Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Buch, Karen; Li, B.; Qureshi, Muhammad M.; Kuno, Hirofumi; Anderson, Stephan W.; Sakai, Osamu

doi:10.3174/ajnr.a5139

Cited by 46 publications

(35 citation statements)

References 29 publications

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“…Original DICOM data sets were downloaded and then imported into in‐house developed MATLAB (MathWorks, Natick, MA) texture analysis software to calculate texture analysis features. The texture analysis software was developed by the co‐author (BL) and the use of this texture analysis program has been previously reported in the literature . Image segmentation of phantom was performed manually by an experienced radiologist (co‐author HK), using the same geometric boundaries and a uniform contour volume for each dataset in an effort to reduce potential variation related to the manual segmentation process.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous texture analysis equations have been defined and developed. Only a subset of 41 texture features were employed in this study based on our prior work, and based on the popularity of reported texture features in the radiomics literature …”

Section: Methodsmentioning

confidence: 99%

“…The use of our in‐house developed MATLAB program and the specific details of the texture analysis features calculated by this program have been previously published . A full description of the mathematical equations is described in the work by Haralick et al .…”

Section: Methodsmentioning

confidence: 99%

“…Prior research studies have used a radiomics approach for investigating prostate cancer radiotherapy responses, responsiveness of neoadjuvant chemotherapy in breast cancer, and prognostic predictions of advanced nasopharyngeal carcinoma . However, larger studies and systematic reviews on radiomics have noted methodological variations as a source of difficulty precluding an accurate and collective interpretation of data …”

Section: Introductionmentioning

confidence: 99%

“…Based on our knowledge of how changes in the CT scanning parameters varies texture analysis features, as well as preliminary studies investigating the sensitivity of texture features to variations in MRI technique, we could similarly deduce that changes in MRI scanning parameters such as differences in magnet strength and scanner platform could also influence texture analysis features. Thus, the purpose of this study was to evaluate and quantify changes in MRI sequence parameters may have on texture analysis features using a simple, nonanatomic phantom model.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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ObjectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.MethodsPublicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).ResultsSignificant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).ConclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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Radiomics predicts response of individual HER2‐amplified colorectal cancer liver metastases in patients treated with HER2‐targeted therapy

Giannini

Rosati

Defeudis

et al. 2020

Intl Journal of Cancer

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The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R−), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.

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The impact of phantom design and material‐dependence on repeatability and reproducibility of CT‐based radiomics features

Reyhan

Zhang

et al. 2022

Medical Physics

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Purpose To understand the design of radiomics phantom and material‐dependence on repeatability and reproducibility of computed tomography (CT) radiomics features. Methods A radiomics phantom consisting of various materials with uniformity, textural, and biological components, was constructed. The phantom was scanned with different manufacturer CT scanners and the scans were repeated multiple times on the same scanner with different acquisition settings as kVp, mAs, orientation, field of view (FOV), slice thickness, pitch, reconstruction kernels, and acquisition mode. A total of 72 phantom scans were included. For each scan, 18 different regions of interest (ROI) were contoured and 708 radiomics features were extracted from each ROI via an open source radiomics tool, IBEX. To relate the phantom data to patient data, the radiomics features from different phantom materials were compared with those extracted from 50 patients’ images of five disease sites as brain, head‐and‐neck, breast, liver, and lung cases using box‐plots comparison and principal component analysis (PCA). The temporal stability of imaging features was then evaluated with respect to a controlled scenario (test–retest) via the intra‐class correlation coefficient (ICC). The reproducibility of radiomics features with respect to different scanners or acquisition settings were further evaluated with concordance correlation coefficients (CCC). Results Among all phantom materials, the biological component had feature values closest to human tissues, especially for tumors in brain and liver. The textural component showed similar ranges of variation to lung lesions, particularly for cartridges of rice, cereal, and the 3D‐printed textural phantom with fine and rough grid. It also showed that certain materials, such as polystyrene foam, plaster, and peanuts, did not have comparable values to human tissue and could be excluded for future phantom design. High repeatability was observed in the test–retest study as indicated by an ICC value of 0.998 ± 0.020. All materials were used for feature stability analysis. For the inter‐scanner study, shape‐related features were the most‐reliable category with 94% of features having CCC ≥ 0.9, while gradient orientation histogram (GOH) were the least‐reliable with only 14.6% meeting the criteria. For the intra‐scanner study, the reproducibility of CT‐based radiomics features showed material‐dependence. In general, the instability of radiomics features introduced by kVp, mAs, pitch, acquisition mode, and orientation were relatively mild. However, the homogeneous materials were more vulnerable to those changes compared to materials with textural patterns. Regardless of material compositions, resolution parameters like FOV and slice thickness, could have large impact on feature stability. Switching between standard and bone reconstruction kernels could also result significant changes to feature reproducibility. Conclusion We have built a radiomics phantom using materials that cover a wide span of tumor textures seen i...

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Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Cited by 46 publications

References 29 publications

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Radiomics predicts response of individual HER2‐amplified colorectal cancer liver metastases in patients treated with HER2‐targeted therapy

The impact of phantom design and material‐dependence on repeatability and reproducibility of CT‐based radiomics features

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