Individual patient data meta-analysis of FMISO and FAZA hypoxia PET scans from head and neck cancer patients undergoing definitive radio-chemotherapy.

BACKGROUND AND PURPOSE: Tumor hypoxia plays an important role in head and neck squamous cell carcinomas (HNSCC). Various positron emission tomography (PET) tracers promise non-invasive assessment of tumor hypoxia. So far, the applicability of hypoxia PET is hampered by monocentric imaging trials with few patients. MATERIALS AND METHODS: Multicenter individual patient data based meta-analysis of the original PET data from four prospective imaging trials was performed. All patients had localized disease and were treated with curatively intended radio(-chemo)therapy. Hypoxia PET imaging was performed with 18F-Fluoromisonidazole (FMISO, 102 patients) or 18F-Fluoroazomycin-arabinoside (FAZA, 51 patients). Impact of hypoxia PET parameters on loco-regional control (LRC) and overall survival (OS) was analyzed by uni- and multivariable Cox regression. RESULTS: Baseline characteristics between participating centers differed significantly, especially regarding T stage (p < 0.001), tumor volume (p < 0.001) and p16 status (p = 0.009). The commonly used hypoxia parameters, maximal tumor-to-muscle ratio (TMRmax) and hypoxic volume with 1.6 threshold (HV1.6), showed a strong association with LRC (p = 0.001) and OS (p < 0.001). These findings were irrespective of the radiotracer and the same cut-off values could be applied for FMISO and FAZA (TMRmax > 2.0 or HV1.6 > 1.5 ml). The effect size of TMRmax was similar for subgroups of patients defined by radiotracer, p16 status and FDG-PET parameters for LRC and OS, respectively. CONCLUSION: PET measured hypoxia is robust and has a strong impact on LRC and OS in HNSCC. The most commonly investigated tracers FMISO and FAZA can probably be used equivalently in multicenter trials. Optimal strategies to improve the dismal outcome of hypoxic tumors remain elusive.

Implementation of a software for REmote COMparison of PARticlE and photon treatment plans: ReCompare.

PURPOSE: To guarantee equal access to optimal radiotherapy, a concept of patient assignment to photon or particle radiotherapy using remote treatment plan exchange and comparison - ReCompare - was proposed. We demonstrate the implementation of this concept and present its clinical applicability. MATERIALS AND METHODS: The ReCompare concept was implemented using a client-server based software solution. A clinical workflow for the remote treatment plan exchange and comparison was defined. The steps required by the user and performed by the software for a complete plan transfer were described and an additional module for dose-response modeling was added. RESULTS: The ReCompare software was successfully tested in cooperation with three external partner clinics and worked meeting all required specifications. It was compatible with several standard treatment planning systems, ensured patient data protection, and integrated in the clinical workflow. CONCLUSION: The ReCompare software can be applied to support non-particle radiotherapy institutions with the patient-specific treatment decision on the optimal irradiation modality by remote treatment plan exchange and comparison.

Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets.

Disconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within "Big Data". Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology.