Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and ...computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
Developing the Naval Mind serves as a how-to manual and syllabus for leaders to create and lead wardroom, ready room, and work center discussion groups across the fleet to create a more educated and ...professionally engaged Navy and Marine Corps.
The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging ...data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)—an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) ...imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.
Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.
Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).
This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.
Objectives
To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).
Methods
This retrospective, ...hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra- and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney
U
test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association.
P
values < 0.05 were considered significant.
Results
Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (
p
< 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (
p
= 0.047,
τ
= 0.326).
Conclusion
This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins.
Key Points
• CT-based texture features of intra- and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC.
• CT imaging traits correlate with protein abundance in patients with HGSOC.
Online public repositories for sharing research data allow investigators to validate existing research or perform secondary research without the expense of collecting new data. Patient data made ...publicly available through such repositories may constitute a breach of personally identifiable information if not properly de-identified. Imaging data are especially at risk because some intricacies of the Digital Imaging and Communications in Medicine (DICOM) format are not widely understood by researchers. If imaging data still containing protected health information (PHI) were released through a public repository, a number of different parties could be held liable, including the original researcher who collected and submitted the data, the original researcher's institution, and the organization managing the repository. To minimize these risks through proper de-identification of image data, one must understand what PHI exists and where that PHI resides, and one must have the tools to remove PHI without compromising the scientific integrity of the data. DICOM public elements are defined by the DICOM Standard. Modality vendors use private elements to encode acquisition parameters that are not yet defined by the DICOM Standard, or the vendor may not have updated an existing software product after DICOM defined new public elements. Because private elements are not standardized, a common de-identification practice is to delete all private elements, removing scientifically useful data as well as PHI. Researchers and publishers of imaging data can use the tools and process described in this article to de-identify DICOM images according to current best practices.
Purpose
To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.
Methods
A deep ...learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (
n
= 276), evaluation (
n
= 104) and testing (
n
= 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.
Results
Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (
p
values being 4 × 10
–7
, 3 × 10
–4
, 4 × 10
–2
, respectively), and for the omental lesions on the evaluation set (
p
= 1 × 10
–3
). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (
p
= 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.
Conclusion
Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.
Relevance statement
Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.
Key points
• The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented.
• Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists.
• Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.
Graphical Abstract
The small animal imaging Digital Imaging and Communications in Medicine (DICOM) acquisition context structured report (SR) was developed to incorporate pre-clinical data in an established DICOM ...format for rapid queries and comparison of clinical and non-clinical datasets. Established terminologies (i.e., anesthesia, mouse model nomenclature, veterinary definitions, NCI Metathesaurus) were utilized to assist in defining terms implemented in pre-clinical imaging and new codes were added to integrate the specific small animal procedures and handling processes, such as housing, biosafety level, and pre-imaging rodent preparation. In addition to the standard DICOM fields, the small animal SR includes fields specific to small animal imaging such as tumor graft (i.e., melanoma), tissue of origin, mouse strain, and exogenous material, including the date and site of injection. Additionally, the mapping and harmonization developed by the Mouse-Human Anatomy Project were implemented to assist co-clinical research by providing cross-reference human-to-mouse anatomies. Furthermore, since small animal imaging performs multi-mouse imaging for high throughput, and queries for co-clinical research requires a one-to-one relation, an imaging splitting routine was developed, new Unique Identifiers (UID's) were created, and the original patient name and ID were saved for reference to the original dataset. We report the implementation of the small animal SR using MRI datasets (as an example) of patient-derived xenograft mouse models and uploaded to The Cancer Imaging Archive (TCIA) for public dissemination, and also implemented this on PET/CT datasets. The small animal SR enhancement provides researchers the ability to query any DICOM modality pre-clinical and clinical datasets using standard vocabularies and enhances co-clinical studies.
PDF
