Joan Fledelius1, Anne Winther-Larsen2, Azza A Khalil3, Catharina M Bylov4, Karin Hjorthaug5, Aksel Bertelsen6, Jørgen Frøkiær5, Peter Meldgaard3. 1. Department of Nuclear Medicine, Herning Regional Hospital, Herning, Denmark joan.fledelius@rm.dk. 2. Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark. 3. Department of Oncology, Aarhus University Hospital, Aarhus, Denmark. 4. Department of Radiology, Aarhus University Hospital, Aarhus, Denmark; and. 5. Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark. 6. Department of Nuclear Medicine, Herning Regional Hospital, Herning, Denmark.
Abstract
The purpose of this study was to determine which method for early response evaluation with 18F-FDG PET/CT performed most optimally for the prediction of response on a later CT scan in erlotinib-treated non-small cell lung cancer patients. Methods: 18F-FDG PET/CT scans were obtained before and after 7-10 d of erlotinib treatment in 50 non-small cell lung cancer patients. The scans were evaluated using a qualitative approach and various semiquantitative methods including percentage change in SUVs, lean body mass-corrected (SUL) SULpeak, SULmax, and total lesion glycolysis (TLG). The PET parameters and their corresponding response categories were compared with the percentage change in the sum of the longest diameter in target lesions and the resulting response categories from a CT scan obtained after 9-11 wk of erlotinib treatment using receiver-operating-characteristic analysis, linear regression, and quadratic-weighted κ. Results: TLG delineation according to the PERCIST showed the strongest correlation to sum of the longest diameter (R = 0.564, P < 0.001), compared with SULmax (R = 0.298, P = 0.039) and SULpeak (R = 0.402, P = 0.005). For predicting progression on CT, receiver-operating-characteristic analysis showed area under the curves between 0.79 and 0.92, with the highest area under the curve of 0.92 (95% confidence interval [CI], 0.84-1.00) found for TLG (PERCIST). Furthermore, the use of a cutoff of 25% change in TLG (PERCIST) for both partial metabolic response and progressive metabolic disease, which is the best predictor of the CT response categories, showed a κ-value of 0.53 (95% CI, 0.31-0.75). This method identifies 41% of the later progressive diseases on CT, with no false-positives. Visual evaluation correctly categorized 50%, with a κ-value of 0.47 (95% CI, 0.24-0.70). Conclusion: TLG (PERCIST) was the optimal predictor of response on later CT scans, outperforming both SULpeak and SULmax The use of TLG (PERCIST) with a 25% cutoff after 1-2 wk of treatment allows us to safely identify 41% of the patients who will not benefit from erlotinib and stop the treatment at this time.
The purpose of this study was to determine which method for early response evaluation with 18F-FDG PET/CT performed most optimally for the prediction of response on a later CT scan in erlotinib-treated non-small cell lung cancerpatients. Methods: 18F-FDG PET/CT scans were obtained before and after 7-10 d of erlotinib treatment in 50 non-small cell lung cancerpatients. The scans were evaluated using a qualitative approach and various semiquantitative methods including percentage change in SUVs, lean body mass-corrected (SUL) SULpeak, SULmax, and total lesion glycolysis (TLG). The PET parameters and their corresponding response categories were compared with the percentage change in the sum of the longest diameter in target lesions and the resulting response categories from a CT scan obtained after 9-11 wk of erlotinib treatment using receiver-operating-characteristic analysis, linear regression, and quadratic-weighted κ. Results:TLG delineation according to the PERCIST showed the strongest correlation to sum of the longest diameter (R = 0.564, P < 0.001), compared with SULmax (R = 0.298, P = 0.039) and SULpeak (R = 0.402, P = 0.005). For predicting progression on CT, receiver-operating-characteristic analysis showed area under the curves between 0.79 and 0.92, with the highest area under the curve of 0.92 (95% confidence interval [CI], 0.84-1.00) found for TLG (PERCIST). Furthermore, the use of a cutoff of 25% change in TLG (PERCIST) for both partial metabolic response and progressive metabolic disease, which is the best predictor of the CT response categories, showed a κ-value of 0.53 (95% CI, 0.31-0.75). This method identifies 41% of the later progressive diseases on CT, with no false-positives. Visual evaluation correctly categorized 50%, with a κ-value of 0.47 (95% CI, 0.24-0.70). Conclusion:TLG (PERCIST) was the optimal predictor of response on later CT scans, outperforming both SULpeak and SULmax The use of TLG (PERCIST) with a 25% cutoff after 1-2 wk of treatment allows us to safely identify 41% of the patients who will not benefit from erlotinib and stop the treatment at this time.
Authors: Joan Fledelius; Anne Winther-Larsen; Azza A Khalil; Karin Hjorthaug; Jørgen Frøkiær; Peter Meldgaard Journal: Am J Nucl Med Mol Imaging Date: 2018-02-05
Authors: Gábor Opposits; Marianna Nagy; Zoltán Barta; Csaba Aranyi; Dániel Szabó; Attila Makai; Imre Varga; László Galuska; Lajos Trón; László Balkay; Miklós Emri Journal: EJNMMI Res Date: 2021-07-26 Impact factor: 3.138