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Histogram analysis of synthetic magnetic resonance imaging: Correlations with histopathological factors in head and neck squamous cell carcinoma. Eur J Radiol 2023; 160:110715. [PMID: 36753947 DOI: 10.1016/j.ejrad.2023.110715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 01/24/2023] [Indexed: 01/30/2023]
Abstract
PURPOSE To analyse the association between histogram parameters derived from synthetic MRI (SyMRI) and different histopathological factors in head and neck squamous cell carcinoma (HNSCC). METHOD Sixty-one patients with histologically proven primary HNSCC were prospectively enrolled. The correlations between histogram parameters of SyMRI (T1, T2 and proton density (PD) maps) and histopathological factors were analysed using Spearman analysis. The Mann-Whitney U test or Student's t test was utilized to differentiate histological grades and human papillomavirus (HPV) status. The ROC curves and leave-one-out cross-validation (LOOCV) were used to evaluate the differentiation performance. Bootstrapping was applied to avoid overfitting. RESULTS Several histogram parameters were associated with histological grade: T1 map (r = 0.291) and PD map (r = 0.294 - 0.382/-0.343), and PD_75th Percentile showed the highest differentiation performance (AUC: 0.721 (ROC) and 0.719 (LOOCV)). Moderately negative correlations were found between p16 status and the histogram parameters: T1 map (r = -0.587 - -0.390), T2 map (r = -0.649 - -0.357) and PD map (r = -0.537 - -0.338). In differentiating HPV infection, Entropy was the most discriminative parameter in each map and T2_Entropy showed the highest diagnostic performance (AUC: 0.851 [ROC] and 0.851 [LOOCV]). Additionally, several histogram parameters were correlated with Ki-67 (r = -0.379/-0.397), epidermal growth factor receptor (EGFR) (r = 0.318/0.322) status and p53 (r = 0.452 - 0.665/-0.607) status. CONCLUSIONS Histogram parameters derived from SyMRI may serve as a potential biomarker for discriminating relevant histopathological features, including histological differentiation grade, HPV infection, Ki-67, EGFR and p53 statuses.
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Costa ALF, Fardim KAC, Ribeiro IT, Jardini MAN, Braz-Silva PH, Orhan K, de Castro Lopes SLP. Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis. Imaging Sci Dent 2023; 53:43-51. [PMID: 37006790 PMCID: PMC10060763 DOI: 10.5624/isd.20220166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/14/2022] [Indexed: 01/12/2023] Open
Abstract
Purpose This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively). Materials and Methods CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.
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Affiliation(s)
| | - Karolina Aparecida Castilho Fardim
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil
| | - Isabela Teixeira Ribeiro
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil
| | - Maria Aparecida Neves Jardini
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil
| | - Paulo Henrique Braz-Silva
- Division of General Pathology, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil
- Laboratory of Virology, Institute of Tropical Medicine of São Paulo, School of Medicine, University of São Paulo, São Paulo, SP, Brazil
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.
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Ito K, Kondo T, Andreu-Arasa VC, Li B, Hirahara N, Muraoka H, Sakai O, Kaneda T. Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology. Oral Radiol 2021; 38:315-324. [PMID: 34327595 DOI: 10.1007/s11282-021-00558-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/25/2021] [Indexed: 08/29/2023]
Abstract
OBJECTIVES The purpose of this study was to investigate computed tomography (CT) texture features of mucosal thickening of maxillary sinus mucosa to differentiate odontogenic maxillary sinusitis (OMS) from non-odontogenic maxillary sinusitis (NOMS). METHODS Eighteen OMS patients and age- and gender-matched 18 NOMS patients who underwent sinus CT were retrospectively reviewed. OMS patients were identified by histopathological examination of tissues excised at surgery combined with CT imaging findings. Patients with mucosal thickening in the maxillary sinus without apical periodontitis or advanced periodontal bone loss near the maxillary sinus on CT were defined as NOMS. Patients with thin mucosal thickening (< 10 mm), cyst, tumor, post-operative deformity, severe metal artifact precluding visualization of the maxillary sinus, and age younger than 20 years were excluded. CT texture features of the mucosal thickening were analyzed using an in-house developed Matlab-based texture analysis program. Forty-five texture features were extracted from each segmented volume. The results were tested with the Mann-Whitney U test. RESULTS Six histogram features (mean, median, standard deviation, entropy, geometric mean, harmonic mean) and two gray-level co-occurrence matrix features (entropy, correlation) showed significant differences between OMS and NOMS patients. CONCLUSIONS CT texture analysis revealed the quantitative differences between OMS and NOMS. The texture features can serve as a quantitative indicator of maxillary sinusitis to differentiate between OMS and NOMS and help prevent incorrect treatment choices.
