Rapid Standardized CT-Based Method to Determine Lean Body Mass SUV for PET-A Significant Improvement Over Prediction Equations.

In 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) studies, maximum standardized uptake value (SUVmax) is the parameter commonly used to provide a measurement of the metabolic activity of a tumor. SUV normalized by body mass is affected by the proportions of body fat and lean tissue, which present high variability in patients with cancer. SUV corrected by lean body mass (LBM), denoted as SUL, is recommended to provide more accurate, consistent, and reproducible SUV results; however, LBM is frequently estimated rather than measured. Given the increasing importance of a quantitative PET parameter, especially when comparing PET studies over time to evaluate disease response clinically, and its use in oncological clinical trials, we set out to evaluate the commonly used equations originally derived by James (1976) and Janmahasatian et al. (2005) against computerized tomography (CT)-derived measures of LBM. Methods: Whole-body 18F-FDG PET images of 195 adult patients with cancer were analyzed retrospectively. Representative liver SUVmean was normalized by total body mass. SUL was calculated using a quantitative determination of LBM based on the CT component of the PET/CT study (LBMCT) and compared against the equation-estimated SUL. Bland and Altman plots were generated for SUV-SUL differences. Results: This consecutive sample of patients undergoing usual care (men, n = 96; women, n = 99) varied in body mass (38-127 kg) and in Body Mass Index (BMI) (14.7-47.2 kg/m2). LBMCT weakly correlated with body mass (men, r2 = 0.32; women, r2 = 0.22), and thus SUV and SULCT were also weakly correlated (men, r2 = 0.24; women, r2 = 0.11). Equations proved inadequate for the assessment of LBM. LBM estimated by James' equation showed a mean bias (overestimation of LBM compared with LBMCT) in men (+6.13 kg; 95% CI 4.61-7.65) and in women (+6.32 kg; 95% CI 5.26-7.39). Janmahasatian's equation provided similarly poor performance. Conclusions: CT-based LBM determinations incorporate the patient's current body composition at the time of a PET/CT study, and the information garnered can provide care teams with information with which to more accurately determine FDG uptake values, allowing comparability over multiple scans and treatment courses and will provide a robust basis for the use of PET Response Criteria in Solid Tumors (PERCIST) in clinical trials.

A local contrast based approach to threshold segmentation for PET target volume delineation.

Current radiation therapy techniques, such as intensity modulated radiation therapy and three-dimensional conformal radiotherapy rely on the precise delivery of high doses of radiation to well-defined volumes. CT, the imaging modality that is most commonly used to determine treatment volumes cannot, however, easily distinguish between cancerous and normal tissue. The ability of positron emission tomography (PET) to more readily differentiate between malignant and healthy tissues has generated great interest in using PET images to delineate target volumes for radiation treatment planning. At present the accurate geometric delineation of tumor volumes is a subject open to considerable interpretation. The possibility of using a local contrast based approach to threshold segmentation to accurately delineate PET target cross sections is investigated using well-defined cylindrical and spherical volumes. Contrast levels which yield correct volumetric quantification are found to be a function of the activity concentration ratio between target and background, target size, and slice location. Possibilities for clinical implementation are explored along with the limits posed by this form of segmentation.

The use of phase sequence image sets to reconstruct the total volume occupied by a mobile lung tumor.

The use of phase sequence image (PSI) sets to reveal the total volume occupied by a mobile target is presented. Isocontrast composite clinical target volumes (CCTVs) may be constructed from PSI sets in order to reveal the total volume occupied by a mobile target during the course of its travel. The ability of the CCTV technique to properly account for target motion is demonstrated by comparison to contours of the true total volume occupied (TVO) for a number of experimental phantom geometries. Finally, using real patient data, the clinical utility of the CCTV technique to properly account for internal tumor motion while minimizing the volume of healthy lung tissue irradiated is assessed by comparison to the standard approach of applying safety margins. Results of the phantom study reveal that CCTV cross sections constructed at the 20% isocontrast level yield good agreement with the total cross sections (TXO) of mobile targets. These CCTVs conform well to the TVOs of the moving targets examined whereby the addition of small uniform margins ensures complete circumscription of the TVO with the inclusion of minimal amounts of surrounding external volumes. The CCTV technique is seen to be clearly superior to the common practice of the addition of safety margins to individual CTV contours in order to account for internal target motion. Margins required with the CCTV technique are eight to ten times smaller than those required with individual CTVs.

The use of phase sequence image sets to reconstruct the total volume occupied by a mobile lung tumor.

The use of phase sequence image (PSI) sets to reveal the total volume occupied by a mobile target is presented. Isocontrast composite clinical target volumes (CCTVs) may be constructed from PSI sets in order to reveal the total volume occupied by a mobile target during the course of its travel. The ability of the CCTV technique to properly account for target motion is demonstrated by comparison to contours of the true total volume occupied (TVO) for a number of experimental phantom geometries. Finally, using real patient data, the clinical utility of the CCTV technique to properly account for internal tumor motion while minimizing the volume of healthy lung tissue irradiated is assessed by comparison to the standard approach of applying safety margins. Results of the phantom study reveal that CCTV cross sections constructed at the 20% isocontrast level yield good agreement with the total cross sections (TXO) of mobile targets. These CCTVs conform well to the TVOs of the moving targets examined whereby the addition of small uniform margins ensures complete circumscription of the TVO with the inclusion of minimal amounts of surrounding external volumes. The CCTV technique is seen to be clearly superior to the common practice of the addition of safety margins to individual CTV contours in order to account for internal target motion. Margins required with the CCTV technique are eight to ten times smaller than those required with individual CTVs.

The impact of tumor motion upon CT image integrity and target delineation.

Accurate planning target volume delineation is vital to the success of conformal radiation techniques such as standard three-dimensional conformal radiotherapy and intensity modulated radiation therapy. With the exception of breath-hold schemes, all current approaches acquire images while the tumor is nonstationary and, as such, are subject to the presence of motion artifacts. In lung cancer sites where tumor mobility can be significant, the detrimental effect of these motion-induced distortions on image quality and subsequently target volume delineation cannot be ignored in the pursuit of improved treatment outcomes. To investigate the fundamental nature and functional dependence of computed tomography (CT) artifacts associated with lung tumor motion, and the implications for tumor delineation, a filtered backprojection algorithm was developed in MATLAB to generate transverse CT simulation images. In addition, a three-dimensional phantom capable of mimicking the essential motions of lung tumors was constructed for experimental verification. Results show that the spatial extent of a mobile object is distorted from its true shape and location and does not accurately reflect the volume occupied during the extent of motion captured. The presence of motion also negatively impacts image intensity (density) integrity rendering accurate volume delineation highly problematic and calling into question the use of such data in CT-based heterogeneity correction algorithms for dosimetric calculation.