20 research outputs found
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
Learning from scanners: Bias reduction and feature correction in radiomics
Contains fulltext :
208661.pdf (publisher's version ) (Open Access)Purpose: Radiomics are quantitative features extracted from medical images. Many radiomic features depend not only on tumor properties, but also on non-tumor related factors such as scanner signal-to-noise ratio (SNR), reconstruction kernel and other image acquisition settings. This causes undesirable value variations in the features and reduces the performance of prediction models. In this paper, we investigate whether we can use phantom measurements to characterize and correct for the scanner SNR dependence. Methods: We used a phantom with 17 regions of interest (ROI) to investigate the influence of different SNR values. CT scans were acquired with 9 different exposure settings. We developed an additive correction model to reduce scanner SNR influence. Results: Sixty-two of 92 radiomic features showed high variance due to the scanner SNR. Of these 62 features, 47 showed at least a factor 2 significant standard deviation reduction by using the additive correction model. We assessed the clinical relevance of radiomics instability by using a 221 NSCLC patient cohort measured with the same scanner. Conclusions: Phantom measurements show that roughly two third of the radiomic features depend on the exposure setting of the scanner. The dependence can be modeled and corrected significantly reducing the variation in feature values with at least a factor of 2. More complex models will likely increase the correctability. Scanner SNR correction will result in more reliable radiomics predictions in NSCLC
External Validation of a Bayesian Network for Error Detection in Radiotherapy Plans
Artificial intelligence (AI) applications have recently been proposed to detect errors in radiotherapy plans. External validation of such systems is essential to assess their performance and safety before applying them to clinical practice. We collected data from 5238 patients treated at Maastro Clinic and introduced a range of common radiotherapy plan errors for the model to detect. We estimated the model's discrimination by calculating the area under the receiver-operating characteristic curve (AUC). We also assessed its clinical usefulness as an alert system that could reduce the need for manual checks by calculating the percentage of values flagged as errors and the positive predictive value (PPV) for a range of high sensitivities (95 %-99 %) and error prevalence. The AUC when considering all variables was 67.8% (95% CI, 65.6%-69.9%). The AUC varied widely for different types of errors (from 90.4% for table angle errors to 54.5% for planning tumor volume-PTV dose errors). The percentage of flagged values ranged from 84% to 90% for sensitivities between 95% and 99% and the PPV was only slightly higher than the prevalence of the errors. The model's performance in the external validation was significantly worse than that in its original setting (AUC of 68% versus 89%). Its usefulness as an alert system to reduce the need for manual checks is questionable due to the low PPV and high percentage of values flagged as potential errors to achieve a high sensitivity. We analyzed the apparent limitations of the model and we proposed actions to overcome them