Relevant bibliographies by topics / Radiogenomic

Academic literature on the topic 'Radiogenomic'

Author: Grafiati
Published: 10 December 2022
Last updated: 22 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Radiogenomic.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Radiogenomic"

1

Corr, Felix, Dustin Grimm, Benjamin Saß, Mirza Pojskić, Jörg W. Bartsch, Barbara Carl, Christopher Nimsky, and Miriam H. A. Bopp. "Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review." Journal of Personalized Medicine 12, no. 3 (March 4, 2022): 402. http://dx.doi.org/10.3390/jpm12030402.

Full text
Abstract:
Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.
APA, Harvard, Vancouver, ISO, and other styles
2

Morris, Bethan, Lee Curtin, Andrea Hawkins-Daarud, Bernard Bendok, Maciej Mrugala, Jing Li, Nhan Tran, et al. "TMOD-15. IDENTIFYING THE SPATIAL AND TEMPORAL DYNAMICS OF GLIOBLASTOMA SUBPOPULATIONS WITHIN INDIVIDUAL PATIENTS." Neuro-Oncology 21, Supplement_6 (November 2019): vi265—vi266. http://dx.doi.org/10.1093/neuonc/noz175.1114.

Full text
Abstract:
Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.
APA, Harvard, Vancouver, ISO, and other styles
3

Slovak, Ryan, Meaghan Dendy Case, and Hyun S. Kim. "Genomics and Interventional Oncology in Primary Liver Cancer." Digestive Disease Interventions 04, no. 01 (March 2020): 053–59. http://dx.doi.org/10.1055/s-0040-1708533.

Full text
Abstract:
AbstractPersonalized medicine is revolutionizing oncologic care. Molecular and imaging “fingerprinting” of cancer through genomics, radiomics, and radiogenomics has allowed for the meticulous characterization of many forms of malignancy, including primary liver cancers. With this data, treatments are being developed that precisely target and exploit key variations in individual tumors. As these methods continue to evolve, interventional oncologists are well positioned to capitalize on the advances being made. This article will provide a concise overview of the genomic, radiomic, and radiogenomic research on hepatocellular carcinoma and intrahepatic cholangiocarcinoma, in addition to discussions on how precision medicine would relate to interventional oncology.
APA, Harvard, Vancouver, ISO, and other styles
4

Zanfardino, Pane, Mirabelli, Salvatore, and Franzese. "TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review." International Journal of Molecular Sciences 20, no. 23 (November 29, 2019): 6033. http://dx.doi.org/10.3390/ijms20236033.

Full text
Abstract:
In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular ‘omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular ‘omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular ‘omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.
APA, Harvard, Vancouver, ISO, and other styles
5

Khaleel, Sari, Andrew Katims, Shivaram Cumarasamy, Shoshana Rosenzweig, Kyrollis Attalla, A. Ari Hakimi, and Reza Mehrazin. "Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions." Cancers 14, no. 9 (April 22, 2022): 2085. http://dx.doi.org/10.3390/cancers14092085.

Full text
Abstract:
Radiogenomics is a field of translational radiology that aims to associate a disease’s radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: “radiogenomics”, “renal cell carcinoma”, and “clear cell renal cell carcinoma”. Articles included were limited to the English language and published between 2009–2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.
APA, Harvard, Vancouver, ISO, and other styles
6

Sukhadia, Shrey S., Aayush Tyagi, Vivek Venkatraman, Pritam Mukherjee, Prathosh A.P., Mayur Divate, Olivier Gevaert, and Shivashankar H. Nagaraj. "Abstract 6341: ImaGene: A robust AI-based software platform for tumor radiogenomic evaluation and reporting." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6341. http://dx.doi.org/10.1158/1538-7445.am2022-6341.

