nsclc radiomics interobserver1

The first cohort will be treated with a dose of 35 Gy in 10 fractions, the second cohort will be treated with a dose of 40 Gy in 10 fractions, the third cohort will be treated with a dose of 45 Gy in 10 fractions, the fourth cohort will be treated with a dose of 50 Gy in 10 fractions. J Clin Oncol. It is likely that persons working independently could apply the same ontologies but produce quite different (and potentially incompatible) knowledge representations. In semantic data circles, this is well‐known as the “open‐world” paradigm that is commonly expressed as “anyone can say anything about anything.”. Radiomics is the high-throughput extraction and analysis of quantitative image features. GTV radiomics features were extracted using the open‐source Ontology‐Guided Radiomics Analysis Workflow (O‐RAW). NOTE. Semantic ontologies23 were developed in order to add descriptive metadata and hierarchical relationships on top of the data. Role of the project is … 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. 60. A systematic review. Ontologies make explicit the formal meaning of concepts within its proscribed domain and the essential relationships between its set of concepts. Pixel data only formats such as Neuroimaging Informatics Technology Initiative (NIfTI) and Nearly Raw Raster Data (NRRD) may be more intuitive for direct computation, but these have been stripped of imaging metadata. TCIA is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download. For convenience, you can also obtain the publications specifically based on TCIA in Endnote XML format: Pubs_basedon_TCIA.xml. Lastly, we were able to exploit the intrinsically federated pattern matching nature of SPARQL queries to show how to efficiently cross‐reference data from across the clinical, DICOM header, and radiomics domains. The most common types of NSCLC are squamous cell carcinoma, large cell carcinoma, and adenocarcinoma, but there are several other types that occur less frequently, and all types can occur in unusual histologic variants. Data interoperability and reusability are supported by referencing existing public ontologies. The results of a radiomics extraction software application (in our case O‐RAW, but the same holds for other software) must first be transferred into a set of inter‐related tables needed for the IBSI. Use the link below to share a full-text version of this article with your friends and colleagues. We have provided examples of SPARQL queries, primarily as a form of guidance notes on how to use this data submission. 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. Assessment of precision irradiation in early non-small cell lung cancer and interstitial lung disease (ASPIRE-ILD): study protocol for a phase II trial. Reviews There are no … <>stream The workflow of the conversion of clinical data, DICOM metadata, and radiomics features to RDF triples is represented in Fig. The aim of our study was to evaluate the semantic MRI-R parameters of NSCLC brain metastases and their correlation with ALK status. x�c`@ ��V���R�U1�����*��F���~b�o�D�'& ��_*&!�V�R L�� This would be very useful for mapping prospective data, but it is less clear how such rigid standards should be applied to legacy data and retrospective studies. The ROI called, This collection consists of radiotherapy dosimetry planning CT scans of 137 subjects with either laryngeal or oropharyngeal cancer treated by conventionally fractionated (chemo)‐radiotherapy at a single Dutch center. x�c`@ ��V���R�U1�����*��F���~b�o�D�'& ��_*&!�V�R L�� The reusability of the datasets is strongly supported by the usage of publicly available ontologies, such that the reader is able to look up the ontologies online to search for concepts of interest to them. Explore key issues in the management of advanced NSCLC in this educational program designed to keep you abreast of rapidly evolving treatment practices. About 60% of NSCLC are unresectable at diagnosis, hence, the poor prognosis – ten to twelve months survival when treated with platinum-based chemotherapy. The data are organized as “collections”; typically patients’ imaging related by a common disease (e.g. We converted and published clinical data, radiomics features and DICOM headers as online RDF from these four datasets using ontologies and standard web technology. The present work reuses the Radiation Oncology Ontology (ROO),24 