David Mönnich1,2, Stefan Welz3, Daniela Thorwarth1, Christina Pfannenberg4, Gerald Reischl5, Paul-Stefan Mauz6, Konstantin Nikolaou4, Christian la Fougère7, Daniel Zips3. 1. a Section for Biomedical Physics, Department of Radiation Oncology , Eberhard Karls University Tübingen , Germany. 2. b German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) , Heidelberg , Germany. 3. c Department of Radiation Oncology , Eberhard Karls University Tübingen , Germany. 4. d Department of Diagnostic and Interventional Radiology , Eberhard Karls University Tübingen , Germany. 5. e Department of Preclinical Imaging and Radiopharmacy , Eberhard Karls University Tübingen , Germany. 6. f Department of Otorhinolaryngology , Head and Neck Surgery, Eberhard Karls University Tübingen , Germany. 7. g Department of Nuclear Medicine , Eberhard Karls University Tübingen , Germany.
Abstract
BACKGROUND: Previous studies suggested the maximum tumor to background ratio (TBRmax) in FMISO PET images as a potentially predictive parameter for local control after radio-chemotherapy (CRT) in head and neck squamous cell carcinomas (HNSCC). However, different TBRmax thresholds for stratification were reported, implying that a common threshold cannot readily be used among different institutions without the risk of reducing prediction accuracy. Therefore, this study investigated the robustness of using a common pre-defined TBRmax, simulating a multicenter clinical trial. MATERIAL AND METHODS: FMISO PET/CT was performed four hours post-injection in 22 patients with advanced HNSCC in a phase II FMISO dose escalation study. PET background regions of interest (ROIs) were manually defined in deep neck muscles. TBRmax was calculated as the mean of the highest-valued voxels within the high risk RT planning target volume. Its predictive power with respect to local control was tested, classifying patients using median TBRmax as threshold. The influence of systematically varying quantification between institutions was studied in silico by applying offsets of ± 10% and ± 20% to the TBRmax of all patients, while the threshold remained constant. The effect was analyzed using a receiver operating characteristic (ROC). True positive and false positive rates (TPR/FPR) as well as positive and negative predictive values (PPV/NPV) were evaluated. RESULTS: For the reference condition without an offset the median TBRmax was 2.0 (1.4-3.5). Patients were classified using this threshold and TPR = 0.7, FPR = 0.4, PPV = 0.5 and NPV = 0.8 were observed. Accuracy declined with increasing offsets. Negative offsets of -10% and -20% resulted in TPR = 0.43 and 0.14, FPR = 0.20 and 0.13, PPV = 0.50 and 0.33 and NPV = 0.75 and 0.68, respectively. Positive offsets of + 10% and + 20% resulted in TPR = 1.00 and 1.00, FPR = 0.53 and 0.67, PPV = 0.47 and 0.41 and NPV = 1.00 and 1.00, respectively. CONCLUSIONS: Using a common pre-defined TBRmax threshold in multicenter trials requires careful standardization and harmonization of all steps from patient preparation to image analysis. Our results indicate that TBRmax should deviate less than 10% from reference conditions (absolute value in this dataset ± 0.2). This conclusion likely applies to all low contrast nitroimidazole hypoxia PET tracers.
BACKGROUND: Previous studies suggested the maximum tumor to background ratio (TBRmax) in FMISO PET images as a potentially predictive parameter for local control after radio-chemotherapy (CRT) in head and neck squamous cell carcinomas (HNSCC). However, different TBRmax thresholds for stratification were reported, implying that a common threshold cannot readily be used among different institutions without the risk of reducing prediction accuracy. Therefore, this study investigated the robustness of using a common pre-defined TBRmax, simulating a multicenter clinical trial. MATERIAL AND METHODS: FMISO PET/CT was performed four hours post-injection in 22 patients with advanced HNSCC in a phase II FMISO dose escalation study. PET background regions of interest (ROIs) were manually defined in deep neck muscles. TBRmax was calculated as the mean of the highest-valued voxels within the high risk RT planning target volume. Its predictive power with respect to local control was tested, classifying patients using median TBRmax as threshold. The influence of systematically varying quantification between institutions was studied in silico by applying offsets of ± 10% and ± 20% to the TBRmax of all patients, while the threshold remained constant. The effect was analyzed using a receiver operating characteristic (ROC). True positive and false positive rates (TPR/FPR) as well as positive and negative predictive values (PPV/NPV) were evaluated. RESULTS: For the reference condition without an offset the median TBRmax was 2.0 (1.4-3.5). Patients were classified using this threshold and TPR = 0.7, FPR = 0.4, PPV = 0.5 and NPV = 0.8 were observed. Accuracy declined with increasing offsets. Negative offsets of -10% and -20% resulted in TPR = 0.43 and 0.14, FPR = 0.20 and 0.13, PPV = 0.50 and 0.33 and NPV = 0.75 and 0.68, respectively. Positive offsets of + 10% and + 20% resulted in TPR = 1.00 and 1.00, FPR = 0.53 and 0.67, PPV = 0.47 and 0.41 and NPV = 1.00 and 1.00, respectively. CONCLUSIONS: Using a common pre-defined TBRmax threshold in multicenter trials requires careful standardization and harmonization of all steps from patient preparation to image analysis. Our results indicate that TBRmax should deviate less than 10% from reference conditions (absolute value in this dataset ± 0.2). This conclusion likely applies to all low contrast nitroimidazolehypoxia PET tracers.
Authors: Daniela Thorwarth; Stefan Welz; David Mönnich; Christina Pfannenberg; Konstantin Nikolaou; Matthias Reimold; Christian La Fougère; Gerald Reischl; Paul-Stefan Mauz; Frank Paulsen; Markus Alber; Claus Belka; Daniel Zips Journal: J Nucl Med Date: 2019-05-10 Impact factor: 10.057
Authors: Jairo A Socarrás Fernández; David Mönnich; Sara Leibfarth; Stefan Welz; Alex Zwanenburg; Stefan Leger; Steffen Löck; Christina Pfannenberg; Christian La Fougère; Gerald Reischl; Michael Baumann; Daniel Zips; Daniela Thorwarth Journal: Phys Imaging Radiat Oncol Date: 2020-07
Authors: A Sörensen; M Carles; H Bunea; L Majerus; C Stoykow; N H Nicolay; N E Wiedenmann; P Vaupel; P T Meyer; A L Grosu; M Mix Journal: Eur J Nucl Med Mol Imaging Date: 2019-11-26 Impact factor: 9.236
Authors: Tanuj Puri; Tessa A Greenhalgh; James M Wilson; Jamie Franklin; Lia Mun Wang; Victoria Strauss; Chris Cunningham; Mike Partridge; Tim Maughan Journal: EJNMMI Res Date: 2017-09-20 Impact factor: 3.138