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Song Y, Ma S, Mao B, Xu K, Liu Y, Ma J, Jia J. Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT. Dentomaxillofac Radiol 2024; 53:316-324. [PMID: 38627247 PMCID: PMC11211686 DOI: 10.1093/dmfr/twae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/03/2024] [Accepted: 04/11/2024] [Indexed: 06/29/2024] Open
Abstract
OBJECTIVES Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance. METHODS We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists. RESULTS Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better. CONCLUSIONS Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC. ADVANCES IN KNOWLEDGE ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.
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Affiliation(s)
- Yang Song
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Sirui Ma
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
- Department of Oral and Maxillofacial-Head and Neck Oncology, School and Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
| | - Bing Mao
- Zhengzhou University People's Hospital (Henan Provincial People's Hospital), Weiwu Road, Zhengzhou, 450003, China
| | - Kun Xu
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Yuan Liu
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Jingdong Ma
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Jun Jia
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
- Department of Oral and Maxillofacial-Head and Neck Oncology, School and Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
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Shi YJ, Li JP, Wang Y, Ma RH, Wang YL, Guo Y, Li G. Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress. Dentomaxillofac Radiol 2024; 53:271-280. [PMID: 38814810 PMCID: PMC11211683 DOI: 10.1093/dmfr/twae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024] Open
Abstract
Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.
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Affiliation(s)
- Yu-Jie Shi
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Ju-Peng Li
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Yue Wang
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Ruo-Han Ma
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
| | - Yan-Lin Wang
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
| | - Yong Guo
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Gang Li
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
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Yomtako S, Watanabe H, Kuribayashi A, Sakamoto J, Miura M. Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images. Dentomaxillofac Radiol 2024; 53:281-288. [PMID: 38565278 PMCID: PMC11211680 DOI: 10.1093/dmfr/twae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/24/2024] [Accepted: 02/24/2024] [Indexed: 04/04/2024] Open
Abstract
OBJECTIVES This study aimed to establish a method for differentiating radicular cysts from granulomas via texture analysis (TA) of multi-slice computed tomography (CT) images. METHODS A total of 222 lesions with multi-slice computed tomography images acquired at our hospital between 2013 and 2022 that were pathologically diagnosed were included in this study. Cases of contrast-enhanced images, severe metallic artefacts, and lesions that were not sufficiently large to be analysed were excluded. The images were chronologically divided into a training group and a validation group. The radiological characteristics were determined. Subsequently, a TA was performed. Pyradiomics software was used for the TA of three-dimensionally segmented volumes extracted from 2 mm slice thickness images with a soft-tissue algorithm. Features that differed significantly between the two lesions in the training group were extracted and used to create machine-learning models. The discriminative ability of these models was evaluated in the validation group using receiver operating characteristic curve analysis. RESULTS A total of 131 lesions, comprising 28 radicular cysts and 103 granulomas, were analysed. Forty-three texture features that exhibited significant variations were extracted. A support vector machine and decision tree model, with areas under the curves of 0.829 and 0.803, respectively, were created. These models showed high discriminative abilities, even for the validation group, with areas under the curve of 0.727 and 0.701, respectively. Both models showed superior performance compared with that of the models based on radiographic findings. CONCLUSION Discriminatory models were established for the TA of radicular cysts and granulomas using CT images.
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Affiliation(s)
- Supasith Yomtako
- Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
- School of Dentistry, Mae Fah Luang University, 333 Mool, Thasud, Muang, Chiang Rai, Thailand
| | - Hiroshi Watanabe
- Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
| | - Ami Kuribayashi
- Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
| | - Junichiro Sakamoto
- Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
| | - Masahiko Miura
- Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
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Fang S, Wang Y, He Y, Yu T, Xie Y, Cai Y, Li W, Wang Y, Huang Z. Machine Learning Model Based on Radiomics for Preoperative Differentiation of Jaw Cystic Lesions. Otolaryngol Head Neck Surg 2024; 170:1561-1569. [PMID: 38557958 DOI: 10.1002/ohn.744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE This study aims to use machine learning techniques together with radiomics methods to build a preoperative predictive diagnostic model from spiral computed tomography (CT) images. The model is intended for the differential diagnosis of common jaw cystic lesions. STUDY DESIGN Retrospective, case-control study. SETTING This retrospective study was conducted at Sun Yat-sen Memorial Hospital of Sun Yat-sen University (Guangzhou, Guangdong, China). All the data used to build the predictive diagnostic model were collected from 160 patients, who were treated at the Department of Oral and Maxillofacial Surgery at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 2019 and 2023. METHODS We included a total of 160 patients in this study. We extracted 107 radiomic features from each patient's CT scan images. After a feature selection process, we chose 15 of these radiomic features to construct the predictive diagnostic model. RESULTS Among the preoperative predictive diagnostic models built using 3 different machine learning methods (support vector machine, random forest [RF], and multivariate logistic regression), the RF model showed the best predictive performance. It demonstrated a sensitivity of 0.923, a specificity of 0.643, an accuracy of 0.825, and an area under the receiver operating characteristic curve of 0.810. CONCLUSION The preoperative predictive model, based on spiral CT radiomics and machine learning algorithms, shows promising differential diagnostic capabilities. For common jaw cystic lesions, this predictive model has potential clinical application value, providing a scientific reference for treatment decisions.
