Clinical use of PET-CT data for radiotherapy planning: What are we looking for? (original) (raw)
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Quantitative Assessment of Heterogeneity in Tumor Metabolism Using FDG-PET
International Journal of Radiation Oncology*Biology*Physics, 2012
Tumor heterogeneity was determined using dynamic [ 18 F]-fluorodeoxyglucosepositron emission tomography. Pharmacokinetic analysis was performed in three segments per lesion with increasing metabolic rate of glucose. With increasing metabolic rate, significant increases in uptake, washout and phosphorylation were observed, with decreasing tissue blood volume fractions.
Positron Emission Tomography (PET) in Oncology
Cancers, 2014
Since its introduction in the early nineties as a promising functional imaging technique in the management of neoplastic disorders, FDG-PET, and subsequently FDG-PET/CT, has become a cornerstone in several oncologic procedures such as tumor staging and restaging, treatment efficacy assessment during or after treatment end and radiotherapy planning. Moreover, the continuous technological progress of image generation and the introduction of sophisticated software to use PET scan as a biomarker paved the way to calculate new prognostic markers such as the metabolic tumor volume (MTV) and the total amount of tumor glycolysis (TLG). FDG-PET/CT proved more sensitive than contrast-enhanced CT scan in staging of several type of lymphoma or in detecting widespread tumor dissemination in several solid cancers, such as breast, lung, colon, ovary and head and neck carcinoma. As a consequence the stage of patients was upgraded, with a change of treatment in 10%-15% of them. One of the most evident advantages of FDG-PET was its ability to detect, very early during treatment, significant changes in glucose metabolism or even complete shutoff of the neoplastic cell metabolism as a surrogate of tumor chemosensitivity assessment. This could enable clinicians to detect much earlier the effectiveness of a given antineoplastic treatment, as compared to the traditional radiological detection of tumor shrinkage, which usually takes time and occurs much later.
The standard uptake value (SUV) approach in oncological positron emission tomography has known shortcomings, all of which affect the reliability of the SUV as a surrogate of the targeted quantity, the metabolic rate of [18F]fluorodeoxyglucose (FDG), Km. Among the shortcomings are time dependence, susceptibility to errors in scanner and dose calibration, insufficient correlation between systemic distribution volume and body weight, and, consequentially, residual inter-study variability of the arterial input function (AIF) despite SUV normalization. Especially the latter turns out to be a crucial factor adversely affecting the correlation between SUV and Km and causing inter-study variations of tumor SUVs that do not reflect actual changes of the metabolic uptake rate. In this work, we propose to replace tumor SUV by the tumor-to-blood standard uptake ratio (SUR) in order to distinctly improve the linear correlation with Km. Assuming irreversible FDG kinetics, SUR can be expected to exhibit a much better linear correlation to Km than SUV. The theoretical derivation for this prediction is given and evaluated in a group of nine patients with liver metastases of colorectal cancer for which 15 fully dynamic investigations were available and Km could thus be derived from conventional Patlak analysis. For any fixed time point T at sufficiently late times post injection, the Patlak equation predicts a linear correlation between SUR and Km under the following assumptions: (1) approximate shape invariance (but arbitrary scale) of the AIF across scans/patients and (2) low variability of the apparent distribution volume Vr (the intercept of the Patlak Plot). This prediction - and validity of the underlying assumptions - has been verified in the investigated patient group. Replacing tumor SUVs by SURs does improve the linear correlation of the respective parameter with Km from r = 0.61 to r = 0.98. SUR is an easily measurable parameter that is highly correlated to Km. In this respect, it is clearly superior to SUV. Therefore, SUR should be seriously considered as a drop-in replacement for SUV-based approaches.
Quantification of serial tumor glucose metabolism
PubMed, 1996
We developed a method to improve the quantitative precision of FDG-PET scans in cancer patients. The total-lesion evaluation method generates a correlation coefficient (r) constrained Patlak parametric image of the lesion together with three calculated glucose metabolic indices: (a) the total-lesion metabolic index ("KT-tle", ml/min/lesion); (b) the total-lesion voxel index ("VT-tle", voxels/lesion); and (c) the global average metabolic index ("KV-tle", ml/min/voxel). Methods: The glucose metabolic indices obtained from conventional region of interest (ROI) and multiplane evaluation were used as standards to evaluate the accuracy of the total-lesion evaluation method. Computer simulations and four patients with metastatic melanoma before and after chemotherapy were studied. Results: Computer simulations showed that the total-lesion evaluation method has improved precision (% s.d. < 0.6%) and accuracy (approximately 10% error) compared with the conventional ROI method (% s.d. approximately 5%; approximately 25% error). The KT-tle and VT-tle indices from human FDG-PET studies using the total-lesion evaluation method showed excellent correlations with the corresponding values obtained from the conventional ROI methods and multiplane evaluation (r approximately 1.0) and CT lesion volume measurements. Conclusion: This method is a simple but reliable way to quantitatively monitor tumor FDG uptake. The method has several advantages over the conventional ROI method: (a) less sensitive to the ROI definition, (b) no need for image registration of serial scan data and (c) includes tumor volume changes in the global tumor metabolism.
