Repeatability of quantitative18F-FLT uptake measurements in solid tumors: an individual patient data multi-center meta-analysis

INTRODUCTION: 3'-deoxy-3'-[18F]fluorothymidine (18F-FLT) positron emission tomography (PET) provides a non-invasive method to assess cellular proliferation and response to antitumor therapy. Quantitative18F-FLT uptake metrics are being used for evaluation of proliferative response in investigational setting, however multi-center repeatability needs to be established. The aim of this study was to determine the repeatability of18F-FLT tumor uptake metrics by re-analyzing individual patient data from previously published reports using the same tumor segmentation method and repeatability metrics across cohorts. METHODS: A systematic search in PubMed, EMBASE.com and the Cochrane Library from inception-October 2016 yielded five18F-FLT repeatability cohorts in solid tumors.18F-FLT avid lesions were delineated using a 50% isocontour adapted for local background on test and retest scans. SUVmax, SUVmean, SUVpeak, proliferative volume and total lesion uptake (TLU) were calculated. Repeatability was assessed using the repeatability coefficient (RC = 1.96 × SD of test-retest differences), linear regression analysis, and the intra-class correlation coefficient (ICC). The impact of different lesion selection criteria was also evaluated. RESULTS: Images from four cohorts containing 30 patients with 52 lesions were obtained and analyzed (ten in breast cancer, nine in head and neck squamous cell carcinoma, and 33 in non-small cell lung cancer patients). A good correlation was found between test-retest data for all18F-FLT uptake metrics (R2 ≥ 0.93; ICC ≥ 0.96). Best repeatability was found for SUVpeak(RC: 23.1%), without significant differences in RC between different SUV metrics. Repeatability of proliferative volume (RC: 36.0%) and TLU (RC: 36.4%) was worse than SUV. Lesion selection methods based on SUVmax ≥ 4.0 improved the repeatability of volumetric metrics (RC: 26-28%), but did not affect the repeatability of SUV metrics. CONCLUSIONS: In multi-center studies, differences ≥ 25% in18F-FLT SUV metrics likely represent a true change in tumor uptake. Larger differences are required for FLT metrics comprising volume estimates when no lesion selection criteria are applied

Which patients benefit specifically from short-term psychodynamic psychotherapy (STPP) for depression? Study protocol of a systematic review and meta-analysis of individual participant data

INTRODUCTION: Short-term psychodynamic psychotherapy (STPP) is an empirically supported treatment that is often used to treat depression. However, it is largely unclear if certain subgroups of depressed patients can benefit specifically from this treatment method. We describe the protocol for a systematic review and meta-analysis of individual participant data (IPD) aimed at identifying predictors and moderators of STPP for depression efficacy. METHOD AND ANALYSIS: We will conduct a systematic literature search in multiple bibliographic databases (PubMed, PsycINFO, Embase.com, Web of Science, and Cochrane's Central Register of Controlled Trials), ‘grey literature’ databases (GLIN and UMI ProQuest), and a prospective trial register (http://www.controlled-trials.com). We will include studies reporting (a) outcomes on standardized measures of (b) depressed (c) adult patients (d) receiving STPP. We will next invite the authors of these studies to share the participant-level data of their trials and combine these data to conduct IPD meta-analyses. The primary outcome for this study is post-treatment efficacy as assessed by a continuous depression measure. Potential predictors and moderators include all socio-demographic variables, clinical variables, and psychological patient characteristics that are measured before the start of treatment and are assessed consistently across studies. One-stage IPD meta-analyses will be conducted using mixed effects models. ETHICS AND DISSEMINATION: IRB approval is not required for this study. We intend to submit reports of the outcomes of this study for publication to international peer-reviewed journals in the fields of psychiatry or clinical psychology. We also intend to present the outcomes at international scientific conferences aimed at psychotherapy researchers and clinicians. The findings of this study can have important clinical implications, as they can inform expectations of STPP efficacy for individual patients, and help to make an informed choice concerning the best treatment option for a given patient

Decision support systems for incurable non-small cell lung cancer: a systematic review

Abstract Background Individually tailored cancer treatment is essential to ensure optimal treatment and resource use. Treatments for incurable metastatic non-small cell lung cancer (NSCLC) are evolving rapidly, and decision support systems (DSS) for this patient population have been developed to balance benefits and harms for decision-making. The aim of this systematic review was to inventory DSS for stage IIIB/IV NSCLC patients. Methods A systematic literature search was performed in Pubmed, Embase and the Cochrane Library. DSS were described extensively, including their predictors, model performances (i.e., discriminative ability and calibration), levels of validation and user friendliness. Results The systematic search yielded 3531 articles. In total, 67 articles were included after additional reference tracking. The 39 identified DSS aim to predict overall survival and/or progression-free survival, but give no information about toxicity or cost-effectiveness. Various predictors were incorporated, such as performance status, serum and inflammatory markers, and patient and tumor characteristics. Some DSS were developed for the entire incurable NSCLC population, whereas others were specifically for patients with brain or spinal metastases. Few DSS had been validated externally using recent clinical data, and the discrimination and calibration were often poor. Conclusions Many DSS have been developed for incurable NSCLC patients, but DSS are still lacking that are up-to-date with a good model performance, while covering the entire treatment spectrum. Future DSS should incorporate genetic and biological markers based on state-of-the-art evidence, and compare multiple treatment options to estimate survival, toxicity and cost-effectiveness