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Affiliation(s)
- Songling Fang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yuepeng Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yilin He
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Taihui Yu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yutong Xie
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
| | - Yongkang Cai
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Wenhao Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yan Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Zhiquan Huang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
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Bayat N, Ghavimi MA, Rahimipour K, Razi S, Esmaeili F. Radiographic texture analysis of the hard tissue changes following socket preservation with allograft and xenograft materials for dental implantation: a randomized clinical trial. Oral Maxillofac Surg 2024; 28:705-713. [PMID: 37981622 DOI: 10.1007/s10006-023-01193-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/10/2023] [Indexed: 11/21/2023]
Abstract
OBJECTIVES This study aimed to assess the hard tissue changes following socket preservation with allograft and xenograft materials for dental implantation by texture analysis (TA) using cone-beam computed tomography (CBCT). MATERIALS AND METHODS This prospective clinical trial was conducted on 25 patients who required the extraction of carious mandibular posterior teeth and their subsequent replacement with dental implants. The patients were categorized into three groups: (I) no socket preservation, (II) socket preservation with xenograft material, and (III) socket preservation with allograft material. Four months after tooth extraction, the patients were recalled for preoperative assessment before dental implantation, and CBCT scans were obtained (Kvp:110, mA:1.94, S:3.6). MaZda software was used to compare homogeneity, contrast, and texture complexity on axial CBCT sections among the three groups. RESULTS Significant differences existed among the three groups in all parameters (P < 0.05) except for the mean correlation parameter (P > 0.05). The results showed no significant difference between the no graft and xenograft groups regarding contrast and differential (dif.) entropy (P > 0.05). Also, no significant difference was found between the xenograft and allograft groups regarding the dif. variance and also between the no graft and allograft groups regarding the inverse difference moment(InvDfMom) and dif. variance parameters (P > 0.05). All other pairwise comparisons revealed significant differences (P < 0.05). CONCLUSION TA can be used for the quantification of radiographic changes of bone following socket preservation and potentially accelerate the process of decision-making for dental implant treatment.
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Affiliation(s)
- Narges Bayat
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad Ali Ghavimi
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Kasra Rahimipour
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sedigheh Razi
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Farzad Esmaeili
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran.
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Park S, Jeon SJ, Yeom HG, Seo MS. Differential diagnosis of cemento-osseous dysplasia and periapical cyst using texture analysis of CBCT. BMC Oral Health 2024; 24:442. [PMID: 38605361 PMCID: PMC11008037 DOI: 10.1186/s12903-024-04208-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/29/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Radiolucencies found at the root apex in patients with cemento-osseous dysplasia (COD) may be mistaken for periapical cysts (PC) of endodontic origin. The purpose of this study was to examine the utility of quantitative texture analysis using cone-beam computed tomography (CBCT) to differentiate between COD and PC. METHODS Patients who underwent CBCT at Wonkwang University Daejeon Dental Hospital between January 2019 and December 2022 and were diagnosed with COD and PC by clinical, radiologic, and, if necessary, histopathologic examination were included. Twenty-five patients each were retrospectively enrolled in the COD and PC group. All lesions observed on axial CBCT images were manually segmented using the open-access software MaZda version 4.6 to establish the regions of interest, which were then subjected to texture analysis. Among the 279 texture features obtained, 10 texture features with the highest Fisher coefficients were selected. Statistical analysis was performed using the Mann-Whitney U-test, Welch's t-test, or Student's t-test. Texture features that showed significant differences were subjected to receiver operating characteristics (ROC) curve analysis to evaluate the differential diagnostic ability of COD and PC. RESULTS The COD group consisted of 22 men and 3 women, while the PC group consisted of 14 men and 11 women, showing a significant difference between the two groups in terms of sex (p=0.003). The 10 selected texture features belonged to the gray level co-occurrence matrix and included the sum of average, sum of entropy, entropy, and difference of entropy. All 10 selected texture features showed statistically significant differences (p<0.05) when comparing patients with COD (n=25) versus those with PC (n=25), osteolytic-stage COD (n=11) versus PC (n=25), and osteolytic-stage COD (n=11) versus cementoblastic-stage COD (n=14). ROC curve analysis to determine the ability to differentiate between COD and PC showed a high area under the curve ranging from 0.96 to 0.98. CONCLUSION Texture analysis of CBCT images has shown good diagnostic value in the differential diagnosis of COD and PC, which can help prevent unnecessary endodontic treatment, invasive biopsy, or surgical intervention associated with increased risk of infection.