Full text
Abstract:
Abstract The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. These imaging and omics feature data can then be correlated and modeled using artificial intelligence (AI) techniques to highlight notable associations between tumor genotype and phenotype. Currently, however, the radiogenomics field lacks a unified and robust software platform capable of algorithmically analyzing imaging and omics features using modifiable parameters, detecting significant relationships among these features, and subjecting them to AI-based analysis. To address this gap, we developed ImaGene, a robust AI-based platform that uses omics and imaging features as inputs for different tumor types, performs statistical analyses of the correlations between these data types, and constructs AI models based upon significantly correlated features. It has several modifiable configuration parameters that provide users with complete control over their experiments. For each run, ImaGene produces comprehensive reports that can contribute to the construction of a novel radiogenomic knowledge base, in addition to enabling the deployment and sharing of AI models. To demonstrate the utility of ImaGene, we acquired imaging and omics datasets pertaining to Invasive Breast Cancer (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) from public databases and analyzed them with this platform using specific parameters. In both cases, we uncovered significant associations between several imaging features and 11 genes: CRABP1, VRTN, SMTNL2, FABP1, HAND2, HAS1, C4BPA, FAM163A, DSG1, SMTNL2 and KCNJ16 for IBC, and 10 genes: CEACAM6, IGLL1, SERPINA1, NANOG, OCA2, PRLR, ACSM2B, CYP11B1, and VPREB1 for HNSCC. Overall, our software platform is capable of identifying, analyzing, and correlating important features from tumor scans, thereby providing researchers with a reliable and accurate tool for their radiogenomics experiments. We anticipate that ImaGene will become the gold standard for tumor analyses in the field of radiogenomics owing to its ease of use, flexibility, and reproducibility. Citation Format: Shrey S. Sukhadia, Aayush Tyagi, Vivek Venkatraman, Pritam Mukherjee, Prathosh A.P., Mayur Divate, Olivier Gevaert, Shivashankar H. Nagaraj. ImaGene: A robust AI-based software platform for tumor radiogenomic evaluation and reporting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6341.
APA, Harvard, Vancouver, ISO, and other styles
7

Gallivanone, Francesca, Gloria Bertoli, and Danilo Porro. "Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions." Methods and Protocols 5, no. 5 (October 3, 2022): 78. http://dx.doi.org/10.3390/mps5050078.

Full text
Abstract:
Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with –omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).
APA, Harvard, Vancouver, ISO, and other styles
8

Abazeed, M., D. Adams, P. Tamayo, B. Yard, J. Loeffler, J. Suh, M. Meyerson, P. Hammerman, and S. Schreiber. "The Radiogenomic Landscape of Cancer." International Journal of Radiation Oncology*Biology*Physics 90, no. 1 (September 2014): S34. http://dx.doi.org/10.1016/j.ijrobp.2014.05.145.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Trout, Andrew T., Matthew R. Batie, Anita Gupta, Rachel M. Sheridan, Gregory M. Tiao, and Alexander J. Towbin. "3D printed pathological sectioning boxes to facilitate radiological–pathological correlation in hepatectomy cases." Journal of Clinical Pathology 70, no. 11 (June 8, 2017): 984–87. http://dx.doi.org/10.1136/jclinpath-2016-204293.

Full text
Abstract:
Radiogenomics promises to identify tumour imaging features indicative of genomic or proteomic aberrations that can be therapeutically targeted allowing precision personalised therapy. An accurate radiological–pathological correlation is critical to the process of radiogenomic characterisation of tumours. An accurate correlation, however, is difficult to achieve with current pathological sectioning techniques which result in sectioning in non-standard planes. The purpose of this work is to present a technique to standardise hepatic sectioning to facilitateradiological–pathological correlation. We describe a process in which three-dimensional (3D)-printed specimen boxes based on preoperative cross-sectional imaging (CT and MRI) can be used to facilitate pathological sectioning in standard planes immediately on hepatic resection enabling improved tumour mapping. We have applied this process in 13 patients undergoing hepatectomy and have observed close correlation between imaging and gross pathology in patients with both unifocal and multifocal tumours.
APA, Harvard, Vancouver, ISO, and other styles
10

Kim, Byung-Hoon, Hyeonhoon Lee, Kyu Sung Choi, Ju Gang Nam, Chul-Kee Park, Sung-Hye Park, Jin Wook Chung, and Seung Hong Choi. "Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge." Cancers 14, no. 19 (October 3, 2022): 4827. http://dx.doi.org/10.3390/cancers14194827.

