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Oda M, Staziaki PV, Qureshi MM, Andreu-Arasa VC, Li B, Takumi K, Chapman MN, Wang A, Salama AR, Sakai O. Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions. Eur J Radiol 2019; 120:108654. [PMID: 31539792 DOI: 10.1016/j.ejrad.2019.108654] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/16/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE Cystic and cystic-appearing odontogenic lesions of the jaw may appear similar on CT imaging. Accurate diagnosis is often difficult although the relationship of the lesion to the tooth root or crown may offer a clue to the etiology. The purpose of this study was to evaluate CT texture analysis as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw. METHODS This was an IRB-approved retrospective study including 42 pathology-proven dentigerous cysts, 37 odontogenic keratocysts, and 19 ameloblastomas. Each lesion was manually segmented on axial CT images, and textural features were analyzed using an in-house-developed Matlab-based texture analysis program that extracted 47 texture features from each segmented volume. Statistical analysis was performed comparing all pairs of the three types of lesions. RESULTS Pairwise analysis revealed that nine histogram features, one GLCM feature, three GLRL features, two Laws features, four GLGM features and two Chi-square features showed significant differences between dentigerous cysts and odontogenic keratocysts. Four histogram features and one Chi-square feature showed significant differences between odontogenic keratocysts and ameloblastomas. Two histogram features showed significant differences between dentigerous cysts and ameloblastomas. CONCLUSIONS CT texture analysis may be useful as a noninvasive method to obtain additional quantitative information to differentiate cystic and cystic-appearing odontogenic lesions of the jaw.
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Affiliation(s)
- Masafumi Oda
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Division of Oral and Maxillofacial Radiology, Kyushu Dental University, Kitakyushu, Fukuoka, Japan
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Muhammad M Qureshi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Baojun Li
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Koji Takumi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Margaret N Chapman
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Albert Wang
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Andrew R Salama
- Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States; Department of Oral & Maxillofacial Surgery, Boston Medical Center, Boston University Henry M. Goldman School of Dental Medicine, United States
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States.
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Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images. Acad Radiol 2019; 26:1001-1007. [PMID: 30393055 DOI: 10.1016/j.acra.2018.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/12/2018] [Accepted: 10/12/2018] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0-3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4-6) on routine ultrasound images. MATERIALS AND METHODS This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18-81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4-6 cm2) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ2 of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance [Formula: see text] of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ2 (χ2 = σ2/ [Formula: see text] ) was computed at each secondary ROI, forming a χ2 map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ2 maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis. RESULTS In the training group, the optimal setting was found to be [Formula: see text] = 6.0, which resulted a maximum discrimination of F0-3 vs F4-6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0-3 class had an average fibrosis area fraction of 4.7% (1.7%-11.4%), whereas the F4-6 class had an average fibrosis area fraction of 17.3% (9.8%-29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%). CONCLUSION The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0-3) and advanced liver fibrosis or cirrhosis (F4-6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades.
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Kuno H, Garg N, Qureshi MM, Chapman MN, Li B, Meibom SK, Truong MT, Takumi K, Sakai O. CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [ 18F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma. AJNR Am J Neuroradiol 2019; 40:543-550. [PMID: 30792253 DOI: 10.3174/ajnr.a5974] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 01/05/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE Differentiating nodal metastases from reactive adenopathy in HIV-infected patients with [18F] FDG-PET/CT can be challenging because lymph nodes in HIV-positive patients often show increased [18F] FDG uptake. The purpose of this study was to assess CT textural analysis characteristics of HIV-positive and HIV-negative lymph nodes on [18F] FDG-PET/CT to differentiate nodal metastases from disease-specific nodal reactivity. MATERIALS AND METHODS Nine HIV-positive patients with head and neck squamous cell carcinoma (7 men, 2 women; 29-62 years of age; median age, 48 years) with 22 lymph nodes (≥1 cm) who underwent contrast-enhanced CT with [18F] FDG-PET followed by pathologic evaluation of cervical lymph nodes were retrospectively reviewed. Twenty-six HIV-negative patients with head and neck squamous cell carcinoma with 61 lymph nodes were evaluated as a control group. Each lymph node was manually segmented, and an in-house-developed Matlab-based texture analysis program extracted 41 texture features from each segmented volume. A mixed linear regression model was used to compare the pathologically proved malignant lymph nodes with benign nodes in the 2 enrolled groups. RESULTS Thirteen (59%) lymph nodes in the HIV-positive group and 22 (36%) lymph nodes in the HIV-negative control group were confirmed as positive for metastases. There were 7 histogram features (P = .017-0.032), 3 gray-level co-occurrence features (P = .009-.025), and 9 gray-level run-length features (P < .001-.033) that demonstrated a significant difference in HIV-positive patients with either benign or malignant lymph nodes. CONCLUSIONS CT texture analysis may be useful as a noninvasive method of obtaining additional quantitative information to differentiate nodal metastases from disease-specific nodal reactivity in HIV-positive patients with head and neck squamous cell carcinoma.
