3,022 research outputs found
Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis
Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis
prediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof-
the-art scientific papers published in the last 6 years that built cancer prognosis predictive models
using multimodal data. We have defined the multimodality of data as four main types: clinical,
anatomopathological, molecular, and medical imaging; and we have expanded on the information
that each modality provides. The 43 studies were divided into three categories based on the modelling
approach taken, and their characteristics were further discussed together with current issues and
future trends. Research in this area has evolved from survival analysis through statistical modelling
using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a
multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional
data containing multi-omics and medical imaging information and by applying Machine Learning
and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive
multimodal models are capable of better stratifying patients, which can improve clinical management
and contribute to the implementation of personalised medicine as well as provide new and valuable
knowledge on cancer biology and its progression
Quantitative analysis with machine learning models for multi-parametric brain imaging data
Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Cancer remains one of the most challenging diseases to treat in the medical
field. Machine learning has enabled in-depth analysis of rich multi-omics
profiles and medical imaging for cancer diagnosis and prognosis. Despite these
advancements, machine learning models face challenges stemming from limited
labeled sample sizes, the intricate interplay of high-dimensionality data
types, the inherent heterogeneity observed among patients and within tumors,
and concerns about interpretability and consistency with existing biomedical
knowledge. One approach to surmount these challenges is to integrate biomedical
knowledge into data-driven models, which has proven potential to improve the
accuracy, robustness, and interpretability of model results. Here, we review
the state-of-the-art machine learning studies that adopted the fusion of
biomedical knowledge and data, termed knowledge-informed machine learning, for
cancer diagnosis and prognosis. Emphasizing the properties inherent in four
primary data types including clinical, imaging, molecular, and treatment data,
we highlight modeling considerations relevant to these contexts. We provide an
overview of diverse forms of knowledge representation and current strategies of
knowledge integration into machine learning pipelines with concrete examples.
We conclude the review article by discussing future directions to advance
cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in
artificial intelligence, including deep learning, have boosted the field of
computational pathology. This field holds tremendous potential to automate
clinical diagnosis, predict patient prognosis and response to therapy, and
discover new morphological biomarkers from tissue images. Some of these
artificial intelligence-based systems are now getting approved to assist
clinical diagnosis; however, technical barriers remain for their widespread
clinical adoption and integration as a research tool. This Review consolidates
recent methodological advances in computational pathology for predicting
clinical end points in whole-slide images and highlights how these developments
enable the automation of clinical practice and the discovery of new biomarkers.
We then provide future perspectives as the field expands into a broader range
of clinical and research tasks with increasingly diverse modalities of clinical
data
Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance
Background and objectives: By extracting this information, Machine or Deep
Learning (ML/DL)-based autonomous data analysis tools can assist clinicians and
cancer researchers in discovering patterns and relationships from complex data
sets. Many DL-based analyses on ovarian cancer (OC) data have recently been
published. These analyses are highly diverse in various aspects of cancer
(e.g., subdomain(s) and cancer type they address) and data analysis features.
However, a comprehensive understanding of these analyses in terms of these
features and AI assurance (AIA) is currently lacking. This systematic review
aims to fill this gap by examining the existing literature and identifying
important aspects of OC data analysis using DL, explicitly focusing on the key
features and AI assurance perspectives. Methods: The PRISMA framework was used
to conduct comprehensive searches in three journal databases. Only studies
published between 2015 and 2023 in peer-reviewed journals were included in the
analysis. Results: In the review, a total of 96 DL-driven analyses were
examined. The findings reveal several important insights regarding DL-driven
ovarian cancer data analysis: - Most studies 71% (68 out of 96) focused on
detection and diagnosis, while no study addressed the prediction and prevention
of OC. - The analyses were predominantly based on samples from a non-diverse
population (75% (72/96 studies)), limited to a geographic location or country.
- Only a small proportion of studies (only 33% (32/96)) performed integrated
analyses, most of which used homogeneous data (clinical or omics). - Notably, a
mere 8.3% (8/96) of the studies validated their models using external and
diverse data sets, highlighting the need for enhanced model validation, and -
The inclusion of AIA in cancer data analysis is in a very early stage; only
2.1% (2/96) explicitly addressed AIA through explainability
MOAB: Multi-Modal Outer Arithmetic Block for Fusion of Histopathological Images and Genetic Data for Brain Tumor Grading
Brain tumors are an abnormal growth of cells in the brain. They can be classified into distinct grades based on their growth. Often grading is performed based on a histological image and is one of the most significant predictors of a patient's prognosis; the higher the grade, the more aggressive the tumor. Correct diagnosis of the tumor's grade remains challenging. Though histopathological grading has been shown to be prognostic, results are subject to interobserver variability, even among experienced pathologists. Recently, the World Health Organization reported that advances in molecular genetics have led to improvements in tumor classification. This paper seeks to integrate histological images and genetic data for improved computer-aided diagnosis. We propose a novel Multi-modal Outer Arithmetic Block (MOAB) based on arithmetic operations to combine latent representations of the different modalities for predicting the tumor grade (Grade II, III and IV). Extensive experiments evaluate the effectiveness of our approach. By applying MOAB to The Cancer Genome Atlas (TCGA) glioma dataset, we show that it can improve separation between similar classes (Grade II and III) and outperform prior state-of-the-art grade classification techniques
Current State-of-the-Art of AI Methods Applied to MRI
Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.publishersversionpublishe
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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