Full text
Abstract:
O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Radiogenomic"

1

GALLOTTI, ALBERTO LUIGI. "Validating advanced MRI features as surrogate biomarkers of the molecular subgroups of glioblastoma by exploiting patient-specific cancer stem cell (CSC)-based animal models." Doctoral thesis, Università Vita-Salute San Raffaele, 2023. https://hdl.handle.net/20.500.11768/136637.

Full text
Abstract:
Introduction: Glioblastoma (GBM) is the most malignant brain tumor in adults. Transcriptional subgroups (proneural, PN, classical, CL, and mesenchymal, MES) have been identified, with MES being the most aggressive. Radiomics has been recently applied to neurooncology, but generally not to transcriptional affiliation. GBM derived glioma stem cells (GSCs) are used to model GBM, but culturing conditions may affect effective modeling. Materials and Methods: 36 IDHwt GBMs have been studied with advanced MRI protocols including diffusion sequences. 14 GSC lines were established from 48 GBMs and were transplanted to generate xenografts. Radiomic features were extracted from humans and xenografts to train a model predicting MES affiliation. Human GBMs and their GSC lines were profiled at the transcriptional and protein level. Results: In the first track of our study, we exploited radiomic features extracted from DTI and NODDI that indicate that MES tumors are more locally infiltrative and have more heterogeneous signal than non-MES tumors, probably due to more proliferative, less migrating cells and deposition of extracellular matrix. Models based on such features can predict MES affiliation. In the second study track, we demonstrated a progressive in vitro drift in transcriptional affiliation of GBM-derived GSCs, with some diverging to a PN profile, while other to a MES. CL component was generally downregulated in vitro. Still, PN lines efficiently model PN GBMs, as do MES GSCs for MES GBMs. We also demonstrated that protein-based categorizations effectively approximate transcriptional classification. In the third track, we demonstrated an increasing transcriptional distance of PN, CL and MES GBMs from healthy brain tissue, suggesting a likely progression from PN to MES. MES GBMs are more hypoxic and angiogenic and more dependent on extracellular matrix. On the contrary, PN tumors exploit neuronal ontologies, likely to establish synapses with neurons to guide infiltration along white matter tracts. In the last track, we identified IL7R as a candidate MES-specific mediator. Of note, tumor expression of IL7R in immunocompetent but not in immunocompromised murine recipients suggests a crosstalk between the immune microenvironment and tumor cells. Conclusions: We identified novel diffusion MRI radiomic features that correlate and predict MES affiliation of GBMs. We confirmed GSCs as powerful tools to model GBM heterogeneity, especially at early and intermediate passages, notwithstanding the progressive in vitro drift in transcriptional affiliation. We postulated a transcriptional-based evolution of GBMs, suggesting also different mechanisms of infiltration. We also propose a role of IL7R in MES GBM as potential biomarker.