Clinical Positron Imaging, 1999
Functional" tumor treatment response parameters have been developed to measure treatment induced biochemical changes in the entire tumor mass, using positron emission tomography (PET) and [F-18] fludeoxyglucose (FDG). These new parameters are intended to measure global changes in tumor glycolysis. The response parameters are determined by comparing the pre-and posttreatment PET-FDG images either visually from the change in image appearance in the region of the tumor, or quantitatively based on features of the calibrated digital PET image. The visually assessed parameters are expressed as a visual response score (VRS), or visual response index (VRI), as the estimated percent response of the tumor. Visual Response Score (VRS) is recorded on a 5 point response scale (0-4): 0: no response or progression; 1: 1-33%; 2: Ͼ33%-66%; 3: Ͼ66%-99%; and 4: Ͼ99%, estimated response, respectively. The quantitative changes are expressed as total lesion glycolysis TLG or as the change in TLG during treatment, also called ␦TLG or Larson-Ginsberg Index (LGI), expressed as percent response. The volume of the lesion is determined from the PET-FDG images by an adaptive thresholding technique. This response index is computed as, ␦TLG (LGI) ϭ {[(SUV ave ) 1 * (Vol) 1 -(SUV ave ) 2 * (Vol) 2 ]/[(SUV ave ) 1 * (Vol) 1 ]} * 100. Where "1" and "2" denote the pre-and posttreatment PET-FDG, scans respectively. Pre-and posttreatment PET-FDG scans were performed on a group of 41 locally advanced lung (2), rectal (17), esophageal (16) and gastric (6) cancers. These patients were treated before surgery with neoadjuvant chemo-radiation. Four experienced PET readers determined individual VRS and VRI blinded to each other as well as to the clinical history. Consensus VRS was obtained based on a discussion. The interobserver variability captured by intraclass correlation coefficient was 89.7%. In addition, reader reliability was assessed for the categorized VRS using Kendall's coefficient of concordance for ordinal data and was found to be equal to 85% This provided assurance that these response parameters were highly reproducible. The correlation of ␦TLG with % change in SUV ave and % change in SUV max , as widely used parameters of response, were 0.73 and 0.78 (P Ͻ .0001) respectively. The corresponding correlation of VRI were 0.63 and 0.64 (P Ͻ .0001) respectively. Both ␦TLG and VRI showed greater mean changes than SUV maximum or average (59.7% and 76% vs. 46.9% and 46.8%). We conclude that VRS and ␦TLG are substantially correlated with other response parameters and are highly reproducible. As global measures of metabolic response, VRS, VRI and ␦TLG (LGI) should provide complementary information to more commonly used PET response parameters like the metabolic rate of FDG (MRFDG), or the standardized uptake value (SUV), that are calculated as normalized per gram of tumor. These findings set the stage for validation studies of the VRS and ␦TLG as objective measures of clinical treatment response, through comparison to the appropriate gold standards of posttreatment histopathology, recurrence free survival, and disease specific survival in well characterized populations of patients with locally advanced cancers. (Clin Pos Imag 1999;2:159-171)
FDG-PET Determination of Metabolically Active Tumor Volume and Comparison with CT
Clinical Positron Imaging, 1998
Purpose: To determine if tumor volume, in addition to tumor metabolic activity, can be assessed noninvasively from attenuation-corrected fluorodeoxyglucose (FDG)-PET imaging using a semiautomated method. Methods: CT and FDG-PET scanning was performed in 14 patients, eight with newly diagnosed untreated malignancies, and six patients with progressive non-Hodgkin's lymphoma (NHL). Tumor volume was determined from CT scans by summation of manually drawn regions of interest over tumor. Tumor volume was determined at FDG-PET with a semiautomated method based on quantitation of 18 F uptake and thresholding. Results: Mean tumor volume was 187 Ϯ 189 cm 3. Tumor volume determined by means of PET and CT was strongly correlated (r ϭ 0.98, P Ͻ 0.001, N ϭ 8) in the patients with untreated tumors. Correlation was weaker (r ϭ 0.70, P ϭ 0.006, N ϭ 14) for all patients, mainly due to one previously treated patient with a large disparity between CT and metabolically active tumor volumes at FDG-PET, presumably due to tumor necrosis. Conclusions: Tumor volume determination by FDG-PET was strongly correlated with tumor volumes determined by anatomic imaging with CT. FDG-PET appears comparable to CT in measuring untreated tumor volumes of this size. FDG-PET may be superior to anatomic techniques in assessing metabolically active tumor volume, and warrants further study in this role.