Can FDG PET predict radiation treatment outcome in head and neck cancer? Results of a prospective study

Contains fulltext : 96692.pdf (publisher's version ) (Closed access)PURPOSE: In head and neck cancer (HNC) various treatment strategies have been developed to improve outcome, but selecting patients for these intensified treatments remains difficult. Therefore, identification of novel pretreatment assays to predict outcome is of interest. In HNC there are indications that pretreatment tumour (18)F-fluorodeoxyglucose (FDG) uptake may be an independent prognostic factor. The aim of this study was to assess the prognostic value of FDG uptake and CT-based and FDG PET-based primary tumour volume measurements in patients with HNC treated with (chemo)radiotherapy. METHODS: A total of 77 patients with stage II-IV HNC who were eligible for definitive (chemo)radiotherapy underwent coregistered pretreatment CT and FDG PET. The gross tumour volume of the primary tumour was determined on the CT (GTV(CT)) and FDG PET scans. Five PET segmentation methods were applied: interpreting FDG PET visually (PET(VIS)), applying an isocontour at a standardized uptake value (SUV) of 2.5 (PET(2.5)), using fixed thresholds of 40% and 50% (PET(40%), PET(50%)) of the maximum intratumoral FDG activity (SUV(MAX)) and applying an adaptive threshold based on the signal-to-background (PET(SBR)). Mean FDG uptake for each PET-based volume was recorded (SUV(mean)). Subsequently, to determine the metabolic volume, the integrated SUV was calculated as the product of PET-based volume and SUV(mean). All these variables were analysed as potential predictors of local control (LC), regional recurrence-free survival (RRFS), distant metastasis-free survival (DMFS), disease-free survival (DFS) and overall survival (OS). RESULTS: In oral cavity/oropharynx tumours PET(VIS) was the only volume-based method able to predict LC. Both PET(VIS) and GTV(CT) were able to predict DMFS, DFS and OS in these subsites. Integrated SUVs were associated with LC, DMFS, DFS and OS, while SUV(mean) and SUV(MAX) were not. In hypopharyngeal/laryngeal tumours none of the variables was associated with outcome. CONCLUSION: There is no role yet for pretreatment FDG PET as a predictor of (chemo)radiotherapy outcome in HNC in daily routine. However, this potential application needs further exploration, focusing both on FDG PET-based primary tumour volume, integrated SUV and SUV(MAX) of the primary tumour

Prognostic value of gross tumor volume delineated by FDG-PET-CT based radiotherapy treatment planning in patients with locally advanced pancreatic cancer treated with chemoradiotherapy

<p>Abstract</p> <p>Background</p> <p>We aimed to assess whether gross tumor volume (GTV) determined by fusion of contrast-enhanced computerized tomography (CT) and 18F-fluoro-deoxy-D-glucose positron emission tomography-CT (FDG-PET-CT) based radiotherapy planning could predict outcomes, namely overall survival (OS), local-regional progression-free survival (LRPFS), and progression-free survival (PFS) in cases with locally advanced pancreas cancer (LAPC) treated with definitive concurrent chemoradiotherapy.</p> <p>Methods</p> <p>A total of 30 patients with histological proof of LAPC underwent 50.4 Gy (1.8 Gy/28 fractions) of radiotherapy concurrent with continuously infused 5-FU followed by 4 to 6 courses of maintenance gemcitabine. Target volume delineations were performed on FDG-PET-CT-based RTP. Patients were stratified into 2 groups: GTV lesser (GTV_L) versus greater (GTV_G) than cut off value determined by receiver operating characteristic (ROC) analysis, and compared in terms of OS, LRPFS and PFS.</p> <p>Results</p> <p>Median GTV delineated according to the FDG-PET-CT data was 100.0 cm³. Cut off GTV value determined from ROC curves was 91.1 cm³. At a median follow up of 11.2 months, median OS, LRPFS and PFS for the entire population were 10.3, 7.8 and 5.7 months, respectively. Median OS, LRPFS and PFS for GTV_Land GTV_Gcohorts were 16.3 vs. 9.5 (<it>p </it>= 0.005), 11.0 vs. 6.0 (<it>p </it>= 0.013), and 9.0 vs. 4.8 months (<it>p </it>= 0.008), respectively.</p> <p>Conclusions</p> <p>The superior OS, LRPFS and PFS observed in GTV_Lpatients over GTV_Gones suggests a potential for FDG-PET-CT-defined GTV size in predicting outcomes of LAPC patients treated with definitive C-CRT, which needs to be validated by further studies with larger cohorts.</p