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Affiliation(s)
- H Kuno
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Department of Diagnostic Radiology (H.K.), National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - N Garg
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - M M Qureshi
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - M N Chapman
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - B Li
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - S K Meibom
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - M T Truong
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - K Takumi
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Department of Radiology (K.T.), Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - O Sakai
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.) .,Radiation Oncology (M.M.Q., M.T.T., O.S.).,Otolaryngology-Head and Neck Surgery (O.S.), Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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Tsai A, Buch K, Fujita A, Qureshi MM, Kuno H, Chapman MN, Li B, Oda M, Truong MT, Sakai O. Using CT texture analysis to differentiate between nasopharyngeal carcinoma and age-matched adenoid controls. Eur J Radiol 2018; 108:208-214. [DOI: 10.1016/j.ejrad.2018.09.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 11/28/2022]
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Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J Appl Clin Med Phys 2018; 19:253-264. [PMID: 30369010 PMCID: PMC6236836 DOI: 10.1002/acm2.12482] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 09/22/2018] [Accepted: 09/24/2018] [Indexed: 12/22/2022] Open
Abstract
Objectives To evaluate the influence of MRI scanning parameters on texture analysis features. Methods Publicly 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). Results Significant 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). Conclusion Significant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.
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Affiliation(s)
- Karen Buch
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Hirofumi Kuno
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.,Department of Diagnostic Radiology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Muhammad M Qureshi
- Departments of Radiology and Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Baojun Li
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Osamu Sakai
- Departments of Radiology, Otolaryngology - Head and Neck Surgery, and Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
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Latha M, Kavitha G. Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2018; 31:483-499. [PMID: 29397450 DOI: 10.1007/s10334-018-0674-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 01/05/2018] [Accepted: 01/09/2018] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. MATERIALS AND METHODS T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. RESULTS The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). CONCLUSION A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
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Affiliation(s)
- Manohar Latha
- Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India.
| | - Ganesan Kavitha
- Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India
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Kuno H, Qureshi MM, Chapman MN, Li B, Andreu-Arasa VC, Onoue K, Truong MT, Sakai O. CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy. AJNR Am J Neuroradiol 2017; 38:2334-2340. [PMID: 29025727 DOI: 10.3174/ajnr.a5407] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/16/2017] [Indexed: 01/08/2023]
Abstract
BACKGROUND AND PURPOSE The accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy. MATERIALS AND METHODS This retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status. RESULTS Twenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68, P = .026], harmonic mean [hazard ratio = 8.61, P = .004], and fourth moment [hazard ratio = 4.56, P = .048]) and 4 gray-level run-length features (short-run emphasis [hazard ratio = 3.75, P = .044], gray-level nonuniformity [hazard ratio = 5.72, P = .004], run-length nonuniformity [hazard ratio = 4.15, P = .043], and short-run low gray-level emphasis [hazard ratio = 5.94, P = .035]) were significant predictors of outcome after adjusting for clinical variables. CONCLUSIONS Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.
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Affiliation(s)
- H Kuno
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.).,Department of Diagnostic Radiology (H.K.), National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - M M Qureshi
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - M N Chapman
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.)
| | - B Li
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.)
| | - V C Andreu-Arasa
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.)
| | - K Onoue
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.)
| | - M T Truong
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - O Sakai
- From the Departments of Radiology (H.K., M.M.Q., M.N.C., B.L., V.C.A.A., K.O., M.T.T., O.S.) .,Radiation Oncology (M.M.Q., M.T.T., O.S.).,Otolaryngology-Head and Neck Surgery (O.S.), Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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