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Uppal S, Kumar Shrivastava P, Khan A, Sharma A, Kumar Shrivastav A. Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review. Int J Med Inform 2024; 186:105421. [PMID: 38552265 DOI: 10.1016/j.ijmedinf.2024.105421] [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: 11/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
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Affiliation(s)
- Simran Uppal
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | | | - Atiya Khan
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Aditi Sharma
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
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Mahmood H, Shaban M, Rajpoot N, Khurram SA. Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer 2021; 124:1934-1940. [PMID: 33875821 PMCID: PMC8184820 DOI: 10.1038/s41416-021-01386-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/11/2021] [Accepted: 03/31/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. METHODS Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009-2020). No restrictions were placed on the AI/ML method or imaging modality used. RESULTS In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). CONCLUSIONS There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.
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Affiliation(s)
- Hanya Mahmood
- Academic Unit of Oral & Maxillofacial Surgery, School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
| | - Muhammad Shaban
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Syed A Khurram
- Unit of Oral & Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
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Mei HX, Cheng JH, Li YZ, Ma HS, Zhang KW, Shou YK, Li Y. [Advances in the application of machine learning in maxillofacial cysts and tumors]. HUA XI KOU QIANG YI XUE ZA ZHI = HUAXI KOUQIANG YIXUE ZAZHI = WEST CHINA JOURNAL OF STOMATOLOGY 2020; 38:687-691. [PMID: 33377348 PMCID: PMC7738924 DOI: 10.7518/hxkq.2020.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 01/19/2020] [Indexed: 02/05/2023]
Abstract
The application of artificial intelligence in medicine has gradually received attention along with its development. Many studies have shown that machine learning has a wide range of applications in stomatology, especially in the clinical diagnosis and treatment of maxillofacial cysts and tumors. This article reviews the application of machine learning in maxillofacial cyst and tumor to provide a new method for the diagnosis of oral and maxillofacial diseases.
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Affiliation(s)
- Hong-Xiang Mei
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Jun-Hao Cheng
- College of Computer Science, Sichuan University, Chengdu 610041, China
| | - Yi-Zhou Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Huang-Shui Ma
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Kai-Wen Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yu-Ke Shou
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yang Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
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Sunny S, Baby A, James BL, Balaji D, N. V. A, Rana MH, Gurpur P, Skandarajah A, D’Ambrosio M, Ramanjinappa RD, Mohan SP, Raghavan N, Kandasarma U, N. S, Raghavan S, Hedne N, Koch F, Fletcher DA, Selvam S, Kollegal M, N. PB, Ladic L, Suresh A, Pandya HJ, Kuriakose MA. A smart tele-cytology point-of-care platform for oral cancer screening. PLoS One 2019; 14:e0224885. [PMID: 31730638 PMCID: PMC6857853 DOI: 10.1371/journal.pone.0224885] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/23/2019] [Indexed: 12/14/2022] Open
Abstract
Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84–86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67–0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.
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Affiliation(s)
- Sumsum Sunny
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
- Manipal Academy of Higher Education, Manipal, Karnataka, India
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Arun Baby
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Bonney Lee James
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
| | - Dev Balaji
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Aparna N. V.
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Maitreya H. Rana
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | | | - Arunan Skandarajah
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | - Michael D’Ambrosio
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | | | - Sunil Paramel Mohan
- Department of Oral and Maxillofacial pathology, Sree Anjaneya Dental College, Kozhikode, Kerala, India
| | - Nisheena Raghavan
- Department of Pathology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
| | - Uma Kandasarma
- Department of Oral and Maxillofacial Pathology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Sangeetha N.
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Subhasini Raghavan
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Naveen Hedne
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
| | - Felix Koch
- University of Mainz, 55099, Mainz, Germany
| | - Daniel A. Fletcher
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | - Sumithra Selvam
- Division of Epidemiology and Biostatistics, St. John’s Research Institute, St. John’s National Academy of Health Sciences, Bangalore, India
| | | | - Praveen Birur N.
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Lance Ladic
- Siemens Healthineers, Malvern, Pennsylvania, United States of America
| | - Amritha Suresh
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
| | - Hardik J. Pandya
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
- * E-mail: (HJP); (MAK)
| | - Moni Abraham Kuriakose
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
- * E-mail: (HJP); (MAK)
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Current evidence on DNA aneuploidy cytology in noninvasive detection of oral cancer. Oral Oncol 2019; 101:104367. [PMID: 31300271 DOI: 10.1016/j.oraloncology.2019.07.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 07/02/2019] [Accepted: 07/06/2019] [Indexed: 02/07/2023]
Abstract
DNA-aneuploidy cytology as a promising noninvasive tool in diagnosing oral precancer and cancer has been proposed in 2015. In this letter, we identified 9 studies on DNA aneuploidy cytology with special emphasis on using fresh tissue sample in detection of oral precancer and cancer. Evidence was updated as follows, for detection of OSCC in general oral lesions, the pooled sensitivity and specificity was 84.8 and 99.0 respectively; for discrimination of dysplasia and OSCC form oral lesions, the sensitivity and specificity was 75.7 and 76.8 respectively. On the whole, current evidence on the theme is not robust, and multicenter prospective studies are needed to consolidate the evidence.
