Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer

Jansen, Jacobus F.A.; Lu, Yutong; Gupta, Gaorav P.; Lee, Nancy Y.; Stambuk, Hilda E.; Mazaheri, Yousef; Deasy, Joseph O.; Shukla–Dave, Amita

doi:10.4329/wjr.v8.i1.90

Cited by 38 publications

(33 citation statements)

References 30 publications

(42 reference statements)

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“…Quantitative maps derived from dynamic contrast-enhanced images and acquired during treatment were instead considered for the calculation of heterogeneity features, which demonstrated their ability in predicting the response of head and neck squamous-cell carcinoma, highlighting a reduction in heterogeneity during CRT. 63 In this work, they did not find any correlation between pre-treatment features and the final outcome, contrary to the results reported by other groups about the use of textural features from parametric perfusion maps. In fact, in these studies, it was found that heterogeneity measures at baseline, such as coherence and fractal dimension, can be predictive of the final response for limb 92 and colorectal cancer.…”

Section: Application Of Texture Analysis In Radiotherapycontrasting

confidence: 56%

“…Texture analysis of these maps has been introduced in some recent works to assess tissue heterogeneity from the diffusion and perfusion point of view. [63][64][65] However, the reliability of textural features calculated on these maps depends on the robustness of fitting. This is a research area which is just starting to be explored; only few works assessed the relationship between the reproducibility of parametric maps estimated from DW-MRI and DCE-MRI and the reliability of textural indices.…”

Section: Mr Imagesmentioning

confidence: 99%

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Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

127

108

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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Section: Application Of Texture Analysis In Radiotherapycontrasting

confidence: 56%

Section: Mr Imagesmentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

127

108

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“…The analysis of heterogeneity in functional imaging could potentially further improve the imaging-based prognostic models. In head and neck cancer, texture of dynamic contrast-enhanced MRI (DCE-MRI) showed differences throughout the course of radiochemotherapy [16]. However, these differences were not correlated with local treatment failure.…”

Section: Introductionmentioning

confidence: 99%

Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

et al. 2017

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(2017). Material and methodsData from HNSCC patients (n=121) treated with definitive radiochemotherapy were used for model training. In total, 569 radiomic features were extracted from both contrast enhanced CT and 18F-FDG PET images in the primary tumor region.CT, PET and combined PET/CT radiomic models to assess local tumor control were trained separately. Five feature selection and three classification methods were implemented. The performance of the models was quantified using concordance index (CI) in 5-fold cross-validation in the training cohort. The best models, per image modality, were compared and verified in the independent validation cohort (n=51). The difference in CI was investigated using bootstrapping. Additionally, the observed and radiomics-based estimated probabilities of local tumor control were compared between two risk groups. ResultsThe feature selection using principal component analysis and the classification based on the multivariabale Cox regression with backward selection of the variables resulted in the best models for all image modalities (CICT=0.72, CIPET=0.74, CIPET/CT=0.77). Tumors more homogenous in CT density (decreased GLSZMsize_zone_entropy) and with a focused region of high FDG uptake (higher GLSZMSZLGE) indicated better prognosis. No significant difference in the performance of the models in the validation cohort was observed (CICT=0.73, CIPET=0.71, CIPET/CT=0.73). However, the CT radiomics based model overestimated the probability of tumor control in the poor prognostic group (predicted=68%, observed=56%). ConclusionBoth CT and PET radiomics showed equally good discriminative power for local tumor control modeling in HNSCC. However, CT-based predictions overestimated the local control rate in the poor prognostic validation cohort and thus we recommend to base the local control modeling on the 18F-FDG PET.

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“…Radiomic data are used to extract a large amount of feature information from the image by mining and analyzing the acquired feature data to establish an accurate diagnosis of the disease. Radiomic features can help to differentiate malignant from benign lesions; to predict the pathological type, early metastasis and prognosis of the tumor; and to generate an effective treatment plan ( 9 - 11 ). Researchers suggested that texture features extracted from DCE-MRI are effective for differentiation of malignant and benign, differentiation of pathological type and early prediction of breast cancer response to neoadjuvant chemotherapy ( 12 - 14 ).…”

Section: Introductionmentioning

confidence: 99%

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

Zhao

et al. 2018

Chinese Journal of Cancer Research

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ObjectiveTo develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI).MethodsEighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation. Radiomic features based on the ADC map were generated automatically by Artificial Intelligence Kit software (A.K. software; GE Healthcare, China). Feature reduction was conducted using the Mann-Whitney test and Spearman correlation after pre-treatment. A prediction model of ADC radiomics was established by logistic linear regression and cross-validation. A nomogram was established based on ADC radiomic features, pharmacokinetics and clinical features, including the morphology and ADC value for breast BI-RADS 4 lesions on MRI.ResultsA total of 396 radiomic features were extracted automatically by the A.K. software. Five features were selected after pre-processing, Mann-Whitney tests and Spearman correlation analysis. The area under the ROC curve of the prediction model comprising ADC radiomic features was 0.79 when the cutoff value was 0.45, and the accuracy, sensitivity and specificity were 80.0%, 0.813 and 0.778, respectively. A visualized differential nomogram based on the radiomic score, pharmacokinetics and clinical features was established. The decision curve showed good consistency.ConclusionsADC radiomic features could provide an important reference for differential diagnosis between benign and malignant lesions in suspicious BI-RADS 4 lesions. The visualized nomogram based on ADC radiomic features, pharmacokinetics and clinical features may have good prospects for clinical application.

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Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer

Cited by 38 publications

References 30 publications

Texture analysis of medical images for radiotherapy applications

Texture analysis of medical images for radiotherapy applications

Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

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