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Affiliation(s)
- Sanghee Park
- Department of Conservative Dentistry, Wonkang University Daejeon Dental Hospital, 77 Dunsan-Ro, Seo-Gu, Daejeon, 302-120, Republic of Korea
| | - Su-Jin Jeon
- Department of Conservative Dentistry, Wonkang University Daejeon Dental Hospital, 77 Dunsan-Ro, Seo-Gu, Daejeon, 302-120, Republic of Korea
| | - Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Min-Seock Seo
- Department of Conservative Dentistry, Wonkang University Daejeon Dental Hospital, 77 Dunsan-Ro, Seo-Gu, Daejeon, 302-120, Republic of Korea.
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Muraoka H, Kaneda T, Kondo T, Okada S, Tokunaga S. Differential diagnosis of parotid gland tumors using apparent diffusion coefficient, texture features, and their combination. Dentomaxillofac Radiol 2023; 52:20220404. [PMID: 37015250 PMCID: PMC10170173 DOI: 10.1259/dmfr.20220404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 04/06/2023] Open
Abstract
OBJECTIVES Warthin's tumors (WT) and pleomorphic adenomas (PA) are the commonest parotid gland tumors; however, their differentiation remains difficult. This study aimed to investigate the utility of the apparent diffusion coefficient (ADC) value, texture features, and their combination for the differential diagnosis of parotid gland tumors. METHODS Patients who underwent magnetic resonance imaging (MRI) between April 2008 and March 2021 for parotid gland tumors were included and divided into two groups according to the tumor type: WT and PA. The tumor types were used as predictor variables, while the ADC value, texture features, and their combination were the outcome variables. Texture features were measured on short tau inversion recovery (STIR) images and selected using the Fisher's coefficient method and probability of error, and average correlation coefficients. The Mann-Whitney U-test was used to analyze bivariate statistics. Receiver operating characteristic curve analysis was used to assess the ability of the ADC value, texture features, and their combination to distinguishing between the two tumor types. RESULTS A total of 22 patients were included, 11 in each group. The ADC value, 10 texture features, and their combination were significantly different between the two groups (p < .001). Moreover, all three variables had high area under the curve values of 0.93-0.96. CONCLUSION The ADC value, texture features, and their combination demonstrated good diagnostic ability to distinguish between WTs and PAs. This method may be used to aid the differential diagnosis of parotid gland tumors, thereby promoting timely and adequate treatment.
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Affiliation(s)
- Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, Japan
| | - Takumi Kondo
- Department of Radiology, Nihon University School of Dentistry at Matsudo 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, Japan
| | - Shunya Okada
- Department of Radiology, Nihon University School of Dentistry at Matsudo 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, Japan
| | - Satoshi Tokunaga
- Department of Radiology, Nihon University School of Dentistry at Matsudo 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, Japan
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Magnetic resonance image texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis: a preliminary report. Oral Radiol 2023; 39:242-247. [PMID: 35701653 DOI: 10.1007/s11282-022-00625-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: 04/06/2022] [Accepted: 05/11/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI) is useful for assessing temporomandibular disorders (TMDs). However, few studies have attempted texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis (RA). This study aims to investigate the usefulness of MRI texture analysis of the lateral pterygoid muscle of patients with RA of the temporomandibular joint (TMJ). METHODS We analyzed the data from 36 patients (18 non-RA patients and 18 RA patients) who complained of pain and underwent MRI between April 2008 and August 2021. From the MRI scans of these patients, 279 radiomics features were extracted using STIR image data of the ROIs on the lateral pterygoid muscle of patients with RA and analyzed using MaZda ver. 3.3. Seven gray-level co-occurrence matrix features (Sum entropy, Sum variance) were picked up using the Fisher coefficient, for comparison between the RA and non-RA groups. Data analysis was performed using the Mann-Whitney U test A P value of < 0.05 was considered as statistically significant. RESULTS All seven lateral pterygoid muscle radiomic features indicated significant differences between the non-RA and RA groups (P < 0.05). CONCLUSION MRI texture analysis shows potential for application in radiomics diagnosis of RA in TMJ.
