FAIR-compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head-Neck1 TCIA collections

Purpose: One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets — RIDER, Interobserver, Lung1, and Head–Neck1. To support repeatability, reproducibility, generalizability, and transparency in radiomics research, we publish the subjects’ clinical data, extracted radiomics features, and digital imaging and communications in medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable, and reusable (FAIR) data management principles. Acquisition and validation methods: Overall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the primary gross tumor volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open-source Ontology-Guided Radiomics Analysis Workflow (O-RAW). We reshaped the output of O-RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). Digital imaging and communications in medicine metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects’ clinical data were described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), that is, triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission. Data format: The accumulated RDF data are publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross-reference clinical, DICOM, and radiomics data within a single query, while being agnostic to the original data format and coding system. The feder

Matrix-assisted laser desorption ionization--time of flight mass spectrometry: an emerging tool for the rapid identification of mosquito vectors.

BACKGROUND: The identification of mosquito vectors is typically based on morphological characteristics using morphological keys of determination, which requires entomological expertise and training. The use of protein profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), which is increasingly being used for the routine identification of bacteria, has recently emerged for arthropod identification. METHODS: To investigate the usefulness of MALDI-TOF-MS as a mosquito identification tool, we tested protein extracts made from mosquito legs to create a database of reference spectra. The database included a total of 129 laboratory-reared and field-caught mosquito specimens consisting of 20 species, including 4 Aedes spp., 9 Anopheles spp., 4 Culex spp., Lutzia tigripes, Orthopodomyia reunionensis and Mansonia uniformis. For the validation study, blind tests were performed with 76 specimens consisting of 1 to 4 individuals per species. A cluster analysis was carried out using the MALDI-Biotyper and some spectra from all mosquito species tested. RESULTS: Biomarker mass sets containing 22 and 43 masses have been detected from 100 specimens of the Anopheles, Aedes and Culex species. By carrying out 3 blind tests, we achieved the identification of mosquito vectors at the species level, including the differentiation of An. gambiae complex, which is possible using MALDI-TOF-MS with 1.8 as the cut-off identification score. A cluster analysis performed with all available mosquito species showed that MALDI-Biotyper can distinguish between specimens at the subspecies level, as demonstrated for An gambiae M and S, but this method cannot yet be considered a reliable tool for the phylogenetic study of mosquito species. CONCLUSIONS: We confirmed that even without any specific expertise, MALDI-TOF-MS profiling of mosquito leg protein extracts can be used for the rapid identification of mosquito vectors. Therefore, MALDI-TOF-MS is an alternative, efficient and inexpensive tool that can accurately identify mosquitoes collected in the field during entomological surveys