PURPOSE: The impact of quantitative parameters on the differentiation of primary colorectal tumours from normal colon tissue was assessed. Dynamic PET data (DPET) were acquired, and compartment and non-compartment modelling applied. The discriminant power of single parameters and the combination of PET parameters was assessed. All lesions were confirmed by histology. METHODS: FDG DPET studies were acquired in 22 patients with colorectal tumours prior to surgery. Five of these patients also had liver metastases at the time of the PET study. The SUV 56-60 min p.i. was included in the evaluation. A two-tissue compartment model was applied and the parameters k1-k4 as well as the fractional blood volume (VB) were obtained. The FDG influx was calculated from the compartment data. Non-compartment modelling was used to calculate the fractal dimension (FD) of the time-activity data. RESULTS: FD, SUV, influx and k3 were the most important single parameters for lesion differentiation. The highest accuracy was achieved for FD (88.78%). The overall tracer uptake was mainly dependent on k3 and not on k1 or VB. The support vector machines (SVM) algorithm was used to predict the classification based on the combination of individual PET parameters. The overall accuracy was 97.3%, with only one false positive case and no false negative results. The analysis of the subgroup of five patients with primary tumours and synchronous metastases revealed no significant differences for the individual PET parameters. However, VB tended to be lower while k1 and k2 were higher in patients with synchronous metastases. The SVM classification analysis predicted the presence of metastases based on the PET data of the primary tumour in three of five patients. CONCLUSION: Quantitative FDG PET studies provide very accurate data for the differentiation of primary colorectal tumours from normal tissue. The use of quantitative data has the advantage that the detection of a colorectal tumour is not primarily dependent on the individual assessment and experience of the physician evaluating the FDG PET data only visually. The results suggest that the presence of metastatic lesions may be predicted by analysis of the dynamic PET data of the corresponding primary tumour. Further studies are needed to assess this aspect in detail.
PURPOSE: The impact of quantitative parameters on the differentiation of primary colorectal tumours from normal colon tissue was assessed. Dynamic PET data (DPET) were acquired, and compartment and non-compartment modelling applied. The discriminant power of single parameters and the combination of PET parameters was assessed. All lesions were confirmed by histology. METHODS: FDG DPET studies were acquired in 22 patients with colorectal tumours prior to surgery. Five of these patients also had liver metastases at the time of the PET study. The SUV 56-60 min p.i. was included in the evaluation. A two-tissue compartment model was applied and the parameters k1-k4 as well as the fractional blood volume (VB) were obtained. The FDG influx was calculated from the compartment data. Non-compartment modelling was used to calculate the fractal dimension (FD) of the time-activity data. RESULTS: FD, SUV, influx and k3 were the most important single parameters for lesion differentiation. The highest accuracy was achieved for FD (88.78%). The overall tracer uptake was mainly dependent on k3 and not on k1 or VB. The support vector machines (SVM) algorithm was used to predict the classification based on the combination of individual PET parameters. The overall accuracy was 97.3%, with only one false positive case and no false negative results. The analysis of the subgroup of five patients with primary tumours and synchronous metastases revealed no significant differences for the individual PET parameters. However, VB tended to be lower while k1 and k2 were higher in patients with synchronous metastases. The SVM classification analysis predicted the presence of metastases based on the PET data of the primary tumour in three of five patients. CONCLUSION: Quantitative FDG PET studies provide very accurate data for the differentiation of primary colorectal tumours from normal tissue. The use of quantitative data has the advantage that the detection of a colorectal tumour is not primarily dependent on the individual assessment and experience of the physician evaluating the FDG PET data only visually. The results suggest that the presence of metastatic lesions may be predicted by analysis of the dynamic PET data of the corresponding primary tumour. Further studies are needed to assess this aspect in detail.
Authors: Liyang Wei; Yongyi Yang; Robert M Nishikawa; Miles N Wernick; Alexandra Edwards Journal: IEEE Trans Med Imaging Date: 2005-10 Impact factor: 10.048
Authors: T Ohtake; N Kosaka; T Watanabe; I Yokoyama; T Moritan; M Masuo; M Iizuka; K Kozeni; T Momose; S Oku Journal: J Nucl Med Date: 1991-07 Impact factor: 10.057
Authors: Antonia Dimitrakopoulou-Strauss; Vassilios Georgoulias; Michael Eisenhut; Felix Herth; Sophia Koukouraki; Helmut R Mäcke; Uwe Haberkorn; Ludwig G Strauss Journal: Eur J Nucl Med Mol Imaging Date: 2006-03-29 Impact factor: 9.236
Authors: A Dimitrakopoulou-Strauss; L G Strauss; M Schwarzbach; C Burger; T Heichel; F Willeke; G Mechtersheimer; T Lehnert Journal: J Nucl Med Date: 2001-05 Impact factor: 10.057
Authors: Arman Rahmim; Martin A Lodge; Nicolas A Karakatsanis; Vladimir Y Panin; Yun Zhou; Alan McMillan; Steve Cho; Habib Zaidi; Michael E Casey; Richard L Wahl Journal: Eur J Nucl Med Mol Imaging Date: 2018-09-29 Impact factor: 9.236
Authors: Christos Sachpekidis; Christian Thieke; Vasileios Askoxylakis; Nils H Nicolay; Peter E Huber; Michael Thomas; Georgia Dimitrakopoulou; Juergen Debus; Uwe Haberkorn; Antonia Dimitrakopoulou-Strauss Journal: Am J Nucl Med Mol Imaging Date: 2015-01-15