Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy

Ravanelli, Marco; Farina, Davide; Morassi, Mauro; Roca, Elisa; Cavalleri, Giuseppe; Tassi, Gianfranco; Maroldi, Roberto

doi:10.1007/s00330-013-2965-0

Cited by 108 publications

(62 citation statements)

References 16 publications

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“…Most studies of CT texture analysis have focused on the assessment of neoplasms to determine tumor grade, treatment response, or survival. These studies have reported greater correspondence of heterogeneous tumor texture with higher grade of malignancy and lower overall treatment response and survival (11)(12)(13)(14)35).…”

Section: Musculoskeletal Imaging: Trabecular Ct Texture Analysis In Amentioning

confidence: 99%

Anorexia Nervosa: Analysis of Trabecular Texture with CT

et al. 2017

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Purpose:To determine indexes of skeletal integrity by using computed tomographic (CT) trabecular texture analysis of the lumbar spine in patients with anorexia nervosa and normal-weight control subjects and to determine body composition predictors of trabecular texture. Materials and Methods:This cross-sectional study was approved by the institutional review board and compliant with HIPAA. Written informed consent was obtained. The study included 30 women with anorexia nervosa (mean age 6 standard deviation, 26 years 6 6) and 30 normal-weight age-matched women (control group). All participants underwent lowdose single-section quantitative CT of the L4 vertebral body with use of a calibration phantom. Trabecular texture analysis was performed by using software. Skewness (asymmetry of gray-level pixel distribution), kurtosis (pointiness of pixel distribution), entropy (inhomogeneity of pixel distribution), and mean value of positive pixels (MPP) were assessed. Bone mineral density and abdominal fat and paraspinal muscle areas were quantified with quantitative CT. Women with anorexia nervosa and normal-weight control subjects were compared by using the Student t test. Linear regression analyses were performed to determine associations between trabecular texture and body composition. Results:Women with anorexia nervosa had higher skewness and kurtosis, lower MPP (P , .001), and a trend toward lower entropy (P = .07) compared with control subjects. Bone mineral density, abdominal fat area, and paraspinal muscle area were inversely associated with skewness and kurtosis and positively associated with MPP and entropy. Texture parameters, but not bone mineral density, were associated with lowest lifetime weight and duration of amenorrhea in anorexia nervosa. Conclusion:Patients with anorexia nervosa had increased skewness and kurtosis and decreased entropy and MPP compared with normal-weight control subjects. These parameters were associated with lowest lifetime weight and duration of amenorrhea, but there were no such associations with bone mineral density. These findings suggest that trabecular texture analysis might contribute information about bone health in anorexia nervosa that is independent of that provided with bone mineral density.q RSNA, 2016

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Section: Musculoskeletal Imaging: Trabecular Ct Texture Analysis In Amentioning

confidence: 99%

Anorexia Nervosa: Analysis of Trabecular Texture with CT

et al. 2017

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“…It is impossible to quantitatively validate them against  because  implementation is mainly for PET images and its ROI boundary handling is different from ,  TPS, and . At the time of this writing,  has been used for two substantial projects 11,15 and is currently being used by around 35 researchers from different countries with CT (including contrast-enhanced CT, noncontrast-enhanced CT, cone beam CT, and 4D CT), PET, and MRI images. ROIs have been successfully imported from commercial and research software such as , , MIMvista, , , and .…”

Section: I Testingmentioning

confidence: 99%

“…With these improvements, the overall goal is to better inform and enhance clinical decision making. [6][7][8][9][10][11][12][13][14][15][16][17][18][19] One important advancement in quantitative imaging analysis is the concept of "radiomics." Radiomics is the high-throughput extraction and analysis of quantitative imaging features from medical images.…”

Section: Introductionmentioning

confidence: 99%

ibex: An open infrastructure software platform to facilitate collaborative work in radiomics

et al. 2015

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Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The  software package was developed using the  and /++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand,  is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand,  provides an integrated development environment on top of  and /++, so users are not limited to its built-in functions. In the  developer studio, users can plug in, debug, and test new algorithms, extending 's functionality.  also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the  workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the  software to be intuitive, powerful, and easy to use.  can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone  and 's source code can be downloaded. Conclusions: The authors successfully implemented , an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation. C 2015 American Association of Physicists in Medicine. [http://dx

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“…To face this highly facet topic, a modern approach in medical image analysis is proposed in RADIOMICS [2], [3] authors introduced the extraction from the images of a large number of features and an analysis inspired by genomics analysis and modern Machine Learning (ML) algorithms. Such analysis is now quite common and in many centre studies about prognosis and toxicities have been performed: Ravanelli et al [4] investigate how to predict the effects of the 1st line of chemotherapy in lung cancer treatment by texture analysis; Bayanati et al [5] try to identify, by analysing texture and shape, which features on CT images could predict the state of lymph nodes (benign vs malignant) in lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features

Dinapoli

Alitto

Vallati

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease -which is highly localised and lead to significant social costs-and the large number of images that are available for each patient.At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom.

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Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy

Abstract: TA on CECT images in advanced lung adenocarcinoma provides an independent predictive indicator of response to first-line chemotherapy.

Cited by 108 publications

References 16 publications

Anorexia Nervosa: Analysis of Trabecular Texture with CT

Anorexia Nervosa: Analysis of Trabecular Texture with CT

ibex: An open infrastructure software platform to facilitate collaborative work in radiomics

Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features

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