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Santos GNM, da Silva HEC, Ossege FEL, Figueiredo PTDS, Melo NDS, Stefani CM, Leite AF. Radiomics in bone pathology of the jaws. Dentomaxillofac Radiol 2023; 52:20220225. [PMID: 36416666 PMCID: PMC9793454 DOI: 10.1259/dmfr.20220225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/02/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response. METHODS A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results. CONCLUSIONS GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.
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Affiliation(s)
| | | | | | | | - Nilce de Santos Melo
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| | - Cristine Miron Stefani
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| | - André Ferreira Leite
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
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Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst. Sci Rep 2022; 12:20047. [PMID: 36414657 PMCID: PMC9681845 DOI: 10.1038/s41598-022-20802-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/19/2022] [Indexed: 11/23/2022] Open
Abstract
The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann-Whitney test. Mann-Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.
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Yang JS, Cha JY, Lee JY, Choi SH. Radiographical characteristics and traction duration of impacted maxillary canine requiring surgical exposure and orthodontic traction: a cross-sectional study. Sci Rep 2022; 12:19183. [PMID: 36357464 PMCID: PMC9649639 DOI: 10.1038/s41598-022-23232-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
This cross-sectional study aimed to classify the radiographical characteristics of impacted maxillary canines that were surgically exposed following orthodontic traction and to find out which factor is most closely related to traction duration. This study enrolled 74 patients with 87 maxillary canines. Cone-beam computed tomography images, panoramic radiographs, and medical records were analyzed. Cystic-appearing lesion and resorption of adjacent roots were observed in 26.4% and 23.0% of cases, respectively. Impacted maxillary canines were mostly distributed in the lateral incisor area. The mean (± standard deviation) traction duration for the 47 teeth that met the study criteria was 13.9 (± 8.9) months. Impacted maxillary canines treated with surgical exposure and orthodontic traction showed increasing possibilities of palatal impaction and resorption of the adjacent root as they were located mesially (p < 0.05). The distance from the occlusal plane to the impacted maxillary canine showed the strongest positive correlation with traction duration (r = 0.519, p < 0.01). When establishing treatment plans for patients with impacted maxillary canines, distance from the occlusal plane to the impacted canines, rather than the angle, should be considered in predicting the duration of treatment.
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Affiliation(s)
- Jin-Seob Yang
- grid.416665.60000 0004 0647 2391Department of Orthodontics, National Health Insurance Service Ilsan Hospital, Goyang, 10444 Republic of Korea
| | - Jung-Yul Cha
- grid.15444.300000 0004 0470 5454Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, 03722 Republic of Korea
| | - Ji-Yeon Lee
- grid.416665.60000 0004 0647 2391Department of Orthodontics, National Health Insurance Service Ilsan Hospital, Goyang, 10444 Republic of Korea
| | - Sung-Hwan Choi
- grid.15444.300000 0004 0470 5454Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, 03722 Republic of Korea
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Ghosh A, Lakshmanan M, Manchanda S, Bhalla AS, Kumar P, Bhutia O, Mridha AR. Contrast-enhanced multidetector computed tomography features and histogram analysis can differentiate ameloblastomas from central giant cell granulomas. World J Radiol 2022; 14:329-341. [PMID: 36186516 PMCID: PMC9521432 DOI: 10.4329/wjr.v14.i9.329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 07/05/2022] [Accepted: 09/02/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND No qualitative or quantitative analysis of contrast-enhanced computed tomography (CT) images has been reported for the differentiation between ameloblastomas and central giant cell granulomas (CGCGs).
AIM To describe differentiating multidetector CT (MDCT) features in CGCGs and ameloblastomas and to compare differences in enhancement of these lesions qualitatively and using histogram analysis.
