202 research outputs found

    Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

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    Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model.Comment: Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canad

    Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

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    Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

    Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

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    Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis

    Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology

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    Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. However, there are several challenges along the way that this thesis will attempt to address. Specifically, this thesis focuses on the investigation of repeatability and reproducibility of radiomics features, the development of new machine/deep learning models, and combining these for robust outcomes modeling and their applications in radiotherapy. Radiomics features suffer from robustness issues when applied to outcome modeling problems, especially in head and neck computed tomography (CT) images. These images tend to contain streak artifacts due to patients’ dental implants. To investigate the influence of artifacts for radiomics modeling performance, we firstly developed an automatic artifact detection algorithm using gradient-based hand-crafted features. Then, comparing the radiomics models trained on ‘clean’ and ‘contaminated’ datasets. The second project focused on using hand-crafted radiomics features and conventional machine learning methods for the prediction of overall response and progression-free survival for Y90 treated liver cancer patients. By identifying robust features and embedding prior knowledge in the engineered radiomics features and using bootstrapped LASSO to select robust features, we trained imaging and dose based models for the desired clinical endpoints, highlighting the complementary nature of this information in Y90 outcomes prediction. Combining hand-crafted and machine learnt features can take advantage of both expert domain knowledge and advanced data-driven approaches (e.g., deep learning). Thus, we proposed a new variational autoencoder network framework that modeled radiomics features, clinical factors, and raw CT images for the prediction of intrahepatic recurrence-free and overall survival for hepatocellular carcinoma (HCC) patients in this third project. The proposed approach was compared with widely used Cox proportional hazard model for survival analysis. Our proposed methods achieved significant improvement in terms of the prediction using the c-index metric highlighting the value of advanced modeling techniques in learning from limited and heterogeneous information in actuarial prediction of outcomes. Advances in stereotactic radiation therapy (SBRT) has led to excellent local tumor control with limited toxicities for HCC patients, but intrahepatic recurrence still remains prevalent. As an extension of the third project, we not only hope to predict the time to intrahepatic recurrence, but also the location where the tumor might recur. This will be clinically beneficial for better intervention and optimizing decision making during the process of radiotherapy treatment planning. To address this challenging task, firstly, we proposed an unsupervised registration neural network to register atlas CT to patient simulation CT and obtain the liver’s Couinaud segments for the entire patient cohort. Secondly, a new attention convolutional neural network has been applied to utilize multimodality images (CT, MR and 3D dose distribution) for the prediction of high-risk segments. The results showed much improved efficiency for obtaining segments compared with conventional registration methods and the prediction performance showed promising accuracy for anticipating the recurrence location as well. Overall, this thesis contributed new methods and techniques to improve the utilization of radiomics for personalized radiotherapy. These contributions included new algorithm for detecting artifacts, a joint model of dose with image heterogeneity, combining hand-crafted features with machine learnt features for actuarial radiomics modeling, and a novel approach for predicting location of treatment failure.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163092/1/liswei_1.pd

    Novel Biomarkers of Gastrointestinal Cancer

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    Gastrointestinal (GI) cancer is a major cause of morbidity and mortality in the world. Since early diagnosis and optimal treatment selection are crucial to improving the prognosis of these diseases, the discovery of useful biomarkers has the potential to greatly reduce their burden. Recent technical and mechanical developments have allowed for the detection of tiny differences in various factors modified in physical conditions, which could contribute to the discovery of novel biomarkers for some diseases.In this Special Issue, we aim to focus on novel biomarkers for GI cancers, including esophageal cancer, gastric cancer, colorectal cancer, liver cancer, pancreatic cancer and biliary cancer. In addition, any samples (tissue, blood, urine and feces) are useful as biomarker sources, although body-fluid-based biomarkers are promising as diagnostic biomarkers due to their noninvasiveness. This Special Issue aims to collect novel insights clarifying the current situation and future perspective in this field

    Liver Biopsy

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    Liver biopsy is recommended as the gold standard method to determine diagnosis, fibrosis staging, prognosis and therapeutic indications in patients with chronic liver disease. However, liver biopsy is an invasive procedure with a risk of complications which can be serious. This book provides the management of the complications in liver biopsy. Additionally, this book provides also the references for the new technology of liver biopsy including the non-invasive elastography, imaging methods and blood panels which could be the alternatives to liver biopsy. The non-invasive methods, especially the elastography, which is the new procedure in hot topics, which were frequently reported in these years. In this book, the professionals of elastography show the mechanism, availability and how to use this technology in a clinical field of elastography. The comprehension of elastography could be a great help for better dealing and for understanding of liver biopsy