Pathology-based validation of FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer

Item does not contain fulltextPURPOSE: FDG PET is increasingly incorporated into radiation treatment planning of head and neck cancer. However, there are only limited data on the accuracy of radiotherapy target volume delineation by FDG PET. The purpose of this study was to validate FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer against the pathological method as the standard. METHODS: Twelve patients with head and neck cancer and 28 metastatic lymph nodes eligible for therapeutic neck dissection underwent preoperative FDG PET/CT. The metastatic lymph nodes were delineated on CT (NodeCT) and ten PET segmentation tools were used to assess FDG PET-based nodal volumes: interpreting FDG PET visually (PETVIS), applying an isocontour at a standardized uptake value (SUV) of 2.5 (PETSUV), two segmentation tools with a fixed threshold of 40% and 50%, and two adaptive threshold based methods. The latter four tools were applied with the primary tumour as reference and also with the lymph node itself as reference. Nodal volumes were compared with the true volume as determined by pathological examination. RESULTS: Both NodeCT and PETVIS showed good correlations with the pathological volume. PET segmentation tools using the metastatic node as reference all performed well but not better than PETVIS. The tools using the primary tumour as reference correlated poorly with pathology. PETSUV was unsatisfactory in 35% of the patients due to merging of the contours of adjacent nodes. CONCLUSION: FDG PET accurately estimates metastatic lymph node volume, but beyond the detection of lymph node metastases (staging), it has no added value over CT alone for the delineation of routine radiotherapy target volumes. If FDG PET is used in radiotherapy planning, treatment adaptation or response assessment, we recommend an automated segmentation method for purposes of reproducibility and interinstitutional comparison

Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors

Contains fulltext : 136904.pdf (publisher's version ) (Open Access)BACKGROUND: Quantification of molecular cell processes is important for prognostication and treatment individualization of head and neck cancer (HNC). However, individual tumor comparison can show discord in upregulation similarities when analyzing multiple biological mechanisms. Elaborate tumor characterization, integrating multiple pathways reflecting intrinsic and microenvironmental properties, may be beneficial to group most uniform tumors for treatment modification schemes. The goal of this study was to systematically analyze if immunohistochemical (IHC) assessment of molecular markers, involved in treatment resistance, and 18F-FDG PET parameters could accurately distinguish separate HNC tumors. METHODS: Several imaging parameters and texture features for 18F-FDG small-animal PET and immunohistochemical markers related to metabolism, hypoxia, proliferation and tumor blood perfusion were assessed within groups of BALB/c nu/nu mice xenografted with 14 human HNC models. Classification methods were used to predict tumor line based on sets of parameters. RESULTS: We found that 18F-FDG PET could not differentiate between the tumor lines. On the contrary, combined IHC parameters could accurately allocate individual tumors to the correct model. From 9 analyzed IHC parameters, a cluster of 6 random parameters already classified 70.3% correctly. Combining all PET/IHC characteristics resulted in the highest tumor line classification accuracy (81.0%; cross validation 82.0%), which was just 2.2% higher (p = 5.2x10-32) than the performance of the IHC parameter/feature based model. CONCLUSIONS: With a select set of IHC markers representing cellular processes of metabolism, proliferation, hypoxia and perfusion, one can reliably distinguish between HNC tumor lines. Addition of 18F-FDG PET improves classification accuracy of IHC to a significant yet minor degree. These results may form a basis for development of tumor characterization models for treatment allocation purposes