Semantic DICOM ontology (SeDI),25 and the radiomics ontology (RO).26 These ontologies themselves reuse existing terminologies and thesauri, such as the image biomarker standardization initiative (IBSI),27 National Cancer Institute Thesaurus (NCIT),28 the units of measurement ontology (UO),29 and the DICOM data dictionary,30 to identify its concepts. endobj Number of times cited according to CrossRef: Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA), https://gitlab.com/UM‐CDS/FAIR‐compliant_clinical_radiomics_and_DICOM_metadata, https://doi.org/10.1148/radiol.2016152234, https://doi.org/10.7937/K9/TCIA.2015.U1X8A5NR, https://doi.org/10.7937/tcia.2019.cwvlpd26, https://doi.org/10.7937/tcia.2019.8kap372n, https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI, http://bioportal.bioontology.org/ontologies/SEDI, https://bioportal.bioontology.org/ontologies/RO, https://bioportal.bioontology.org/ontologies/NCIT, https://bioportal.bioontology.org/ontologies/UO, http://dicom.nema.org/medical/dicom/current/output/html/part06.html, https://bioportal.bioontology.org/ontologies/RO/?p=classes&conceptid=http%3A%2F%2Fwww.radiomics.org%2FRO%2FY1RO, https://jena.apache.org/tutorials/sparql.html. Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, 6525 GC The Netherlands, Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, 3015 GD The Netherlands, Department of Medical Informatics, Erasmus Medical Center, Rotterdam, 3015 GD The Netherlands, SOHARD Software GmbH, Fuerth, 90766 Germany, Artificial Intelligence in Medicine (AIM) Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115 United States, Radiology and Nuclear Medicine, CARIM & GROW School for Oncology, Maastricht University, Maastricht, 6211 LK The Netherlands, [Correction added on September 3, 2020, after first online publication: The referenced URL have been corrected.]. Clinical radiological imaging, such as computed tomography (CT), is a mainstay modality for diagnosis, screening, intervention planning, and follow‐up for cancer patients worldwide.1 Radiomics refers to high‐throughput automated characterization of the tumor phenotype by analyzing quantitative features derived from a radiological image.2 Aerts et al. 414 Views . Imaging metadata is the essential context to understand why radiomics features from different scanners may or may not be reproducible.17-20 Software libraries are available that easily change from DICOM to NIfTI/NRRD,21 but in keeping with FAIR (Findable, Accessible, Interoperable, and Reusable) data stewardship principles,22 the imaging metadata needs to be preserved in such a way that links to the source images and postacquisition analyses will be retained. The IVI-NSCLC(egfr+) model is accessible to both technical and non-technical end-users and al-lows them evaluating the impact of uncertainty in clinical evidence, alternative model structures, the decision framework of choice (i.e. This semantic representation of imaging metadata supports cross‐referenced queries of DICOM tags against radiomics features for use in repeatability and reproducibility studies.34. ���g1ނX�5t����Lf���t�p-���5�9x��e Ȟ ����q�->��s����FF_�8����n^������Ͻ���||^>m�5Z� �������]�|�g8 For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care … Other advantages of ontologies include knowledge representation and the support for automated logical inferencing. The clinical trials on this list are for non-small cell lung cancer treatment. 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. Dose-escalation in RTOG 73-01 established 60 Gy in 2 Gy/fx as the standard regimen The ROI called. 60. ���g1ނX�5t����Lf���t�p-���5�9x��e Ȟ ����q�->��s����FF_�8����n^������Ͻ���||^>m�5Z� �������]�|�g8 Additionally, afatinib is indicated for first-line use in metastatic … Radiomics on baseline CT scans may be able to predict early response to first-line platinum-based chemotherapy in patients with advanced … The ontologies further apply some level of knowledge representation that follows in the tracks of human logic and inferencing, such that we can use machine‐based queries to discover and process data, without having to first develop extensive knowledge of the relational database structure of the original data. We have tried to streamline the process in the current submission by preparing mapping files as templates and, wherever possible, using scripting to control serialization applications such as R2RML. Learn