    Bioinformatics and Machine Learning for Cancer Biology

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    Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

    Cholangiocarcinoma 2020: the next horizon in mechanisms and management

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    [EN] Cholangiocarcinoma (CCA) includes a cluster of highly heterogeneous biliary malignant tumours that can arise at any point of the biliary tree. Their incidence is increasing globally, currently accounting for ~15% of all primary liver cancers and ~3% of gastrointestinal malignancies. The silent presentation of these tumours combined with their highly aggressive nature and refractoriness to chemotherapy contribute to their alarming mortality, representing ~2% of all cancer-related deaths worldwide yearly. The current diagnosis of CCA by non- invasive approaches is not accurate enough, and histological confirmation is necessary. Furthermore, the high heterogeneity of CCAs at the genomic, epigenetic and molecular levels severely compromises the efficacy of the available therapies. In the past decade, increasing efforts have been made to understand the complexity of these tumours and to develop new diagnostic tools and therapies that might help to improve patient outcomes. In this expert Consensus Statement, which is endorsed by the European Network for the Study of Cholangiocarcinoma, we aim to summarize and critically discuss the latest advances in CCA, mostly focusing on classification, cells of origin, genetic and epigenetic abnormalities, molecular alterations, biomarker discovery and treatments. Furthermore, the horizon of CCA for the next decade from 2020 onwards is highlightedJ.M.B. received EASL Registry Awards 2016 and 2019 (European CCA Registry, ENS-CCA). J.M.B. and M.J.P. were supported by: the Spanish Ministry of Economy and Competitiveness (J.M.B.: FIS PI12/00380, FIS PI15/01132, FIS PI18/01075 and Miguel Servet Programme CON14/00129; M.J.P.: FIS PI14/00399, FIS PI17/00022 and Ramon y Cajal Programme RYC-2015-17755, co-financed by “Fondo Europeo de Desarrollo Regional” (FEDER)); ISCIII CIBERehd; “Diputación Foral de Gipuzkoa” (J.M.B: DFG15/010, DFG16/004), and BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia BIO15/CA/016/BD); the Department of Health of the Basque Country (M.J.P.: 2015111100; J.M.B.: 2017111010), and “Fundación Científica de la Asociación Española Contra el Cancer” (AECC Scientific Foundation) (J.M.B.). J.M.B. and J.W.V. were supported by the European Commission Horizon 2020 programme (ESCALON project 825510). The laboratory of J.B.A. is supported by competitive grants from the Danish Medical Research Council, the Danish Cancer Society, and the Novo Nordisk and A.P. Møller Foundations. J.J.G.M. and R.I.R.M. were supported by the Carlos III Institute of Health, Spain (PI16/00598 and PI18/00428) and were co-financed by the European Regional Development Fund. J.M.B. and J.J.G.M. were supported by the Ministry of Science and Innovation, Spain (SAF2016-75197-R), and the “Asociación Española Contra el Cancer”, Spain (AECC-2017). R.I.R.M. was supported by the “Centro Internacional sobre el Envejecimiento”, Spain (OLD-HEPAMARKER, 0348-CIE-6-E). A.L. received funding from the Christie Charity. M.M. was supported by the Università Politecnica delle Marche, Ancona, Italy (040020_R.SCIENT.A_2018_MARZIONI_M_STRATEGICO_2017). M.S. was supported by the Yale Liver Center Clinical and Translational Core and the Cellular and Molecular Core (DK034989 Silvio O. Conte Digestive Diseases Research Center). C.C. is supported by grants from INSERM, Université de Rennes, INCa, and ITMO Cancer AVIESAN dans le cadre du Plan Cancer (Non-coding RNA in Cancerology: Fundamental to Translational), Ligue Contre le Cancer and Région Bretagne. J.Bruix was supported by grants from Instituto de Salud Carlos III (PI18/00763), AECC (PI044031) and WCR (AICR) 16-0026. A.F. was supported by grants from ISCIII (PI13/01229 and PI18/00542). CIBERehd is funded by the Instituto de Salud Carlos III. V.C., D.M., J. Bridgewater and P.I. are members of the European Reference Network - Hepatological Diseases (ERN RARE-LIVER). J.M.B. is a collaborator of the ERN RARE-LIVER
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