INTRODUZIONE: Il glioblastoma (GBM) è il tumore cerebrale più maligno degli adulti. Sono stati identificati sottogruppi trascrizionali (proneurale, PN, classico, CL e mesenchimale, MES), con il MES che è il più aggressivo. La radiomica è stata recentemente applicata alla neurooncologia, ma generalmente non all'affiliazione trascrizionale. Le cellule staminali di glioma derivate da GBM (CSG) vengono utilizzate per modellizzare il GBM, ma le condizioni di coltura possono influire sull’efficacia del modello. MATERIALI E METODI: 36 GBM IDHwt sono stati studiati con protocolli di RM avanzata, comprese le sequenze di diffusione. Sono state stabilite 14 linee CSG da 48 GBM e sono state trapiantate per generare xenotrapianti. Le caratteristiche radiomiche sono state estratte dai pazienti e xenotrapianti per creare un modello che determini l'affiliazione MES. I GBM umani e le loro linee di CSG sono stati profilati a livello trascrizionale e proteico. RISULTATI: Nella prima traccia del nostro studio, abbiamo sfruttato le caratteristiche radiomiche estratte da DTI e NODDI che indicano che i tumori MES sono più localmente infiltrativi e hanno un segnale più eterogeneo rispetto ai tumori non MES, probabilmente a causa di cellule maggiormente proliferanti e meno migratorie e che depositano matrice extracellulare. I modelli basati su tali caratteristiche riescono a prevedere l'affiliazione MES. Nella seconda traccia di studio, abbiamo dimostrato una progressiva deriva in vitro nell'affiliazione trascrizionale di CSG derivate da GBM, con alcune divergenti verso un profilo PN, mentre altre verso un profilo MES. La componente CL risultava generalmente sottoregolata in vitro. Tuttavia, le linee PN modellizzano in modo efficiente i GBM PN, così come le CSG MES fanno con i GBM MES. Abbiamo anche dimostrato che le classificazioni basate sulle proteine approssimano efficacemente la classificazione trascrizionale. Nella terza traccia, abbiamo dimostrato una divergenza trascrizionale crescente dei GBM PN, CL e MES dal tessuto cerebrale sano, suggerendo una probabile progressione da PN a MES. I GBM MES sono più ipossici e angiogenici e più dipendenti dalla matrice extracellulare. Al contrario, i tumori PN sfruttano ontologie neuronali, in grado di stabilire sinapsi con i neuroni per guidare l'infiltrazione lungo i tratti della sostanza bianca. Nell'ultima traccia, abbiamo identificato IL7R come un candidato mediatore specifico per il sottogruppo MES. Da notare, l'espressione tumorale di IL7R evidente in ospiti murini immunocompetenti ma non in immunocompromessi suggerisce un rapporto tra il microambiente immunitario e le cellule tumorali. CONCLUSIONI: abbiamo identificato nuove caratteristiche radiomiche derivate da risonanza magnetica di diffusione che correlano e predicono l'affiliazione MES dei GBM. Abbiamo confermato le CSG come potenti strumenti per modellizzare l'eterogeneità dei GBM, specialmente nei passaggi precoci e intermedi, nonostante la progressiva deriva in vitro dell'affiliazione trascrizionale. Abbiamo postulato un'evoluzione trascrizionale dei GBM, suggerendo anche diversi meccanismi di infiltrazione. Abbiamo proposto anche un ruolo per IL7R come potenziale biomarcatore dei GBM MES.
APA, Harvard, Vancouver, ISO, and other styles
2