more. comment. Publishing tables of values as open access data does not by itself comply with FAIR principles, because there may be no metadata that richly describe what the data fields are, what its contents signify, and how it relates to other data. Patients were eligible for inclusion if they were at least 18 years old with T1T2N0M0 NSCLC and good performance status, and had adequate pulmonary reserve to … lung cancer), image modality (MRI, CT, etc) or research focus. We have demonstrated the realizability of this approach of making the combined data available as FAIR data, in order to incentivize multicenter research into reproducibility of radiomics features across multiple datasets. Machine‐based data mining and inferencing tasks are thus feasible in a highly efficient manner, being simplified to a “pattern matching” problem. Stage II-III NSCLC. BMC Cancer. The data are organized as “Collections”, typically patients … 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). Assessment of precision irradiation in early non-small cell lung cancer and interstitial lung disease (ASPIRE-ILD): study protocol for a phase II trial. NSCLC is any type of epithelial lung cancer other than small cell lung cancer (SCLC). Head-Neck-Radiomics-HN1 T HNC 137 CT, PT, RTSTRUCT, SEG Aerts. OMICS International is currently managing more than 400 Open Access journals with quality peer review and copyediting process. 4 0 obj endobj 60. If you do not receive an email within 10 minutes, your email address may not be registered, endstream Gain expert insights on new developments as they emerge with interactive modules, short expert commentaries, and an Interactive Decision Support Tool that will let you compare your treatment choices for specific patient scenarios … NSCLC‐Radiomics‐Interobserver1 : This collection consists of radiotherapy dosimetry planning CT scans of 22 NSCLC subjects treated by conventionally fractionated external beam radiotherapy at a single Dutch center. In the past few years, anaplastic lymphoma kinase (ALK) rearrangements, either inversions or translocations, were identified as driver mutations in non–small-cell lung cancer (NSCLC).1, 2 An echinoderm microtubule-associated protein like 4 (EML4)-ALK fusion is the most common mutation and was first identified by Soda et al in 2007. Dose-escalation in RTOG 73-01 established 60 Gy in 2 Gy/fx as the standard regimen The patients LC-Radiomics. Details of radiomics ontology development and its integration with the IBSI exceed the scope of this data article, but will be covered in detail in a separate publication.37 Radiomics RDF triples were saved to the same aforementioned SPARQL endpoint. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. NSCLC-Radiomics-Genomics Identifier-ark ark:/13960/t2k70pv3z Scanner Internet Archive HTML5 Uploader 1.6.3. plus-circle Add Review. There is also strong research activity toward stricter standardization of data collection and top‐down imposition of knowledge representation. RTOG 98-01 (1998-2002) . All of the above were published in Resource Descriptor Format (RDF), that is, triples. Non–small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers. In the original TCIA submission, some ROIs were vertically displaced due to the how treatment couch offsets were being reported by legacy radiotherapy treatment planning software – these have now been corrected. 6 0 obj All trials on the list are supported by NCI.. NCI’s basic information about clinical trials explains the types and phases of trials and how they are carried out. endobj Electronic mail: [email protected] Histologically, NSCLC is divided into adenocarcinoma, squamous cell carcinoma (SCC) (see the image below), and large cell carcinoma. Please check your email for instructions on resetting your password. This requires existing domain ontologies in order to unambiguously define concepts, and relationships between concepts, by mapping them to standardized terminology. Transparent and reproducible radiomics research requires availability of data and metadata associated with a particular study. However, it is acknowledged that there is no single universally “correct” mapping to a given target ontology. of non-small cell lung cancer (NSCLC) with pathological features and the expression levels of phosphoprotein 53 (p53) and c-Myc in patients. Our methodology and RDF database are therefore not static, so it is intended to be improved and refined together with developing methodology over time. 2017 Jun;123(3):363-369. doi: 10.1016/j.radonc.2017.04.016. lung cancer), image modality or type (MRI, CT, digital histopathology, etc) or research focus. The RDF data are readily findable and accessible through the aforementioned link. endobj The procedures are outlined in the text in sections 2B2, 2B3, and 2B4. