Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach

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Abstract

Objectives

To evaluate a quantitative radiomic approach based on high-resolution magnetic resonance imaging (HR-MRI) to differentiate acute/sub-acute symptomatic basilar artery plaque from asymptomatic plaque.

Methods

Ninety-six patients with basilar artery stenosis underwent HR-MRI between January 2014 and December 2016. Patients were scanned with T1- and T2-weighted imaging, as well as T1 imaging following gadolinium-contrast injection (CE-T1). The stenosis value, plaque area/burden, lumen area, minimal luminal area (MLA), intraplaque haemorrhage (IPH), contrast enhancement ratio and 94 quantitative radiomic features were extracted and compared between acute/sub-acute and asymptomatic patients. Multi-variate logistic analysis and a random forest model were used to evaluate the diagnostic performance.

Results

IPH, MLA and enhancement ratio were independently associated with acute/subacute symptoms. Radiomic features in T1 and CE-T1 images were associated with acute/subacute symptoms, but the features from T2 images were not. The combined IPH, MLA and enhancement ratio had an area under the curve (AUC) of 0.833 for identifying acute/sub-acute symptomatic plaques, and the combined T1 and CE-T1 radiomic approach had a significantly higher AUC of 0.936 ( p = 0.01). Combining all features achieved an AUC of 0.974 and accuracy of 90.5%.

Conclusions

Radiomic analysis of plaque texture on HR-MRI accurately distinguished between acutely symptomatic and asymptomatic basilar plaques.

Key Points

• High-resolution magnetic resonance imaging can assess basilar artery atherosclerotic plaque.

• Radiomic features in T1 and CE-T1 images are associated with acute symptoms.

• Radiomic analysis can accurately distinguish between acute symptomatic and asymptomatic plaque.

• The highest accuracy may be achieved by combining radiomic and conventional features.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5395-1) contains supplementary material, which is available to authorized users.

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Most cited references 40

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Stenting versus aggressive medical therapy for intracranial arterial stenosis.

Marc I Chimowitz, Michael J Lynn, Colin Derdeyn … (2011)

Atherosclerotic intracranial arterial stenosis is an important cause of stroke that is increasingly being treated with percutaneous transluminal angioplasty and stenting (PTAS) to prevent recurrent stroke. However, PTAS has not been compared with medical management in a randomized trial. We randomly assigned patients who had a recent transient ischemic attack or stroke attributed to stenosis of 70 to 99% of the diameter of a major intracranial artery to aggressive medical management alone or aggressive medical management plus PTAS with the use of the Wingspan stent system. The primary end point was stroke or death within 30 days after enrollment or after a revascularization procedure for the qualifying lesion during the follow-up period or stroke in the territory of the qualifying artery beyond 30 days. Enrollment was stopped after 451 patients underwent randomization, because the 30-day rate of stroke or death was 14.7% in the PTAS group (nonfatal stroke, 12.5%; fatal stroke, 2.2%) and 5.8% in the medical-management group (nonfatal stroke, 5.3%; non-stroke-related death, 0.4%) (P=0.002). Beyond 30 days, stroke in the same territory occurred in 13 patients in each group. Currently, the mean duration of follow-up, which is ongoing, is 11.9 months. The probability of the occurrence of a primary end-point event over time differed significantly between the two treatment groups (P=0.009), with 1-year rates of the primary end point of 20.0% in the PTAS group and 12.2% in the medical-management group. In patients with intracranial arterial stenosis, aggressive medical management was superior to PTAS with the use of the Wingspan stent system, both because the risk of early stroke after PTAS was high and because the risk of stroke with aggressive medical therapy alone was lower than expected. (Funded by the National Institute of Neurological Disorders and Stroke and others; SAMMPRIS ClinicalTrials.gov number, NCT00576693.).

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Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

Zelan Ma, Cuishan Liang, Jie Tian … (2016)

Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. (©) RSNA, 2016 Online supplemental material is available for this article.

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Decoding global gene expression programs in liver cancer by noninvasive imaging.

Eran Segal, Claude Sirlin, Clara Ooi … (2007)

Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer. Combinations of twenty-eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by noninvasive imaging, thereby enabling noninvasive, serial and frequent molecular profiling for personalized medicine.

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Author and article information

Contributors

Qi Liu:

ORCID: http://orcid.org/0000-0001-7739-7497

liuqimd@126.com

Journal

Journal ID (nlm-ta): Eur Radiol

Journal ID (iso-abbrev): Eur Radiol

Title: European Radiology

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0938-7994

ISSN (Electronic): 1432-1084

Publication date (Electronic): 9 April 2018

Publication date PMC-release: 9 April 2018

Publication date (Print): 2018

Volume: 28

Issue: 9

Pages: 3912-3921

Affiliations

[1 ]ISNI 0000 0004 0369 1660, GRID grid.73113.37, Department of Radiology, Changhai Hospital, , Second Military Medical University, ; Shanghai, 200433 China

[2 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Radiology and Biomedical Imaging, , UCSF, ; San Francisco, CA USA

[3 ]ISNI 0000 0001 0680 8770, GRID grid.239552.a, Department of Radiology, The Children’s Hospital of Philadelphia, ; Philadelphia, PA USA

Author information

Qi Liu http://orcid.org/0000-0001-7739-7497

Article

Publisher ID: 5395

DOI: 10.1007/s00330-018-5395-1

PMC ID: 6081255

PubMed ID: 29633002

SO-VID: a64dbfc5-92bf-4afd-9156-41609df8164e

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 30 October 2017

Date revision received : 15 February 2018

Date accepted : 20 February 2018

Custom metadata

ScienceOpen disciplines: Radiology & Imaging

Keywords: intracranial arteriosclerosis,magnetic resonance imaging,stroke,atherosclerotic plaques,basilar artery

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: intracranial arteriosclerosis, magnetic resonance imaging, stroke, atherosclerotic plaques, basilar artery

Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach

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Abstract

Objectives

Methods

Results

Conclusions

Key Points

Electronic supplementary material

Related collections

Stroke MRI

Most cited references 40

Stenting versus aggressive medical therapy for intracranial arterial stenosis.

Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

Decoding global gene expression programs in liver cancer by noninvasive imaging.

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