Beig, Niha Ghouse. "PERI-TUMORAL RADIOGENOMIC APPROACHES TO CAPTURE TUMOR ENVIRONMENT FOR DISEASE DIAGNOSIS AND PREDICTING PATIENT SURVIVAL." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1596539894404172.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Xia, Tian. "Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18589.

Full text
Abstract:
While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis.
APA, Harvard, Vancouver, ISO, and other styles
4

Badic, Bogdan. "Caractérisation multiparamétrique des cancers colorectaux." Thesis, Brest, 2018. http://www.theses.fr/2018BRES0070/document.

Full text
Abstract:
L’imagerie est un outil pour réaliser le diagnostic, le bilan d’extension et le suivi thérapeutique de la grande majorité des tumeurs. La tomodensitométrie (TDM) est la méthode la plus utilisée et les images obtenues fournissent une cartographie tumorale fondée sur la densité des tissus. L’analyse plus approfondie de ces images acquises en routine clinique a permis d’extraire des informations supplémentaires quant à la survie du patient ou à la réponse au(x) traitement(s). Toutes ces nouvelles données permettent de décrire le phénotype d’une lésion de façon non invasive et sont regroupées sous le terme de radiomique. La plupart des études de radiomique se sont focalisées sur les paramètres de texture et ont évalué les données acquises à l’aide de TDM avec injection de produit de contraste (phase portale). Pour ces travaux de thèse, nous avons réalisé une analyse des paramètres de radiomique extraits à la fois des images TDM contrastées et non contrastées des tumeurs colorectales. La construction d’un modèle pronostique à l’aide de ces paramètres a permis d’étudier la complémentarité des informations fournies par les deux modalités. Dans un second temps, l’analyse des modifications transcriptomiques des cellules souches et cellules cancéreuses dans le cancer colorectal a permis de valider l’hypothèse que la quantification de modifications transcriptomiques peut également avoir une valeur pronostique. Finalement, l’étude des corrélations entre les données d’expression génétique et la radiomique en TDM a montré que la quantification de l’hétérogénéité tumorale en TDM reflète en partie les modifications transcriptomiques
Imaging is the principal tool for diagnosis, extension assessment and therapeutic follow-up of the vast majority of tumors. Computed tomography (CT) is the most used method and provides an assessment of tumor tissue density. In-depth analysis of those images acquired in clinical routine has suppl ied additional data regarding patient survival or treatment response. All those new data allow to describe the tumor phenotype and are generally grouped under the generic term radiomics. Most of previous studies focused on texture analysis using contrast enhanced CT (portal phase). In the first part of this thesis, we carried out a radiomics analysis of both contrast-enhanced and nonenhanced CT images of the colorectal tumors.Imaging is the principal tool for diagnosis, extension assessment and therapeutic followup of the vast majority of tumors. Computed tomography (CT) is the most used method and provides an assessment of tumor tissue density. In-depth analysis of those images acquired in clinical routine has supplied additional data regarding patient survival or treatment response. All those new data allow to describe the tumor phenotype and are generally grouped under the generic term radiomics. Most of previous studies focused on texture analysis using contrast enhanced CT (portal phase). In the first part of this thesis, we carried out a radiomics analysis of both contrast-enhanced and non-enhanced CT images of the colorectal tumors
APA, Harvard, Vancouver, ISO, and other styles
5

Prasanna, Prateek. "NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Chao. "Characterising heterogeneity of glioblastoma using multi-parametric magnetic resonance imaging." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287475.

Full text
Abstract:
A better understanding of tumour heterogeneity is central for accurate diagnosis, targeted therapy and personalised treatment of glioblastoma patients. This thesis aims to investigate whether pre-operative multi-parametric magnetic resonance imaging (MRI) can provide a useful tool for evaluating inter-tumoural and intra-tumoural heterogeneity of glioblastoma. For this purpose, we explored: 1) the utilities of habitat imaging in combining multi-parametric MRI for identifying invasive sub-regions (I & II); 2) the significance of integrating multi-parametric MRI, and extracting modality inter-dependence for patient stratification (III & IV); 3) the value of advanced physiological MRI and radiomics approach in predicting epigenetic phenotypes (V). The following observations were made: I. Using a joint histogram analysis method, habitats with different diffusivity patterns were identified. A non-enhancing sub-region with decreased isotropic diffusion and increased anisotropic diffusion was associated with progression-free survival (PFS, hazard ratio [HR] = 1.08, P < 0.001) and overall survival (OS, HR = 1.36, P < 0.001) in multivariate models. II. Using a thresholding method, two low perfusion compartments were identified, which displayed hypoxic and pro-inflammatory microenvironment. Higher lactate in the low perfusion compartment with restricted diffusion was associated with a worse survival (PFS: HR = 2.995, P = 0.047; OS: HR = 4.974, P = 0.005). III. Using an unsupervised multi-view feature selection and late integration method, two patient subgroups were identified, which demonstrated distinct OS (P = 0.007) and PFS (P < 0.001). Features selected by this approach showed significantly incremental prognostic value for 12-month OS (P = 0.049) and PFS (P = 0.022) than clinical factors. IV. Using a method of unsupervised clustering via copula transform and discrete feature extraction, three patient subgroups were identified. The subtype demonstrating high inter-dependency of diffusion and perfusion displayed higher lactate than the other two subtypes (P = 0.016 and P = 0.044, respectively). Both subtypes of low and high inter-dependency showed worse PFS compared to the intermediate subtype (P = 0.046 and P = 0.009, respectively). V. Using a radiomics approach, advanced physiological images showed better performance than structural images for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status. For predicting 12-month PFS, the model of radiomic features and clinical factors outperformed the model of MGMT methylation and clinical factors (P = 0.010). In summary, pre-operative multi-parametric MRI shows potential for the non-invasive evaluation of glioblastoma heterogeneity, which could provide crucial information for patient care.
APA, Harvard, Vancouver, ISO, and other styles
7

