MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma

Ramkumar, Shreya Pusapadi; Ranjbar, Sara; Ning, Shuluo; Lal, Devyani; Zwart, Christine M.; Wood, Christopher P.; Weindling, Steven M.; Wu, Teresa; Mitchell, J. Ross; Li, Jing; Hoxworth, Joseph M.

doi:10.3174/ajnr.a5106

Cited by 48 publications

(46 citation statements)

References 38 publications

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“…The application of TA techniques uses mathematics to differentiate between normal and injured tissues. All these parameters are suitable to characterize the gray level distribution of ROIs, which are the basis of physical properties of the tissues shown in the image, 22 and extract features from images which have usually been overlooked by the human eye 23 …”

Section: Discussionmentioning

confidence: 99%

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Gonçalves

Araújo

Nussi

et al. 2020

Journal of Periodontology

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Background The aim of this study was to apply texture analysis (TA) to cone‐beam computed tomography (CBCT) scans of patients with grade C periodontitis for detection of non‐visible changes in the image. Methods TA was performed on CBCT scans of 34 patients with grade C periodontitis. Axial sections of CBCT were divided into three groups as follows: Group L (lesion) in which there is a furcal lesion with periodontal bone loss; Group I (intermediate) in which the border of the furcal lesion has normal characteristics; and Group C (control) in which the area is healthy. Eleven texture parameters were extracted from the region of interest. Mann‐Whitney U test was used to assess the differences in the texture between the three groups as follows: L versus I; L versus C, and I versus C. Results Statistically significant differences (P <0.05) were observed in almost all parameters in the intergroup analyses (i.e., L versus I and L versus C). However, statistical differences were smaller in groups I versus C in which only entropy of sum, entropy of difference, mean of sum, and variance of difference were statistically different (P < 0.05). Conclusion TA can potentially provide prognostic information to improve the diagnostic accuracy in the grading of the tissue around the furcal lesion, thus potentially accelerating the treatment decision‐making process.

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Section: Discussionmentioning

confidence: 99%

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Gonçalves

Araújo

Nussi

et al. 2020

Journal of Periodontology

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“…While multiple prior research studies have investigated the use of a texture analysis applied to MR images the underlying influence of MRI scan parameters on texture analysis features are not entirely understood. Furthermore, despite the increasing use of texture analysis in the field of radiology, a fundamental understanding of the histopathologic and biologic correlation between tissue and texture analysis features remains in its infancy.…”

Section: Discussionmentioning

confidence: 99%

“…The use of a texture analysis applied to imaging studies including CT and MRI have been previously performed for the evaluation of multiple nonneoplastic disorders including the evaluation for mesial temporal sclerosis on MRI, evaluation of intervertebral disc disease on MRI, evaluation of hepatic fibrosis on both CT and MRI, evaluation of subchondral bone on MRI . Prior oncologic studies have also employed texture analyses to evaluate specific tumor features including the assessment of HPV status of oropharyngeal squamous cell carcinomas, prognosis of head and neck neoplasms, classification of gastric and colorectal tumors on CT, genomic mapping and predictive marker identification of gliomas on MRI, the identification of potentially prognostic predictors in lung cancer, evaluation of genitourinary neoplasms on both CT and MRI, and for the radiomic classifications of breast carcinoma subtypes …”

Section: Introductionmentioning

confidence: 99%

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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“…Finalized checklist scores are available in our supplementary dataset (Table S2 in Supplementary Material). Liu et al ( 2 ) addressed the highest number of checklist items (TS: 48), followed by Brown et al ( 36 ) and Ramkumar et al ( 43 ) (TS: 45). Of note, all studies scored points for identifying their clinical goals, stating their predictive modeling, defining their target(s) of prediction, describing their sample size, defining the observational units of their response variable(s), interpreting their final model(s), and reporting the clinical implications of their data.…”

Section: Resultsmentioning

confidence: 99%

“…Likewise, Ramkumar et al ( 43 ) addressed methodology items commonly missing in other studies. For instance, the authors explained possible prediction errors of texture analysis in distinguishing sinonasal squamous cell carcinoma from inverted papilloma.…”

Section: Discussionmentioning

confidence: 99%

Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review

et al. 2018

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BackgroundRadiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.MethodsLiterature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.ResultsSixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].ConclusionAlthough MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.

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MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma

Cited by 48 publications

References 38 publications

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

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

Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review

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