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Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan.
| | - Takumi Kondo
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - V Carlota Andreu-Arasa
- Department of Radiology, 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
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.,Department of Otolaryngology, Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.,Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
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Liu Y, Fang Q, Jiang A, Meng Q, Pang G, Deng X. Texture analysis based on U-Net neural network for intracranial hemorrhage identification predicts early enlargement. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106140. [PMID: 33979753 DOI: 10.1016/j.cmpb.2021.106140] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 04/24/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Early hemorrhage enlargement in hypertensive cerebral hemorrhage indicates a poor prognosis. This study aims to predict the early enlargement of cerebral hemorrhage through the intelligent texture analysis of cerebral hemorrhage after segmentation. METHODS A total of 54 patients with hypertensive intracerebral hemorrhage were selected and divided into enlarged hematoma (enlarged group) and non-enlarged hematoma (negative group). The U-Net Neural network model and contour recognition were used to extract the brain parenchymal region, and Mazda texture analysis software was used to extract regional features. The texture features were reduced by Fisher coefficient (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) to select the best feature parameters. B11 module was used to analyze the selected features. The misclassified rate of feature parameters screened by different dimensionality reduction methods was calculated. RESULTS The neural network based on U-Net can accurately identify the lesion of cerebral hemorrhage. Among the 54 patients, 18 were in the enlarged group and 36 in the negative group. The parameters of gray level co-occurrence matrix and gray level run length matrix can be used to predict the enlargement of intracerebral hemorrhage. Among the features screened by Fisher, POE + ACC and MI, the texture features of MI showed the lowest misclassified rate, which was 0. CONCLUSION The texture analysis based on U-Net neural network is helpful to predict the early expansion of hypertensive cerebral hemorrhage, and the parameters of gray level co-occurrence matrix and gray level run length matrix under MI dimensionality reduction have the most excellent predictive value.
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Affiliation(s)
- Yu Liu
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Qiong Fang
- Department of Basic Medicine, Anhui Medical College, Hefei 230601, China.
| | - Anhong Jiang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China.
| | - Qingling Meng
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Gang Pang
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Xuefei Deng
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
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Ito K, Muraoka H, Hirahara N, Sawada E, Okada S, Kaneda T. Computed tomography texture analysis of mandibular condylar bone marrow in diabetes mellitus patients. Oral Radiol 2021; 37:693-699. [PMID: 33611771 DOI: 10.1007/s11282-021-00517-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 02/08/2021] [Indexed: 10/22/2022]
Abstract
OBJECTIVES Diabetes mellitus (DM) is associated with a broad range of complications, such as retinopathy, nephropathy, neuropathy, and cardiovascular disease. Therefore, predicting DM from head and neck images is a challenge for clinicians. The purpose of this study was to assess the mandibular condylar bone marrow in DM patients using computed tomography (CT) texture analysis. METHODS This retrospective study included 16 DM and age and sex matched 16 control patients (11 men, 5 women; mean age, 56.8 ± 14.4 years; range 31-78 years). Patients with Type I DM, prior history of taking bisphosphonates, osteoarthritis of the temporomandibular joint, and CT images with metal artifacts were excluded from this study. Bilateral mandibular condylar bone marrow was manually contoured on axial CT images. The presence or absence of DM is the primary predictor variable. Texture features of the region of interest was the outcome variable, that were analyzed using an open-access software, MaZda Ver.3.3. For each group, 20 features out of 279 parameters were selected with Fisher, probability of error and average correlation coefficient methods in MaZda. Bivariate statistics were computed with the Mann-Whitney U test and the P value was set at .05. RESULTS One histogram feature, 15 Gy level co-occurrence matrix features, and four gray level run length matrix features showed differences between the DM patients and non-DM patients (P < 0.05). CONCLUSIONS Several texture features of the condyle demonstrated differences between the DM and non-DM patients. CT texture analysis may potentially detect DM from the condylar bone marrow.
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Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan.
