An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

Atlas-based automatic segmentation is used \nin radiotherapy planning to accelerate the delineation of \norgans at risk (OARs). Atlas selection has been proposed \nas a way to improve the accuracy and execution time of \nsegmentation, assuming that, the more similar the atlas is to \nthe patient, the better the results will be. This paper presents \nan analysis of atlas selection methods in the context of \nradiotherapy treatment planning. For a range of commonly \ncontoured OARs, a thorough comparison of a large class \nof typical atlas selection methods has been performed. For \nthis evaluation, clinically contoured CT images of the head \nand neck (N = 316) and thorax (N = 280) were used. The \nstate-of-the-art intensity and deformation similarity-based \natlas selection methods were found to compare poorly to \nperfect atlas selection. Counter-intuitively, atlas selection \nmethods based on a fixed set of representative atlases \noutperformed atlas selection methods based on the patient \nimage. This study suggests that atlas-based segmentation \nwith currently available selection methods compares poorly \nto the potential best performance, hampering the clinical \nutility of atlas-based segmentation. Effective atlas selection \nremains an open challenge in atlas-based segmentation for \nradiotherapy planning

Congenital diaphragmatic hernia and chromosome 15q26: determination of a candidate region by use of fluorescent in situ hybridization and array-based comparative genomic hybridization

Congenital diaphragmatic hernia (CDH) has an incidence of 1 in 3,000 births and a high mortality rate (33%-58%). Multifactorial inheritance, teratogenic agents, and genetic abnormalities have all been suggested as possible etiologic factors. To define candidate regions for CDH, we analyzed cytogenetic data collected on 200 CDH cases, of which 7% and 5% showed numerical and structural abnormalities, respectively. This study focused on the most frequent structural anomaly found: a deletion on chromosome 15q. We analyzed material from three of our patients and from four previously published patients with CDH and a 15q deletion. By using array-based comparative genomic hybridization and fluorescent in situ hybridization to determine the boundaries of the deletions and by including data from two individuals with terminal 15q deletions but without CDH, we were able to exclude a substantial portion of the telomeric region from the genetic etiology of this disorder. Moreover, one patient with CDH harbored a small interstitial deletion. Together, these findings allowed us to define a minimal deletion region of approximately 5 Mb at chromosome 15q26.1-26.2. The region contains four known genes, of which two--NR2F2 and CHD2--are particularly intriguing gene candidates for CDH

Validation of a Combined Transcriptome and T Cell Receptor Alpha/Beta (TRA/TRB) Repertoire Assay at the Single Cell Level for Paucicellular Samples

Transcriptomics can be combined with TRA and TRB clonotype analysis at the single cell level. The aim of this study was to validate this approach on the ICELL8 Single-Cell system and to evaluate its usefulness to analyse clinical paucicellular samples. For this purpose, we carefully select

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

A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: A step toward individualized care and shared decision making

Background: Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models. Purpose: In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors. Materials and methods: Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis. Results: A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models. Conclusion: The models substantially outperformed ROs' predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies