The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Radiology, Journal Year: 2020, Volume and Issue: 295(2), P. 328 - 338

Published: March 10, 2020

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions, benchmark data sets, values verify processing calculations, as well reporting guidelines, analysis.

Language: Английский

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Radiomics in medical imaging—“how-to” guide and critical reflection

Insights into Imaging, Journal Year: 2020, Volume and Issue: 11(1)

Published: Aug. 12, 2020

Abstract Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available clinicians by means of advanced mathematical analysis. Through extraction spatial distribution signal intensities and pixel interrelationships, radiomics quantifies textural information using analysis methods from field artificial intelligence. Various studies different fields in imaging have been published so far, highlighting potential enhance clinical decision-making. However, faces several important challenges, are mainly caused various technical factors influencing extracted radiomic features. The aim present review twofold: first, we typical workflow deliver practical “how-to” guide for Second, discuss current limitations radiomics, suggest improvements, summarize relevant literature on subject.

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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The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Theranostics, Journal Year: 2019, Volume and Issue: 9(5), P. 1303 - 1322

Published: Jan. 1, 2019

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring temporal spatial characteristics of tumor.Progress computational methods, especially artificial intelligence medical image process analysis, has converted these images into quantitative minable data associated with clinical events oncology management.This concept was first described as radiomics 2012.Since then, computer scientists, radiologists, oncologists have gravitated towards this new tool exploited advanced methodologies to mine information behind images.On basis a great quantity radiographic novel technologies, researchers developed validated radiomic models that may improve accuracy diagnoses therapy response assessments.Here, we review recent methodological developments radiomics, including acquisition, segmentation, feature extraction, modelling, well rapidly developing deep learning technology.Moreover, outline main applications diagnosis, treatment planning evaluations field aim personalized medicine.Finally, discuss challenges scope applicability methods.

Language: Английский

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Radiomics in Oncology: A Practical Guide

Radiographics, Journal Year: 2021, Volume and Issue: 41(6), P. 1717 - 1732

Published: Oct. 1, 2021

Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology improve diagnosis, prognostication, clinical decision support, with goal delivering precision medicine. The authors provide a practical approach for successfully implementing radiomic workflow planning conceptualization through manuscript writing. Applications in typically are either classification tasks that involve computing probability sample belonging category, such as benign versus malignant, or prediction events time-to-event analysis, overall survival. is multidisciplinary, involving radiologists scientists, follows stepwise process tumor segmentation, image preprocessing, feature extraction, model development, validation. Images curated processed before which can be performed on tumors, subregions, peritumoral zones. Extracted features describe distribution signal intensities spatial relationship pixels region interest. To performance reduce overfitting, redundant nonreproducible removed. Validation essential estimate new iteratively samples dataset (cross-validation) separate hold-out by using internal external data. A variety noncommercial commercial software applications used. Guidelines artificial intelligence checklists useful when writing up studies. Although interest field continues grow, should familiar potential pitfalls ensure meaningful conclusions drawn. Online supplemental material available this article. Published under CC BY 4.0 license.

Language: Английский

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Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(13), P. 2638 - 2655

Published: June 25, 2019

Language: Английский

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Assessing robustness of radiomic features by image perturbation

Scientific Reports, Journal Year: 2019, Volume and Issue: 9(1)

Published: Jan. 24, 2019

Image features need to be robust against differences in positioning, acquisition and segmentation ensure reproducibility. Radiomic models that only include can used analyse new images, whereas with non-robust may fail predict the outcome of interest accurately. Test-retest imaging is recommended assess robustness, but not available for phenotype interest. We therefore investigated 18 methods determine feature robustness based on image perturbations. perturbation were compared 4032 computed from gross tumour volume two cohorts tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19 head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was measured using intraclass correlation coefficient (1,1) (ICC). Features ICC$\geq0.90$ considered robust. The NSCLC cohort contained more test-retest than HNSCC ($73.5\%$ vs. $34.0\%$). A chain consisting noise addition, affine translation, growth/shrinkage supervoxel-based contour randomisation identified fewest false positive (NSCLC: $3.3\%$; HNSCC: $10.0\%$). Thus, this robustness.

Language: Английский

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Voxel size and gray level normalization of CT radiomic features in lung cancer

Scientific Reports, Journal Year: 2018, Volume and Issue: 8(1)

Published: July 6, 2018

Abstract Radiomic features are potential imaging biomarkers for therapy response assessment in oncology. However, the robustness of with respect to parameters is not well established. Previously identified were found be intrinsically dependent on voxel size and number gray levels (GLs) a recent texture phantom investigation. Here, we validate GL in-phantom normalizations lung tumors. Eighteen patients non-small cell cancer varying tumor volumes analyzed. To compare patient data, scans acquired eight different scanners. Twenty four previously extracted from The Spearman rank (r s ) interclass correlation coefficient (ICC) used as metrics. Eight out 10 showed high > 0.9) low < 0.5) correlations voxels before after normalizations, respectively. Likewise, unstable (ICC 0.6) highly stable 0.8) We conclude that derived study also apply This highlights importance utility investigating radiomic CT phantoms.

Language: Английский

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Radiomics on Gadoxetic Acid–Enhanced Magnetic Resonance Imaging for Prediction of Postoperative Early and Late Recurrence of Single Hepatocellular Carcinoma

Clinical Cancer Research, Journal Year: 2019, Volume and Issue: 25(13), P. 3847 - 3855

Published: Feb. 26, 2019

To evaluate the usefulness of radiomic model in predicting early (≤2 years) and late (>2 recurrence after curative resection cases involving a single hepatocellular carcinoma (HCC) 2-5 cm diameter using preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI), comparison with clinicopathologic model.This retrospective study included 167 patients surgically resected pathologically confirmed HCC (n = 167, training set:validation set 128:39) who underwent MRI between January 2010 December 2015. A model, combined clinicopathologic-radiomic (CCR) were built random survival forest to predict disease-free (DFS) following conditions: DFS versus DFS, dynamic phases, peritumoral area segmentation.The showed prognostic performance comparable only 3-mm border extension [c-index difference (radiomic-clinicopathologic), -0.021, P 0.758]. The CCR highest c-index value but no statistically significant improvement over [CCR, 0.716 (0.627-0.799); 0.696 (0.557-0.799)].The that postoperative for MRI. This suggests importance including changes analysis HCC.

Language: Английский

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Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

European Radiology, Journal Year: 2020, Volume and Issue: 30(11), P. 6241 - 6250

Published: June 1, 2020

Abstract Objective To investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation calculation settings and platform version on statistical reliability radiomic features their corresponding ability to predict clinical outcome. Methods The was assessed retrospectively in three datasets (patient numbers: 108 head neck cancer, 37 small-cell lung 47 non-small-cell cancer). Features were calculated using four platforms (PyRadiomics, LIFEx, CERR IBEX). PyRadiomics, LIFEx are IBSI-compliant, whereas IBEX is not. IBSI user-defined by calculating intraclass correlation coefficients confidence intervals. influence choice relationship between biomarkers survival evaluated univariable cox regression largest dataset. Results different software only excellent (ICC > 0.9) for 4/17 when comparing all platforms. Reliability improved ICC 0.9 15/17 analysis restricted IBSI-compliant Failure harmonise resulted poor reliability, even across Software also had a marked effect feature LIFEx. identified as having significant varied platforms, did direction hazard ratios. Conclusion performance prognostic models radiomics. Key Points • varies with version. compliance improves but harmonised. collectively affect value features.

Language: Английский

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