MARIGLIANO, CHIARA. "Radiological evaluation of biomarkers for renal cell carcinoma." Doctoral thesis, 2018. http://hdl.handle.net/11573/1070034.

Full text
Abstract:
Role of MRI DWI sequences in the evaluation of early response to neo- angiogenesis inhibitors in metastatic renal cell carcinoma Purpose: Angiogenesis inhibitors have a potential role in treating metastatic renal cell carcinoma, but it is still not clear why some patients don't respond. Our objective was to look for DWI parameters able to identify patients with metastatic renal cell carcinoma who would not benefit from target therapy. RECIST1.1 was considered as Reference Standard. Methods & Materials: We prospectively enrolled 43 patients candidate to start angiogenesis inhibitors with at least one target lesion and who underwent 1,5T MRI examination with multiple bvalues DWI sequences (0,40,200,300,600): one week before (t0), 2 weeks after (t2) and 8 weeks (t8) after treatment beginning. ADC value was calculated drawing ROIs on 3 different planes. 33 patients with 38 lesions had suitable data for comparative evaluation. Results: At T8 follow-up 9 patients had partial response (PR), 20 table disease (SD), 4 progression disease (PD); average progression free survival was 272 days. PD group, as compared to DC or to PR showed significantly lower ADC values at b40 at t0 (p<0.05): we can assess that more vascularised lesions are more responsive to treatment. PD group have significantly lower ADC values then both other groups, at t0, t2 and t8, for all b-values (p<0.05). PFS and OS correlates well with ADC, in particular OS with ADC b40 at t0 (r=0,69). Coclusions: Results show that PD group has significantly lower ADC values than PR or DC everytime (t0, t2, t8) At t0 there is a better correlation between PFS or OS & ADC than PFS & dimensional criteria. ADC at t0 may help selecting patients with promising good response to angiogenesis inhibitors. Moreover at t0 and at t2 ADC has the potential to select patients who wouldn't benefit from angiogenesis inhibitors Nowadays, in the era of target therapy, it is crucial to select patients potentially responders. We have to look at cost/benefit ratio and at increasing costs of treatment options. DWI has the potential role to identify patients whose's tumor wouldn't benefit from target therapy, adding a value (ADC) to other imaging (e.g. DCE-MRI, texture imaging) and clinical parameters (e.g. miRNA) in a hypothetic multiparametric analysis.
CT Texture Analysis in Clear Cell Renal Cell Carcinoma: a Radiogenomics Prospective Purpose: The aim of this study was to investigate whether quantitative parameters obtained from CT Texture Analysis (CTTA) correlate with expression of miRNA in clear cell Renal Cell Carcinoma (ccRCC). Methods and Materials: In a retrospective single centre study, multiphasic CT examination (with arterial, portal, equilibrium and urographic phases) was performed on 20 patients with clear cell renal carcinomas (14 men and 6 women; mean age 65 years ± 13). Measures of heterogeneity were obtained in post-processing by placing a ROI on the entire tumour and CTTA parameters such as entropy, kurtosis, skewness, mean, mean of positive pixels, and SD of pixel distribution histogram were measured using multiple filter settings. Quantitative data were correlated with the expression of miRNAs obtained from the same cohort of patients: 8 fresh frozen samples and 12 formalin-fixed paraffin-embedded samples (miR-21-5p, miR-210-3p, miR-185-5p, miR-221-3p, miR-145-5p). Both evaluations (miRNAs and CTTA) were performed on tumour tissues as well as on normal cortico-medullar tissues. Analysis of Variance with linear multiple regression model methods were obtained with SPSS statistic software. For all comparisons, statistical significance was assumed p<0.05 Results: We evidenced that CTTA has robust parameters (e.g. entropy, mean, sd) to distinguish normal from pathological tissues. Moreover, a higher coefficient of determination between entropy and miR-21-5p expression (R2 =0,25) was evidenced in tumour tissues as compared to normal tissues (R2 =0,15). Interestingly, excluding four patients with extreme over-expression of miR-21-5p, excellent relation between entropy and miR21-5p levels was found specifically in tumour samples (R2= 0,64; p<0.05). Conclusion: Entropy and miRNA-21-5p show promising correlation in ccRCC; in addiction CTTA features, in particular mean and entropy show a statistically significant increase in ccRCC as compared with normal renal parenchyma.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Radiogenomic"

