Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data

Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases.

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Quantitative Imaging Features of Preoperative Computed Tomography Images Predict Post-Hepatectomy Liver Insufficiency: A Multi-Institutional Expansion Cohort

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2017.07.306 ◽

2017 ◽

Vol 225 (4) ◽

pp. S137

Author(s):

Linda M. Pak ◽

Jayasree Chakraborty ◽

William C. Chapman ◽

Richard K. Do ◽

BasGroot Koerkamp ◽

...

Keyword(s):

Computed Tomography ◽

Quantitative Imaging ◽

Imaging Features ◽

Liver Insufficiency ◽

Computed Tomography Images ◽

Institutional Expansion ◽

Expansion Cohort

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Epigallocatechin-3-Gallate Protects Pro-Acinar Epithelia Against Salivary Gland Radiation Injury

International Journal of Molecular Sciences ◽

10.3390/ijms22063162 ◽

2021 ◽

Vol 22 (6) ◽

pp. 3162

Author(s):

Erni Sulistiyani ◽

James M. Brimson ◽

Ajjima Chansaenroj ◽

Ladawan Sariya ◽

Ganokon Urkasemsin ◽

...

Keyword(s):

Salivary Gland ◽

Ex Vivo ◽

Biological Effects ◽

Branching Morphogenesis ◽

Epithelial Proliferation ◽

Expression Arrays ◽

Gene Expression Arrays ◽

Antioxidant Agents ◽

Bright Field Microscopy ◽

Injury Models

Antioxidant agents are promising pharmaceuticals to prevent salivary gland (SG) epithelial injury from radiotherapy and their associated irreversible dry mouth symptoms. Epigallocatechin-3-gallate (EGCG) is a well-known antioxidant that can exert growth or inhibitory biological effects in normal or pathological tissues leading to disease prevention. The effects of EGCG in the various SG epithelial compartments are poorly understood during homeostasis and upon radiation (IR) injury. This study aims to: (1) determine whether EGCG can support epithelial proliferation during homeostasis; and (2) investigate what epithelial cells are protected by EGCG from IR injury. Ex vivo mouse SG were treated with EGCG from 7.5–30 µg/mL for up to 72 h. Next, SG epithelial branching morphogenesis was evaluated by bright-field microscopy, immunofluorescence, and gene expression arrays. To establish IR injury models, linear accelerator (LINAC) technologies were utilized, and radiation doses optimized. EGCG epithelial effects in these injury models were assessed using light, confocal and electron microscopy, the Griess assay, immunohistochemistry, and gene arrays. SG pretreated with EGCG 7.5 µg/mL promoted epithelial proliferation and the development of pro-acinar buds and ducts in regular homeostasis. Furthermore, EGCG increased the populations of epithelial progenitors in buds and ducts and pro-acinar cells, most probably due to its observed antioxidant activity after IR injury, which prevented epithelial apoptosis. Future studies will assess the potential for nanocarriers to increase the oral bioavailability of EGCG.

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Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv166 ◽

2015 ◽

Vol 23 (3) ◽

pp. 617-626 ◽

Cited By ~ 1

Author(s):

Nophar Geifman ◽

Sanchita Bhattacharya ◽

Atul J Butte

Keyword(s):

Gene Expression ◽

Large Scale ◽

Biomedical Literature ◽

Cytokine Gene Expression ◽

Future Research ◽

Cytokine Gene ◽

Medical Subject Headings ◽

Expression Arrays ◽

Gene Expression Arrays ◽

Subject Headings

Abstract Objective Cytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research. Results We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation. Discussion We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research. Conclusion Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research.

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Intra-scanner repeatability of quantitative imaging features in a 3D printed semi-anthropomorphic CT phantom

European Journal of Radiology ◽

10.1016/j.ejrad.2021.109818 ◽

2021 ◽

pp. 109818

Author(s):

Hanna Muenzfeld ◽

Claus Nowak ◽

Stefanie Riedlberger ◽

Alexander Hartenstein ◽

Bernd Hamm ◽

...