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Eri Sawada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Shunya Okada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
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Liu C, Ma C, Duan J, Qiu Q, Guo Y, Zhang Z, Yin Y. Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor. BMC Med Imaging 2020; 20:75. [PMID: 32631330 PMCID: PMC7339470 DOI: 10.1186/s12880-020-00475-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/26/2020] [Indexed: 12/13/2022] Open
Abstract
Background This study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images. Methods In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test. Results A total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC ≥ 0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves (AUC) of these features were 0.731 ± 0.075, 0.717, 0.748 ± 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features (Child features). Conclusion Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.
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Affiliation(s)
- Chenlu Liu
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China.,Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China
| | - Changsheng Ma
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China
| | - Jinghao Duan
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China
| | - Qingtao Qiu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China
| | - Yanluan Guo
- Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China
| | - Zhenhua Zhang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Yong Yin
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China.
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Jung YJ, Han M, Ha EJ, Choi JW. Differentiation of salivary gland tumors through tumor heterogeneity: a comparison between pleomorphic adenoma and Warthin tumor using CT texture analysis. Neuroradiology 2020; 62:1451-1458. [PMID: 32621023 DOI: 10.1007/s00234-020-02485-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/22/2020] [Indexed: 11/27/2022]
Abstract
PURPOSE We compared the diagnostic performance of CT texture analysis in single-phase CT scan with that of conventional enhancement pattern analysis in a two-phase CT scan for discrimination of salivary gland tumors, Warthin tumor (WT) from pleomorphic adenoma (PA). METHODS One hundred seventy-eight patients with PA and 84 patients with WT were selected and CT texture analysis was separately performed on early (40s) and delayed (180s) phases, after injection of the contrast agent, using commercially available software. The attenuation changes and enhancement patterns were visually and quantitatively assessed with Hounsfield units (HU). Differences between PAs and WTs were analyzed using χ2 test and independent t test. Diagnostic performance of texture parameters in single-phase CT was compared with that of dynamic enhancement pattern in two-phase CT using the McNemar test. RESULTS Ratio of tumoral HU (delayed phase/early phase) was significantly higher in PAs compared with WTs (p < 0.001). Tumor heterogeneity parameters, standard deviation (SD) and entropy, were significantly lower in WTs regardless of the type of filter used (p ≤ 0.001). Mean with coarse filter (AUC = 0.944) on early phase scan and entropy with medium filter (AUC = 0.901) on delayed scan were best discriminators between PAs and WTs. Diagnostic accuracy of mean (90.5%) on early scan and entropy (84.7%) on delayed scan was not significantly different from the accuracy (89.3%) of conventional wash-out pattern for distinguishing WTs from PAs (p = 0.742, p = 0.088, respectively). CONCLUSION Diagnostic performance of texture parameters was similar to that of quantitative enhancement pattern for differentiating WTs from PAs, with the advantage in lower radiation exposure.
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Affiliation(s)
- Yong Jun Jung
- Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, 164, World Cup-ro, Yeongtong-gu, Suwon, Republic of Korea, 16499
| | - Miran Han
- Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, 164, World Cup-ro, Yeongtong-gu, Suwon, Republic of Korea, 16499.
| | - Eun Ju Ha
- Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, 164, World Cup-ro, Yeongtong-gu, Suwon, Republic of Korea, 16499
| | - Jin Wook Choi
- Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, 164, World Cup-ro, Yeongtong-gu, Suwon, Republic of Korea, 16499
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Gonçalves BC, Araújo EC, Nussi AD, Bechara N, Sarmento D, Oliveira MS, Santamaria MP, Costa ALF, Lopes S. Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion. J Periodontol 2020; 91:1159-1166. [DOI: 10.1002/jper.19-0477] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 12/12/2019] [Accepted: 12/20/2019] [Indexed: 12/13/2022]
Affiliation(s)
- Bianca C. Gonçalves
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Elaine C. Araújo
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Amanda D. Nussi
- Postgraduate Program in Dentistry Cruzeiro do Sul University (UNICSUL) Sao Paulo Sao Paulo Brazil
| | - Naira Bechara
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Dmitry Sarmento
- Department of Stomatology School of Dentistry University of São Paulo Sao Paulo Sao Paulo Brazil
| | - Marcia S. Oliveira
- Department of Physics Institute of Exact Sciences and Technology Paulista University (UNIP) Sao Paulo Sao Paulo Brazil
| | - Mauro P. Santamaria
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
| | - Andre Luiz F. Costa
- Postgraduate Program in Dentistry Cruzeiro do Sul University (UNICSUL) Sao Paulo Sao Paulo Brazil
| | - Sérgio Lopes
- Department of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP) São José dos Campos Sao Paulo Sao Paulo Brazil
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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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