1

Mohy-ud-Din, Hassan, and Saima Rathore, eds. Radiomics and Radiogenomics in Neuro-oncology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40124-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kia, Seyed Mostafa, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, et al., eds. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66843-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Fan, Ming, Jiangning Song, and Zhaowen Qiu, eds. Biomedical Image or Genomic Data Characterization and Radiogenomic/Image-omics. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-8325-0093-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Rubin, Daniel, Ruijiang Li, Lei Xing, and Sandy Napel. Radiomics and Radiogenomics. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Rubin, Daniel, Ruijiang Li, Lei Xing, and Sandy Napel. Radiomics and Radiogenomics. Taylor & Francis Group, 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Suri, Jasjit S., and Sanjay Saxena. Radiomics and Radiogenomics in Neuro-Oncology : An Artificial Intelligence Paradigm - Volume 1: Radiogenomics Flow Using Artificial Intelligence. Elsevier Science & Technology Books, 2024.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Li, Ruijiang, Lei Xing, Sandy Napel, and Daniel L. Rubin. Radiomics and Radiogenomics: Technical Basis and Clinical Applications. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Radiomics and Radiogenomics: Technical Basis and Clinical Applications. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Li, Ruijiang, Lei Xing, Sandy Napel, and Daniel L. Rubin. Radiomics and Radiogenomics: Technical Basis and Clinical Applications. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Ruijiang, Lei Xing, Sandy Napel, and Daniel L. Rubin. Radiomics and Radiogenomics: Technical Basis and Clinical Applications. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Radiogenomic"