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures, Applications and limitations of radiomics, The cancer imaging archive (TCIA): maintaining and operating a public information repository, Learning from scanners: Bias reduction and feature correction in radiomics, Harmonizing the pixel size in retrospective computed tomography radiomics studies, Voxel size and gray level normalization of CT radiomic features in lung cancer, Preliminary investigation into sources of uncertainty in quantitative imaging features, The first step for neuroimaging data analysis: DICOM to NIfTI conversion, Guiding principles for scientific data management and stewardship, A translation approach to portable ontology specifications, The radiation oncology ontology (ROO): publishing linked data in radiation oncology using semantic web and ontology techniques, Radiomics Ontology ‐ Summary | NCBOBioPortal, Initiative for the IBS. Non-small cell lung cancer (NSCLC) cells expressing programmed death-ligand 1 (PD-L1) could interact with programmed death receptor 1 (PD-1) expressed on the surface of T cells, and result in decreased tumor cell kill by the immune system. Introduction. Other datasets hosted on TCIA that are described in this study include:€Head-Neck-Radiomics-HN1,€NSCLC-Radiomics-Interobserver1,€RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. At the opposite end, highly study‐specific mappings may need to be more dynamic or performed on an ad hoc basis. Erlotinib, afatinib, and gefitinib are approved by the FDA for first-line treatment of metastatic NSCLC in patients whose tumors have EGFR exon 19 deletions or exon 21 (L858R) … �����t��nco_�������o./?��_v�����q�:��ٖ���@�������B���C��{��-��j����s���ў��\�5${�S�l}�ۼ+�^��}�;��������[�V���?��ɦ����\����Th��\��b�\-8q�p�5���C#k���V�3��a*�D�k��. Gain expert … a sequence of pattern matching rules that allow us to link patients to radiomics features and overall survival outcome. Patients’ data and specifically demographics or clinical details play a crucial role in prediction modeling studies. By making this data available on the SPARQL endpoint, we offer a version of the combined DICOM data, clinical information, and radiomics features in a manner that is in closer alignment with FAIR data principles. A visual representation of an example ROO graph has been given by Traverso et al.24 The graph was exported as RDF triples and archived on a publicly query‐able SPARQL endpoint. Publications Remember to follow TCIA’s Data Usage & Citation Policies in your publications. Erlotinib, afatinib, and gefitinib are approved by the FDA for first-line treatment of metastatic NSCLC in patients whose tumors have EGFR exon 19 deletions or exon 21 (L858R) substitution mutations, as detected by an FDA-approved test, [] such as the cobas EGFR mutation test [] and therascreen EGFR RGQ PCR Kit. Clinical trials are research studies that involve people. 22. In the example provided in Box 3, we index the imaging modality (CT) with its Series Instance UID and Slice Thickness to the subset of morphological (ROI‐dependent) radiomics features that were computed for the Lung1 dataset, along with the corresponding survival time and survival status. A hierarchical structure is abstracted as directed acyclic graphs, wherein concepts and relationships are represented as vertices and edges of the graph, respectively. The mapping files used for the RDF triples acquisition in this particular data submission are made available for the reader on a public GitLab repository https://gitlab.com/UM‐CDS/FAIR‐compliant_clinical_radiomics_and_DICOM_metadata. Gregory J. Riely, MD, PhD, considers how the role of molecular testing in non–small cell lung cancer can be optimized with trials for precision medicine and adequate reimbursement. NSCLC-Radiomics-Interobserver1; RIDER Lung CT; NSCLC-Radiomics patients 1-100; NSCLC-Radiomics patients 101-200; NSCLC-Radiomics patients 201-300; NSCLC-Radiomics patients … Reviews There are no reviews yet. Background There is currently no Europe-wide