Radiomics: the bridge between medical imaging and personalized medicine

Nature Reviews Clinical Oncology, Journal Year: 2017, Volume and Issue: 14(12), P. 749 - 762

Published: Oct. 4, 2017

Language: Английский

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Artificial intelligence in radiology

Nature reviews. Cancer, Journal Year: 2018, Volume and Issue: 18(8), P. 500 - 510

Published: May 17, 2018

Language: Английский

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Artificial intelligence in cancer imaging: Clinical challenges and applications

CA A Cancer Journal for Clinicians, Journal Year: 2019, Volume and Issue: 69(2), P. 127 - 157

Published: Feb. 5, 2019

Abstract Judgement, as one of the core tenets medicine, relies upon integration multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms evolution disease but also need to take into account individual condition patients, their ability receive treatment, and responses treatment. Challenges remain in accurate detection, characterization, monitoring cancers despite improved technologies. Radiographic assessment most commonly visual evaluations, interpretations which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises make great strides qualitative interpretation cancer imaging expert clinicians, including volumetric delineation tumors over time, extrapolation tumor genotype biological course from radiographic phenotype, prediction clinical outcome, impact treatment on adjacent organs. AI automate processes initial images shift workflow management whether or administer an intervention, subsequent observation yet envisioned paradigm. Here, authors review current state applied describe advances 4 types (lung, brain, breast, prostate) illustrate how common problems are being addressed. Although studies evaluating applications oncology date have been vigorously validated reproducibility generalizability, results do highlight increasingly concerted efforts pushing technology use future directions care.

Language: Английский

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Introduction to Radiomics

Journal of Nuclear Medicine, Journal Year: 2020, Volume and Issue: 61(4), P. 488 - 495

Published: Feb. 14, 2020

Radiomics is a rapidly evolving field of research concerned with the extraction quantitative metrics—the so-called radiomic features—within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity shape may, alone or in combination demographic, histologic, genomic, proteomic data, be used for clinical problem solving. The goal this continuing education article to provide an introduction field, covering basic radiomics workflow: feature calculation selection, dimensionality reduction, data processing. Potential applications nuclear medicine that include PET radiomics-based prediction treatment response survival will discussed. Current limitations radiomics, sensitivity acquisition parameter variations, common pitfalls also covered.

Language: Английский

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Applications and limitations of radiomics

Physics in Medicine and Biology, Journal Year: 2016, Volume and Issue: 61(13), P. R150 - R166

Published: June 8, 2016

Radiomics is an emerging field in quantitative imaging that uses advanced features to objectively and quantitatively describe tumour phenotypes. Radiomic have recently drawn considerable interest due its potential predictive power for treatment outcomes cancer genetics, which may important applications personalized medicine. In this technical review, we challenges of the radiomic field. We will review application areas issues, as well proper practices designs studies.

Language: Английский

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Radiomics: the facts and the challenges of image analysis

European Radiology Experimental, Journal Year: 2018, Volume and Issue: 2(1)

Published: Nov. 8, 2018

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition reconstruction, segmentation, features extraction qualification, analysis, model building. Each step needs careful evaluation for the construction robust reliable models transferred practice purposes prognosis, non-invasive disease tracking, response treatment. After definition texture parameters (shape features; first-, second-, higher-order features), we briefly discuss origin term radiomics methods selecting useful a approach, including cluster principal component random forest, neural network, linear/logistic regression, other. Reproducibility value should firstly tested internal cross-validation then validated on independent external cohorts. This article summarises major issues regarding this multi-step process, focussing in particular challenges sets provided by computed tomography, positron emission magnetic resonance imaging.

Language: Английский

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Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

Annals of Oncology, Journal Year: 2017, Volume and Issue: 28(6), P. 1191 - 1206

Published: Feb. 7, 2017

Language: Английский

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The Potential of Radiomic-Based Phenotyping in Precision Medicine

JAMA Oncology, Journal Year: 2016, Volume and Issue: 2(12), P. 1636 - 1636

Published: Aug. 19, 2016

Importance

Advances in genomics have led to the recognition that tumors are populated by distinct genotypic subgroups drive tumor development and progression. The spatial temporal heterogeneity of solid has been a critical barrier precision medicine approaches because standard approach sampling, often invasive needle biopsy, is unable fully capture state tumor. Image-based phenotyping, which represents quantification phenotype through medical imaging, promising for medicine.

Observations

Medical imaging can provide comprehensive macroscopic picture its environment ideally suited quantifying before, during, after treatment. As noninvasive technique, be performed at low risk inconvenience patient. semantic features accomplished visual assessment radiologists, compared with computational radiomics relies on automated processing assays. Together, these important information diagnostic, prognostic, predictive purposes.

Conclusions Relevance

Although technology already embedded clinical practice diagnosis, staging, treatment planning, response assessment, transition methods clinic surprisingly slow. This review outlines promise novel technologies obstacles application.

Language: Английский

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Machine Learning in Medical Imaging

Journal of the American College of Radiology, Journal Year: 2018, Volume and Issue: 15(3), P. 512 - 520

Published: Feb. 2, 2018

Language: Английский

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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

PLoS Medicine, Journal Year: 2018, Volume and Issue: 15(11), P. e1002711 - e1002711

Published: Nov. 30, 2018

Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for automated quantification of radiographic characteristics potentially improving patient stratification.We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC (age median = 68.3 years [range 32.5-93.3], survival 1.7 0.0-11.7]). Using external validation computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) treated with radiotherapy (n 771, age 68.0 1.3 We then employed transfer approach to achieve surgery 391, 69.1 37.2-88.0], 3.1 0.0-8.8]). found that CNN predictions were significantly associated 2-year overall from start respective treatment (area under receiver operating characteristic curve [AUC] 0.70 [95% CI 0.63-0.78], p < 0.001) (AUC 0.71 0.60-0.82], patients. The was also able stratify into low high mortality risk groups both (p 0.03) datasets. Additionally, outperform random forest models built parameters-including age, sex, node metastasis stage-as well as robustness against test-retest (intraclass correlation coefficient 0.91) inter-reader (Spearman's rank-order 0.88) variations. To gain better understanding captured by CNN, regions most contribution towards highlighted importance tumor-surrounding tissue stratification. present preliminary findings biological basis phenotypes being linked cell cycle transcriptional processes. Limitations include retrospective nature this opaque black box networks.Our results provide evidence networks may be used stratification based standard-of-care CT images motivates future research deciphering prospective data.

Language: Английский

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