Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

doi:10.1007/s00330-018-5463-6

Cited by 115 publications

(88 citation statements)

References 47 publications

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“…High-throughput extraction of features from imaging data composes the essence of radiomics, an emerging field of research which offers significant improvement to decision-support in oncology (4,5). Current work examines the predictive power of quantitative imaging biomarkers, which are quantitative features extracted from routine medical images (4,6,7), as inputs within predictive classifying models. The information contained in the imaging biomarkers has the potential to improve classification accuracy in a variety of statistical models (2).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data

et al. 2019

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As awareness of the habits and risks associated with lung cancer has increased, so has the interest in promoting and improving upon lung cancer screening procedures. Recent research demonstrates the benefits of lung cancer screening; the National Lung Screening Trial (NLST) found as its primary result that preventative screening significantly decreases the death rate for patients battling lung cancer. However, it was also noted that the false positive rate was very high (>94%).In this work, we investigated the ability of various machine learning classifiers to accurately predict lung cancer nodule status while also considering the associated false positive rate. We utilized 416 quantitative imaging biomarkers taken from CT scans of lung nodules from 200 patients, where the nodules had been verified as cancerous or benign. These imaging biomarkers were created from both nodule and parenchymal tissue. A variety of linear, nonlinear, and ensemble predictive classifying models, along with several feature selection methods, were used to classify the binary outcome of malignant or benign status. Elastic net and support vector machine, combined with either a linear combination or correlation feature selection method, were some of the best-performing classifiers (average cross-validation AUC near 0.72 for these models), while random forest and bagged trees were the worst performing classifiers (AUC near 0.60). For the best performing models, the false positive rate was near 30%, notably lower than that reported in the NLST.The use of radiomic biomarkers with machine learning methods are a promising diagnostic tool for tumor classification. The have the potential to provide good classification and simultaneously reduce the false positive rate.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data

et al. 2019

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“…Since then, a large and increasing number of researchers have used these texture features for medical imaging analysis, e.g., in CT, MRI, FDG-PET, and ultrasound [ 28 , 30 – 32 ]. Besides its recent multifold use in oncological imaging in terms of tissue entity discrimination, characterization, and treatment response monitoring, texture analysis also was described as a reproducible tool to quantitatively assess paraspinal fatty infiltration in MRI [ 33 – 36 ]. In these and other studies, texture heterogeneity was described to be associated with therapy response and clinical outcome [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Texture analysis of vertebral bone marrow using chemical shift encoding–based water-fat MRI: a feasibility study

et al. 2019

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Summary This feasibility study investigated the spatial heterogeneity of the lumbar vertebral bone marrow using chemical shift encoding–based water-fat MRI. Acquired texture features like contrast and dissimilarity allowed for differentiation of pre- and postmenopausal women and may serve as imaging biomarkers in the future. Introduction While the vertebral bone marrow fat using chemical shift encoding water-fat magnetic resonance imaging (MRI) has been extensively studied, its spatial heterogeneity has not been analyzed yet. Therefore, this feasibility study investigated the spatial heterogeneity of the lumbar vertebral bone marrow by using texture analysis in proton density fat fraction (PDFF) maps. Methods Forty-one healthy pre- and postmenopausal women were recruited for this study (premenopausal ( n = 15) 30 ± 7 years, postmenopausal ( n = 26) 65 ± 7 years). An eight-echo 3D spoiled gradient echo sequence was used for chemical shift encoding–based water-fat separation at the lumbar spine. Vertebral bodies L1 to L5 were manually segmented. Mean PDFF values and texture features were extracted at each vertebral level, namely variance, skewness, and kurtosis, using statistical moments and second-order features (energy, contrast, correlation, homogeneity, dissimilarity, entropy, variance, and sum average). Parameters were compared between pre- and postmenopausal women and vertebral levels. Results PDFF was significantly higher in post- than in premenopausal women (49.37 ± 8.14% versus 27.76 ± 7.30%, p < 0.05). Furthermore, PDFF increased from L1 to L5 (L1 37.93 ± 12.85%, L2 38.81 ± 12.77%, L3 40.23 ± 12.72%, L4 42.80 ± 13.27%, L5 45.21 ± 14.55%, p < 0.05). Bone marrow heterogeneity based on texture analysis was significantly ( p < 0.05) increased in postmenopausal women. Contrast and dissimilarity performed best in differentiating pre- and postmenopausal women (AUC = 0.97 and 0.96, respectively), not significantly different compared with PDFF (AUC = 0.97). Conclusion Conclusively, an increased bone marrow heterogeneity could be observed in postmenopausal women. In the future, texture parameters might provide additional information to detect and monitor vertebral bone marrow alterations due to aging or hormonal changes beyond conventional anatomic imaging.

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“…Quantitative TA may add useful information to those acquired with MRI. Different TA approaches 2D/3D have been investigated showing the superiority of 3D approach for predicting treatment response 39,40 as well as to develop a classification model. 37 Kniep et al developed a model which was able to able to reveal the primary tumor site by analyzing brain metastasis MRI images using a radiomic approach.…”

Section: Discussionmentioning

confidence: 99%

Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process–A systematic review

et al. 2020

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Advanced malignant melanoma represents a public health matter due to its rising incidence and aggressiveness. Novel therapies such as immunotherapy are showing promising results with improved progression free and overall survival in melanoma patients. However, novel targeted and immunotherapies could generate atypical patterns of response which are nowadays a big challenge since imaging criteria (ie Recist 1.1) have not been proven to be always reliable to assess response. Radiomics and in particular texture analysis (TA) represent new quantitative methodologies which could reduce the impact of these limitations providing most robust data in support of clinical decision process. The aim of this paper was to review the state of the art of radiomics/TA when it is applied to the imaging of metastatic melanoma patients.

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Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

Abstract: • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

Cited by 115 publications

References 47 publications

Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data

Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data

Texture analysis of vertebral bone marrow using chemical shift encoding–based water-fat MRI: a feasibility study

Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process–A systematic review

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