METHODS MDCT of CGCGs and ameloblastomas was retrospectively reviewed to evaluate qualitative imaging descriptors. Histogram analysis was used to compare the extent of enhancement of the soft tissue. Fisher’s exact tests and Mann–Whitney U test were used for statistical analysis (P < 0.05).
RESULTS Twelve CGCGs and 33 ameloblastomas were reviewed. Ameloblastomas had a predilection for the posterior mandible with none of the CGCGs involving the angle. CGCGs were multilocular (58.3%), with a mixed lytic sclerotic appearance (75%). Soft tissue component was present in 91% of CGCGs, which showed hyperenhancement (compared to surrounding muscles) in 50% of cases, while the remaining showed isoenhancement. Matrix mineralization was present in 83.3% of cases. Ameloblastomas presented as a unilocular (66.7%), lytic (60.6%) masses with solid components present in 81.8% of cases. However, the solid component showed isoenhancement in 63%. No matrix mineralization was present in 69.7% of cases. Quantitatively, the enhancement of soft tissue in CGCG was significantly higher than in ameloblastoma on histogram analysis (P < 0.05), with a minimum enhancement of > 49.05 HU in the tumour providing 100% sensitivity and 85% specificity in identifying a CGCG.
CONCLUSION A multilocular, lytic sclerotic lesion with significant hyperenhancement in soft tissue, which spares the angle of the mandible and has matrix mineralization, should indicate prospective diagnosis of CGCG.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Meyyappan Lakshmanan
- Department of Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Smita Manchanda
- Department of Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Ashu Seith Bhalla
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Prem Kumar
- Department of Oral & Maxillofacial Surgery, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Ongkila Bhutia
- Department of Oral & Maxillofacial Surgery, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Asit Ranjan Mridha
- Department of Pathology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
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Feher B, Kuchler U, Schwendicke F, Schneider L, Cejudo Grano de Oro JE, Xi T, Vinayahalingam S, Hsu TMH, Brinz J, Chaurasia A, Dhingra K, Gaudin RA, Mohammad-Rahimi H, Pereira N, Perez-Pastor F, Tryfonos O, Uribe SE, Hanisch M, Krois J. Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning. Diagnostics (Basel) 2022; 12:diagnostics12081968. [PMID: 36010318 PMCID: PMC9406703 DOI: 10.3390/diagnostics12081968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/24/2022] Open
Abstract
The detection and classification of cystic lesions of the jaw is of high clinical relevance and represents a topic of interest in medical artificial intelligence research. The human clinical diagnostic reasoning process uses contextual information, including the spatial relation of the detected lesion to other anatomical structures, to establish a preliminary classification. Here, we aimed to emulate clinical diagnostic reasoning step by step by using a combined object detection and image segmentation approach on panoramic radiographs (OPGs). We used a multicenter training dataset of 855 OPGs (all positives) and an evaluation set of 384 OPGs (240 negatives). We further compared our models to an international human control group of ten dental professionals from seven countries. The object detection model achieved an average precision of 0.42 (intersection over union (IoU): 0.50, maximal detections: 100) and an average recall of 0.394 (IoU: 0.50–0.95, maximal detections: 100). The classification model achieved a sensitivity of 0.84 for odontogenic cysts and 0.56 for non-odontogenic cysts as well as a specificity of 0.59 for odontogenic cysts and 0.84 for non-odontogenic cysts (IoU: 0.30). The human control group achieved a sensitivity of 0.70 for odontogenic cysts, 0.44 for non-odontogenic cysts, and 0.56 for OPGs without cysts as well as a specificity of 0.62 for odontogenic cysts, 0.95 for non-odontogenic cysts, and 0.76 for OPGs without cysts. Taken together, our results show that a combined object detection and image segmentation approach is feasible in emulating the human clinical diagnostic reasoning process in classifying cystic lesions of the jaw.