1

Kinoshita, Manabu. "Radiomics: Artificial Intelligence-Based Radiogenomic Diagnosis of Gliomas." In Multidisciplinary Computational Anatomy, 367–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4325-5_50.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Emchinov, Aleksandr. "A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 345–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Farzana, Walia, Ahmed G. Temtam, Zeina A. Shboul, M. Monibor Rahman, M. Shibly Sadique, and Khan M. Iftekharuddin. "Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 357–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_32.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Rosenstein, Barry S., Gaurav Pandey, Corey W. Speers, Jung Hun Oh, Catharine M. L. West, and Charles S. Mayo. "Radiogenomics." In Big Data in Radiation Oncology, 201–17. Boca Raton : Taylor & Francis, 2018. | Series: Imaging in medical diagnosis and therapy ; 30: CRC Press, 2019. http://dx.doi.org/10.1201/9781315207582-13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gevaert, Olivier. "Radiogenomics." In Radiomics and Radiogenomics, 169–78. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2019] |: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351208277-10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Coates, James, Asha K. Jeyaseelan, Norma Ybarra, Jessie Tao, Marc David, Sergio Faria, Luis Souhami, Fabio Cury, Marie Duclos, and Issam El Naqa. "Evaluation and Visualization of Radiogenomic Modeling Frameworks for the Prediction of Normal Tissue Toxicities." In IFMBE Proceedings, 517–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_127.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ismail, Marwa, Ramon Correa, Kaustav Bera, Ruchika Verma, Anas Saeed Bamashmos, Niha Beig, Jacob Antunes, et al. "Spatial-And-Context Aware (SpACe) “Virtual Biopsy” Radiogenomic Maps to Target Tumor Mutational Status on Structural MRI." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 305–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Raptopoulos, Vassilios, and Leo Tsai. "Introduction to Radiogenomics." In Imaging in Clinical Oncology, 71–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68873-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Li, Ruijiang. "Radiomics and Radiogenomics." In Machine and Deep Learning in Oncology, Medical Physics and Radiology, 385–98. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83047-2_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Napel, Sandy. "Principles and rationale of radiomics and radiogenomics." In Radiomics and Radiogenomics, 3–12. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2019] |: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351208277-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Radiogenomic"

1

Abazeed, Mohamed, Drew Adams, Pablo Tamayo, Matthew Meyerson, Peter Hammerman, and Stuart Schreiber. "Abstract 4259: The radiogenomic landscape of cancer." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-4259.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Smedley, Nova F., and William Hsu. "Using deep neural networks for radiogenomic analysis." In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018. http://dx.doi.org/10.1109/isbi.2018.8363864.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

K R, Spoorthy, Akash R. Mahdev, Vaishnav B, and Shruthi M. L. J. "Deep Learning Approach for Radiogenomic Classification of Brain Tumor." In 2022 IEEE 19th India Council International Conference (INDICON). IEEE, 2022. http://dx.doi.org/10.1109/indicon56171.2022.10039760.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Nero, C., F. Ciccarone, L. Boldrini, J. Lenkowicz, I. Paris, ED Capoluongo, AC Testa, A. Fagotti, V. Valentini, and G. Scambia. "462 Predictive radiogenomic model based on ovarian ultrasound images to detect germline brca 1-2 status (probe study) a radiogenomic model on us images." In IGCS 2020 Annual Meeting Abstracts. BMJ Publishing Group Ltd, 2020. http://dx.doi.org/10.1136/ijgc-2020-igcs.400.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Permuth, Jennifer B., Jung Choi, Yoganand Balarunathan, Jongphil Kim, Dung-Tsa Chen, Kun Jiang, Sonia Orcutt, et al. "Abstract 970A: Using a radiogenomic approach to classify pancreatic cancer precursors." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-970a.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gopal, Priyanka, Titas Bera, Craig Peacock, and Mohamed Abazeed. "Abstract 734: Large-scale radiogenomic profiling of patient derived xenografts (PDX)." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-734.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Gopal, Priyanka, Titas Bera, Craig Peacock, and Mohamed Abazeed. "Abstract 734: Large-scale radiogenomic profiling of patient derived xenografts (PDX)." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-734.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

van der Voort, Sebastian R., Renske Gahrmann, Martin J. van den Bent, Arnaud J. P. E. Vincent, Wiro J. Niessen, Marion Smits, and Stefan Klein. "Radiogenomic classification of the 1p/19q status in presumed low-grade gliomas." In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950601.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Yard, Brian, Aaron Petty, and Mohamed Abazeed. "Abstract 2916: Delineating the radiogenomic landscape of cancer through systematic annotation of genetic variants." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-2916.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Samala, Ravi K., Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, and Renaid Kim. "Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer." In SPIE Medical Imaging, edited by Samuel G. Armato and Nicholas A. Petrick. SPIE, 2017. http://dx.doi.org/10.1117/12.2255512.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Radiogenomic' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography