A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

AbstractPurposeTo assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).Materials and methodsFor 20 NSCLC patients (stages Ib–IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.ResultsHigh overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2cm3, mean±SD) and manual delineations (81.9±94.1cm3; p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).ConclusionsSemiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the “gold standard”. This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors

Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms

Background: Genome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context, the Toxicogenomics Project–Genomics Assisted Toxicity Evaluation system (TG-GATEs) project generated transcriptional profiles from rat liver samples and human/rat cultured primary hepatocytes exposed to more than 100 different chemicals. Objectives: To assess the capacity of the cell culture models to recapitulate pathways induced by chemicals in vivo, we leveraged the TG-GATEs data set to compare the early transcriptional responses observed in the liver of rats treated with a large set of chemicals with those of cultured rat and human primary hepatocytes challenged with the same compounds in vitro. Methods: We developed a new pathway-based computational pipeline that efficiently combines gene set enrichment analysis (GSEA) using pathways from the Reactome database with biclustering to identify common modules of pathways that are modulated by several chemicals in vivo and in vitro across species. Results: We found that some chemicals induced conserved patterns of early transcriptional responses in in vitro and in vivo settings, and across human and rat genomes. These responses involved pathways of cell survival, inflammation, xenobiotic metabolism, oxidative stress, and apoptosis. Moreover, our results support the transforming growth factor beta receptor (TGF-βR) signaling pathway as a candidate biomarker associated with exposure to environmental toxicants in primary human hepatocytes. Conclusions: Our integrative analysis of toxicogenomics data provides a comprehensive overview of biochemical perturbations affected by a large panel of chemicals. Furthermore, we show that the early toxicological response occurring in animals is recapitulated in human and rat primary hepatocyte cultures at the molecular level, indicating that these models reproduce key pathways in response to chemical stress. These findings expand our understanding and interpretation of toxicogenomics data from human hepatocytes exposed to environmental toxicants. Citation El-Hachem N, Grossmann P, Blanchet-Cohen A, Bateman AR, Bouchard N, Archambault J, Aerts HJ, Haibe-Kains B. 2016. Characterization of conserved toxicogenomic responses in chemically exposed hepatocytes across species and platforms. Environ Health Perspect 124:313–320; http://dx.doi.org/10.1289/ehp.140915

diXa: a data infrastructure for chemical safety assessment

Motivation: The field of toxicogenomics (the application of ‘-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. Results: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. Availability and implementation: http://www.dixa-fp7.eu Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. Video Abstract [Figure presented] Development of the bioinformatics tool LOHHLA allows precise measurement of allele-specific HLA copy number, improves the accuracy in neoantigen prediction, and uncovers insights into how immune escape contributes to tumor evolution in non-small-cell lung cancer

Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients

Purpose: Although change in SUV measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer. Methods: Fifty-four esophageal cancer patients received PET/CT scans before and after chemo-radiotherapy. Of these, 45 patients underwent surgery and were classified into complete, partial, and non-responders to the preoperative chemoradiation. SUVmax and SUVmean, two co-occurrence matrix (Entropy and Homogeneity), two run-length-matrix (High-gray-run-emphasis and Short-run-high-gray-run-emphasis), and two size-zone-matrix (High-gray-zone-emphasis and Short-zone-high-gray-emphasis) textures were computed. The relationship between the relative difference of each measure at different treatment time points and the pathologic response and overall survival was assessed using the area under the receiver-operating-characteristic curve (AUC) and Kaplan-Meier statistics respectively. Results: All Textures, except Homogeneity, were better related to pathologic response than SUVmax and SUVmean. Entropy was found to significantly distinguish non-responders from the complete (AUC=0.79, p=1.7x10^-4) and partial (AUC=0.71, p=0.01) responders. Non-responders can also be significantly differentiated from partial and complete responders by the change in the run length and size zone matrix textures (AUC=0.71‒0.76, p≤0.02). Homogeneity, SUVmax and SUVmean failed to differentiate between any of the responders (AUC=0.50‒0.57, p≥0.46). However, none of the measures were found to significantly distinguish between complete and partial responders with AUC0.25). Conclusions: For the patients studied, temporal change in Entropy and all Run length matrix were better correlated with pathological response and survival than the SUV measures. The hypothesis that these metrics can be used as clinical predictors of better patient outcomes will be tested in a larger patient dataset in the future

Radiomic Machine Learning Classifiers for Prognostic Biomarkers of Head & Neck Cancer

Introduction: Radiomics extracts and mines large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine learning approaches are required. In this radiomic study, thirteen feature selection methods and eleven machine learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients. Methods: Two independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted 101 HNSCC patients. Cohort HN2 (n=95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework. Results: We observed that the three feature selection methods MRMR (AUC = 0.69, Stability = 0.66), MIFS (AUC = 0.66, Stability = 0.69), and CIFE (AUC = 0.68, Stability = 0.7) had high prognostic performance and stability. The three classifiers BY (AUC = 0.67, RSD = 11.28), RF (AUC = 0.61, RSD = 7.36), and NN (AUC = 0.62, RSD = 10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance). Conclusions: Our study identified prognostic and reliable machine learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care

SegmentationReview:A Slicer3D extension for fast review of AI-generated segmentations

SegmentationReview is a package developed in Python for fast review and editing of biomedical image segmentations. Biomedical imaging segmentation quality assessment is a crucial part of the development medical artificial intelligence (AI) algorithms but is time-consuming and labor-intensive. SegmentationReview has several components that facilitate efficient segmentation review, including automated importing of lists of images and segmentations into Slicer3D, a user-friendly graphical user interface for reviewing and assessing the quality of the segmentation, and automated tabular data-saving. The package has been tested and released as an open-source extension for Slicer3D. It enables fast, user-friendly review and editing for biomedical image segmentations

Histopathological Image QTL Discovery of Immune Infiltration Variants

Summary: Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image quantitative trait loci (iQTLs) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype-Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network. : Pathology; Bioinformatics; Computational Bioinformatics; Association Analysis Subject Areas: Pathology, Bioinformatics, Computational Bioinformatics, Association Analysi

Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features

Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 – 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55–0.77 and 0.65, CI: 0.54–0.76), comparable to manually defined volumes (0.64, CI: 0.53–0.75 and 0.63, CI: 0.52–0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research