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Affiliation(s)
- Balazs Feher
- Department of Oral Surgery, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria
- Competence Center Oral Biology, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria
- Correspondence: ; Tel.: +43-1-40070-2623
| | - Ulrike Kuchler
- Department of Oral Surgery, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité—University Medicine Berlin, 14197 Berlin, Germany
| | - Lisa Schneider
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité—University Medicine Berlin, 14197 Berlin, Germany
| | - Jose Eduardo Cejudo Grano de Oro
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité—University Medicine Berlin, 14197 Berlin, Germany
| | - Tong Xi
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands
| | - Tzu-Ming Harry Hsu
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Janet Brinz
- Department of Restorative Dentistry, Ludwig-Maximilians-University of Munich, 80336 Munich, Germany
| | - Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George’s Medical University, Lucknow 226003, India
| | - Kunaal Dhingra
- Periodontics Division, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Robert Andre Gaudin
- Department of Oral and Maxillofacial Surgery, Charité—University Medicine Berlin, 14197 Berlin, Germany
- Berlin Institute of Health, 10178 Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1416634793, Iran
| | - Nielsen Pereira
- Private Practice in Oral and Maxillofacial Radiology, Rio de Janeiro 22430-000, Brazil
| | - Francesc Perez-Pastor
- Servei Salut Dental, Gerencia Atencio Primaria, Institut Balear de la Salut, 07003 Palma, Spain
| | - Olga Tryfonos
- Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam, 1081 LA Amsterdam, The Netherlands
| | - Sergio E. Uribe
- Department of Conservative Dentistry & Oral Health, Riga Stradins University, LV-1007 Riga, Latvia
- School of Dentistry, Universidad Austral de Chile, Valdivia 5110566, Chile
- Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, LV-1658 Riga, Latvia
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, University Clinic Münster, 48143 Münster, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité—University Medicine Berlin, 14197 Berlin, Germany
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Cui Z, Ren G, Cai R, Wu C, Shi H, Wang X, Zhu M. MRI-based texture analysis for differentiate between pediatric posterior fossa ependymoma type A and B. Eur J Radiol 2022; 152:110288. [DOI: 10.1016/j.ejrad.2022.110288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/01/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4603475. [PMID: 34594482 PMCID: PMC8478545 DOI: 10.1155/2021/4603475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 09/08/2021] [Indexed: 11/18/2022]
Abstract
This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, control, and experimental groups, whose images were treated by the manual segmentation method, threshold segmentation algorithm, and full convolutional neural network (FCNN) DL algorithm, respectively. Then, effects of different methods on oral lesion CBCT image recognition and segmentation were analyzed. The results showed that there was no substantial difference in the number of patients with different types of oral lesions among three groups (P > 0.05). The accuracy of lesion segmentation in the experimental group was as high as 98.3%, while those of the blank group and control group were 78.4% and 62.1%, respectively. The accuracy of segmentation of CBCT images in the blank group and control group was considerably inferior to the experimental group (P < 0.05). The segmentation effect on the lesion and the lesion model in the experimental group and control group was evidently superior to the blank group (P < 0.05). In short, the image segmentation accuracy of the FCNN DL method was better than the traditional manual segmentation and threshold segmentation algorithms. Applying the DL segmentation algorithm to CBCT images of oral lesions can accurately identify and segment the lesions.
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Fractal Dimension and Texture Analysis of Lesion Autofluorescence in the Evaluation of Oral Lichen Planus Treatment Effectiveness. MATERIALS 2021; 14:ma14185448. [PMID: 34576672 PMCID: PMC8466626 DOI: 10.3390/ma14185448] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/14/2021] [Accepted: 09/17/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Oral Lichen planus (OLP) is a chronic inflammatory disease. Topical steroids are used as the treatment of choice. The alternative is photodynamic therapy (PDT). The study aimed to fabricate optimal biodegradable matrices for methylene blue or triamcinolone acetonide because of a lack of currently commercially available carriers that could adhere to the mucous. METHODS The study was designed as a 12-week single-blind prospective randomized clinical trial with 30 patients, full contralateral split-mouth design. Matrices for steroid and photosensitizer and laser device were fabricated. Fractal and texture analysis of photographs, taken in 405, 450, 405 + 450 nm wavelength, of lesions was performed to increase the objectivity of the assessment of treatment. RESULTS We achieved two total responses for treatment in case of steroid therapy and one in the case of PDT. Partial response was noted in 17 lesions treated using local steroid therapy and 21 in the case of PDT. No statistically significant differences were found between the effectiveness of both used methods. Statistically significant differences in fractal dimension before and after treatment were observed only in the analysis of photographs taken in 405 + 450 nm wavelength. CONCLUSIONS Photodynamic therapy and topical steroid therapy are effective methods for treating OLP. Using a carrier offers the possibility of a more predictable and effective method of drug delivery into the mucous membrane. Autofluorescence enables the detection of lesions especially at the early stage of their development.