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Chung CM, Hung CC, Lee CH, Lee CP, Lee KW, Chen MK, Yeh KT, Ko YC. Variants in FAT1 and COL9A1 genes in male population with or without substance use to assess the risk factors for oral malignancy. PLoS One 2019; 14:e0210901. [PMID: 30657779 PMCID: PMC6338366 DOI: 10.1371/journal.pone.0210901] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 01/03/2019] [Indexed: 12/17/2022] Open
Abstract
A number of genetic variants were suggested to be associated with oral malignancy, few variants can be replicated. The aim of this study was to identify significant variants that enhanced personal risk prediction for oral malignancy. A total of 360 patients diagnosed with oral squamous cell carcinoma, 486 controls and 17 newly diagnosed patients with OPMD including leukoplakia or oral submucous fibrosis were recruited. Fifteen tagSNPs which were derived from somatic mutations were genotyped and examined in associations with the occurrence of oral malignancy. Environmental variables along with the SNPs data were used to developed risk predictive models for oral malignancy occurrence. The stepwise model analysis was conducted to fit the best model in an economically efficient way. Two tagSNPs, rs28647489 in FAT1 gene and rs550675 in COL9A1 gene, were significantly associated with the risk of oral malignancy. The sensitivity and specificity were 85.7% and 85.5%, respectively (area under the receiver operating characteristic curve (AUC) was 0.91) for predicting oral squamous cell carcinoma occurrence with the combined genetic variants, betel-quid, alcohol and age. The AUC for OPMD was only 0.69. The predictive probability of squamous cell carcinoma occurrence for genetic risk score without substance use increased from 10% up to 43%; with substance use increased from 73% up to 92%. Genetic variants with or without substance use may enhance risk prediction for oral malignancy occurrence in male population. The prediction model may be useful as a clinical index for oral malignancy occurrence and its risk assessments.
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Affiliation(s)
- Chia-Min Chung
- Environment-Omics-Disease Research Center, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan
| | - Chung-Chieh Hung
- Graduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan
| | - Chien-Hung Lee
- Department of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chi-Pin Lee
- Environment-Omics-Disease Research Center, China Medical University Hospital, Taichung, Taiwan
| | - Ka-Wo Lee
- Department of Otolaryngology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Mu-Kuan Chen
- Oral Cancer Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Kun-Tu Yeh
- Department of Pathology, Changhua Christian Hospital, Changhua, Taiwan
| | - Ying-Chin Ko
- Environment-Omics-Disease Research Center, China Medical University Hospital, Taichung, Taiwan
- * E-mail: ,
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Liu Y, Li Y, Fu Y, Liu T, Liu X, Zhang X, Fu J, Guan X, Chen T, Chen X, Sun Z. Quantitative prediction of oral cancer risk in patients with oral leukoplakia. Oncotarget 2018; 8:46057-46064. [PMID: 28545021 PMCID: PMC5542248 DOI: 10.18632/oncotarget.17550] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 02/28/2017] [Indexed: 12/16/2022] Open
Abstract
Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.
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Affiliation(s)
- Yao Liu
- Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Yicheng Li
- Cancer Research Program, Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, USA
| | - Yue Fu
- Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Tong Liu
- Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Xiaoyong Liu
- Department of Pathology, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Xinyan Zhang
- Beijing Institute of Dental Research, School of Stomatology, Capital Medical University, Beijing, China
| | - Jie Fu
- Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Xiaobing Guan
- Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Tong Chen
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Xiaoxin Chen
- Cancer Research Program, Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, USA
| | - Zheng Sun
- Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
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Yang X, Xiao X, Wu W, Shen X, Zhou Z, Liu W, Shi L. Cytological study of DNA content and nuclear morphometric analysis for aid in the diagnosis of high-grade dysplasia within oral leukoplakia. Oral Surg Oral Med Oral Pathol Oral Radiol 2017; 124:280-285. [PMID: 28732697 DOI: 10.1016/j.oooo.2017.05.509] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 05/22/2017] [Accepted: 05/25/2017] [Indexed: 12/31/2022]
Abstract
OBJECTIVE To quantitatively examine the DNA content and nuclear morphometric status of oral leukoplakia (OL) and investigate its association with the degree of dysplasia in a cytologic study. STUDY DESIGN Oral cytobrush biopsy was carried out to obtain exfoliative epithelial cells from lesions before scalpel biopsy at the same location in a blinded series of 70 patients with OL. Analysis of nuclear morphometry and DNA content status using image cytometry was performed with oral smears stained with the Feulgen-thionin method. RESULTS Nuclear morphometric analysis revealed significant differences in DNA content amount, DNA index, nuclear area, nuclear radius, nuclear intensity, sphericity, entropy, and fractal dimension (all P < .01) between low-grade and high-grade dysplasia. DNA content analysis identified 34 patients with OL (48.6%) with DNA content abnormality. Nonhomogeneous lesion (P = .018) and high-grade dysplasia (P = .008) were significantly associated with abnormal DNA content. Importantly, the positive correlation between the degree of oral dysplasia and DNA content status was significant (P = .004, correlation coefficient = 0.342). CONCLUSION Cytology analysis of DNA content and nuclear morphometric status using image cytometry may support their use as a screening and monitoring tool for OL progression.
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Affiliation(s)
- Xi Yang
- Department of Oral and Maxillofacial-Head and Neck Oncology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuan Xiao
- Oral Biomedical Engineering Laboratory, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Wenyan Wu
- Department of Oral Mucosal Diseases, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Xuemin Shen
- Department of Oral Mucosal Diseases, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Zengtong Zhou
- Department of Oral Mucosal Diseases, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Wei Liu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Linjun Shi
- Department of Oral Mucosal Diseases, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai, China.
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