consensus on the appropriate preanalytical measures and workflow to optimise procedures for tissue-based molecular testing of non-small-cell lung cancer (NSCLC). The clinical parameters … OMICS International is currently managing more than 400 Open Access journals with quality peer review and copyediting process. "Movsas B et al. Dose escalation of neo-adjuvant SBRT in operable patients with locally advanced non-small cell lung cancer. comment. The most common types of NSCLC are squamous cell carcinoma, large cell carcinoma, and … ���g1ނX�5t����Lf���t�p-���5�9x��e Ȟ ����q�->��s����FF_�8����n^������Ͻ���||^>m�5Z� �������]�|�g8 For a given feature, it is essential to describe how this feature is uniquely defined, which radiomics software (and version) was used to extract it, and what (if any) digital image preprocessing had been applied prior to extraction. This collection consists of radiotherapy dosimetry planning CT scans of 22 NSCLC subjects treated by conventionally fractionated external beam radiotherapy at a single Dutch center. Comparative review of drug–drug interactions with epidermal growth factor receptor tyrosine kinase inhibitors for the treatment of non-small-cell lung cancer Long-term efficacy of afatinib in a patient with squamous cell carcinoma of the lung and multiple ERBB family aberrations: afatinib in ERBB+ lung squamous cell carcinoma NSCLC-Radiomics-Genomics Identifier-ark ark:/13960/t2k70pv3z Scanner Internet Archive HTML5 Uploader 1.6.3. plus-circle Add Review. %PDF-1.4 x�]�M�0�ߪ`�� , The queries demonstrated here work in the same way even if these RDF data had been partitioned over multiple databases, irrespective of its geographical location. NSCLC-Radiomics, NSCLC-Radiomics-Genomics, Head-Neck-Radiomics-HN1, NSCLC-Radiomics-Interobserver1, RIDER Lung CT: Tumor segmentations and radiomic features: 2020-03-23: RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics … Author to whom correspondence should be addressed. CT Phantom Scans for Head, Chest, and Controlled Protocols on 100 Scanners (CC-Radiomics-Phantom-3) Data from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) Head-and-neck squamous cell carcinoma patients with CT taken during pre-treatment, mid-treatment, and post-treatment (HNSCC-3DCT-RT) endobj PMID 15800308, 2005 — "Randomized trial of amifostine in locally advanced non-small-cell lung cancer patients receiving chemotherapy and … Be the first one to write a review. Literature on MR Imaging radiogenomics (MRI-R) as predictors of ALK mutation is limited and less investigated. Learn about our remote access options, Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, 6229 ET The Netherlands. Queries may then be typed by hand or copy‐pasted in the Query tab. The values of, and relationships between, the clinical data concepts were mapped onto a graph structure. The ROIs denoted were manually drawn by … In each of these collections, primary Gross Tumor Volumes (GTVs) had been delineated by experienced radiation oncologists; ROIs are included in the TCIA collections as RTSTRUCT and SEGMENTATION objects. Note that a patient study identifier links both the radiomics and clinical triples, such that we can query into both domains and cross‐reference them within a single SPARQL query. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone … endstream Role of Amifostine []. One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. Find the List of Open Access Journals on Medical, Science and Technology. NSCLC-Radiomics-Interobserver1; RIDER Lung CT; NSCLC-Radiomics patients 1-100; NSCLC-Radiomics patients 101-200; NSCLC-Radiomics patients 201-300; NSCLC-Radiomics patients 301-422; Open your own part of the images and have a browse around, try to … These so‐called shape expressions do not fall within the present scope of submission, but could lead to promising opportunities for improvement. Computed tomography images for some frequently cited studies,3, 12 in the digital imaging and communications in medicine (DICOM) format, have been made available via The Cancer Imaging Archive (TCIA).12-16 The DICOM standard incorporates metadata about image acquisition settings and it extends to regions of interest (ROIs) delineations (i.e., radiotherapy structure set, or RTSTRUCT), but many nonradiology researchers remain unfamiliar with this conjoined data‐metadata format.

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