efficiency and reliability in depicting malignant lesions by automating liver segmentation for Standardized Uptake Value (SUV) normalization.
Area of Science:
- Nuclear medicine imaging
- Oncology
- Medical physics
Background:
- Positron Emission Tomography (PET) enables quantitative assessment of treatment response prior to morphological changes visible on Computed Tomography (CT).
- The PET Response Evaluation Criteria in Solid Tumors (PERCIST) standardizes metabolic response assessment using Standardized Uptake Value (SUV) normalized by lean body mass (LBM).
- Current PERCIST implementation relies on operator-dependent liver segmentation for defining the Volume of Interest (VOI), which is time-consuming and subjective, with challenges in segmenting low-dose CT data.
Purpose of the Study:
- To develop and evaluate a fully automatic framework for calculating PERCIST-based thresholding in whole-body PET-CT studies.
- To overcome the limitations of manual VOI delineation for SUVLBM normalization in PERCIST assessments.
- To improve the efficiency and objectivity of metabolic response evaluation in oncology.
Main Methods:
- A novel framework employing multi-atlas registration and voxel classification on CT data for automated liver segmentation and VOI reference delineation.
- Mapping the delineated VOI from CT to PET data to derive SUVLBM thresholding values.
- Utilizing the derived SUVLBM threshold for selecting metabolically active regions in PET scans.
Main Results:
- The automated framework successfully segmented liver structures and delineated the VOI reference across whole-body PET-CT studies.
- The system demonstrated reliability and efficiency in depicting malignant lesions using PERCIST thresholding, as validated on 28 clinical cases (lung cancer, lymphoma).
- The proposed method provides a robust approach for quantitative metabolic assessment in oncology.
Conclusions:
- A fully automatic framework for PERCIST thresholding in whole-body PET-CT has been successfully developed and validated.
- This automated approach addresses the subjectivity and time-consuming nature of manual VOI delineation, enhancing clinical workflow.
- The framework offers a reliable and efficient tool for quantitative assessment of treatment response in cancer patients.
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