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Buch K. Invited Commentary: Differential Diagnosis for Radiopaque Jaw Lesions-An Algorithmic Approach. Radiographics 2021; 41:E121-E122. [PMID: 34086498 DOI: 10.1148/rg.2021210034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Karen Buch
- Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114
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Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis. Heliyon 2020; 6:e05194. [PMID: 33088959 PMCID: PMC7560585 DOI: 10.1016/j.heliyon.2020.e05194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/25/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
Objective This study aimed to investigate the use of texture analysis for characterization of radicular cysts and periapical granulomas and to assess its efficacy to differentiate between both lesions with histological diagnosis. Methods Cone beam computed tomography (CBCT) images were obtained from 19 patients with 25 periapical lesions (14 radicular cysts and 11 periapical granulomas) confirmed by biopsy. Regions of interest were created in the lesions from which 11 texture parameters were calculated. Spearman's correlation analysis was performed and adjusted with Benjamini-Hochberg false discovery rate procedure (FDR <0.005). Results The texture parameters used to differentiate the lesions were assessed by using a receiver operating characteristic analysis. Five texture parameters were predictive of lesion differentiation for eight positions: angular second moment; sum of squares; sum of average; contrast; correlation. Conclusion Texture analysis of CBCT scans distinguishes radicular cysts from periapical granulomas and can be a promising diagnostic tool for periapical lesions. Clinical significance Texture analysis can be used in diagnostic and treatment monitoring to provide supplementary information.
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Lv M, Zhou Z, Tang Q, Xu J, Huang Q, Lu L, Duan S, Zhu J, Li H. Differentiation of usual vertebral compression fractures using CT histogram analysis as quantitative biomarkers: A proof-of-principle study. Eur J Radiol 2020; 131:109264. [PMID: 32920220 DOI: 10.1016/j.ejrad.2020.109264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/19/2020] [Accepted: 08/24/2020] [Indexed: 01/20/2023]
Abstract
PURPOSE To investigate the utility of CT histogram analysis (CTHA) for discrimination of traumatic, osteoporotic and malignant fractures in patients with vertebral compression fractures (VCFs). To evaluate the feasibility and accuracy of CTHA in differentiating non-malignant (traumatic and osteoporotic) from malignant VCFs. MATERIALS AND METHODS Totally, 235 patients with VCFs were enrolled in the current experimental study. There were 132 patients with traumatic VCFs, 51 with osteoporotic VCFs and 52 with malignant VCFs, with MRI and histology as the standard references. All the patients underwent unenhanced CT scans. Nineteen histogram-based parameters were derived using Omni-Kinetics software (Omni-Kinetics, GE Healthcare). The reproducibility of those parameters was evaluated using two independent delineations conducted by two observers. These histogram parameters were compared among the three different VCFs using Kruskal-Wallis H test. Traumatic VCFs and osteoporotic VCFs were combined as non-malignant VCFs and compared with malignant VCFs using Mann-Whitney U test Multivariable logistic regression analysis was performed on the significantly different features and built a diagnosis model. Receiver operating characteristic (ROC) curve was carried out to observe the difference of diagnostic performance between the single positive parameter and the combination of parameters. RESULTS All the 19 parameters presented excellent reproducibility, with intraclass correlation coefficient values from 0.789 to 0.997. At quantitative evaluation, the best predictive histogram parameters in discrimination of the three different types of VCFs were relative min intensity (p = 0.022), relative entropy (p = 0.043), and relative frequency size (p < 0.001). Relative frequency size (p < 0.001) and relative quantile5 (p = 0.012) resulted in statistically significant difference between non-malignant and malignant VCFs. The area under ROC curve indicated that relative frequency size combined with relative quantile5 (0.754; 95 % confidence intervals: 0.661∼0.829; p < 0.001) was of best performance in differentiating malignant from non-malignant VCFs. CONCLUSIONS Our results are encouraging and suggest that histogram parameters derived from unenhanced CT could be reliable quantitative biomarkers for diff ;erential diagnosis of usual VCFs.
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Affiliation(s)
- Mu Lv
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China
| | - Zhichao Zhou
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China
| | - Qingkun Tang
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China
| | - Jie Xu
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China
| | - Qiao Huang
- Department of Radiology, Mayo Clinic, Rochester, United States
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, United States
| | | | - Jianguo Zhu
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China.
| | - Haige Li
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China
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