Keyword(s):

Quantitative Imaging ◽

Imaging Features ◽

3D Printed

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A comparison of Affymetrix gene expression arrays

BMC Bioinformatics ◽

10.1186/1471-2105-8-449 ◽

2007 ◽

Vol 8 (1) ◽

Cited By ~ 40

Author(s):

Mark D Robinson ◽

Terence P Speed

Keyword(s):

Gene Expression ◽

Expression Arrays ◽

Gene Expression Arrays ◽

Affymetrix Gene Expression

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Radiomics in radiation oncology—basics, methods, and limitations

Strahlentherapie und Onkologie ◽

10.1007/s00066-020-01663-3 ◽

2020 ◽

Vol 196 (10) ◽

pp. 848-855

Author(s):

Philipp Lohmann ◽

Khaled Bousabarah ◽

Mauritius Hoevels ◽

Harald Treuer

Keyword(s):

Mathematical Models ◽

Quantitative Imaging ◽

Human Perception ◽

Image Features ◽

Radiotherapy Planning ◽

Large Field ◽

Imaging Features ◽

Imaging Data ◽

Complete Evaluation ◽

Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

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Gene Expression Arrays

Encyclopedia of Database Systems ◽

10.1007/978-1-4899-7993-3_1435-2 ◽

2016 ◽

pp. 1-4

Author(s):

Mehmet M. Dalkiliç

Keyword(s):

Gene Expression ◽

Expression Arrays ◽

Gene Expression Arrays

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Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

Tomography ◽

10.18383/j.tom.2016.00235 ◽

2016 ◽

Vol 2 (4) ◽

pp. 430-437 ◽

Cited By ~ 50

Keyword(s):

Quantitative Imaging ◽

Lung Nodules ◽

Imaging Features

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Paediatric acquired demyelinating syndromes: incidence, clinical and magnetic resonance imaging features

Multiple Sclerosis Journal ◽

10.1177/1352458512445944 ◽

2012 ◽

Vol 19 (1) ◽

pp. 76-86 ◽

Cited By ~ 61

Author(s):

Michael Absoud ◽

Ming J Lim ◽

Wui K Chong ◽

Christian G De Goede ◽

Katharine Foster ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Clinical Trial Design ◽

Acute Disseminated Encephalomyelitis ◽

Incidence Rates ◽

Imaging Features ◽

Resonance Imaging ◽

Contrast Imaging ◽

Mri Features ◽

First Onset

Objective: Changing trends in multiple sclerosis (MS) epidemiology may first be apparent in the childhood population affected with first onset acquired demyelinating syndromes (ADSs). We aimed to determine the incidence, clinical, investigative and magnetic resonance imaging (MRI) features of childhood central nervous system ADSs in the British Isles for the first time. Methods: We conducted a population active surveillance study. All paediatricians, and ophthalmologists ( n = 4095) were sent monthly reporting cards (September 2009–September 2010). International Paediatric MS Study Group 2007 definitions and McDonald 2010 MS imaging criteria were used for acute disseminated encephalomyelitis (ADEM), clinically isolated syndrome (CIS) and neuromyelitis optica (NMO). Clinicians completed a standard questionnaire and provided an MRI copy for review. Results: Card return rates were 90%, with information available for 200/222 positive notifications (90%). After exclusion of cases, 125 remained (age range 1.3–15.9), with CIS in 66.4%, ADEM in 32.0% and NMO in 1.6%. The female-to-male ratio in children older than 10 years ( n = 63) was 1.52:1 ( p = 0.045). The incidence of first onset ADS in children aged 1–15 years old was 9.83 per million children per year (95% confidence interval [CI] 8.18–11.71). A trend towards higher incidence rates of ADS in children of South Asian and Black ethnicity was observed compared with White children. Importantly, a number of MRI characteristics distinguished ADEM from CIS cases. Of CIS cases with contrast imaging, 26% fulfilled McDonald 2010 MS diagnostic criteria. Conclusions: We report the highest surveillance incidence rates of childhood ADS. Paediatric MS diagnosis at first ADS presentation has implications for clinical practice and clinical trial design.

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