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Alapati R, Renslo B, Wagoner SF, Karadaghy O, Serpedin A, Kim YE, Feucht M, Wang N, Ramesh U, Bon Nieves A, Lawrence A, Virgen C, Sawaf T, Rameau A, Bur AM. Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology. Laryngoscope 2024. [PMID: 39258420 DOI: 10.1002/lary.31756] [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: 02/07/2024] [Revised: 07/25/2024] [Accepted: 08/23/2024] [Indexed: 09/12/2024]
Abstract
OBJECTIVE This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria. DATA SOURCES A comprehensive search was conducted using PubMed, Scopus, Embase, and Cochrane Database of Systematic Reviews, incorporating search terms related to "artificial intelligence," "machine learning," "deep learning," "neural network," and various head and neck neoplasms. REVIEW METHODS Two independent reviewers analyzed each published study for adherence to the 65-point TRIPOD-AI criteria. Items were classified as "Yes," "No," or "NA" for each publication. The proportion of studies satisfying each TRIPOD-AI criterion was calculated. Additionally, the evidence level for each study was evaluated independently by two reviewers using the Oxford Centre for Evidence-Based Medicine (OCEBM) Levels of Evidence. Discrepancies were reconciled through discussion until consensus was reached. RESULTS The study highlights the need for improvements in ML algorithm reporting in head and neck oncology. This includes more comprehensive descriptions of datasets, standardization of model performance reporting, and increased sharing of ML models, data, and code with the research community. Adoption of TRIPOD-AI is necessary for achieving standardized ML research reporting in head and neck oncology. CONCLUSION Current reporting of ML algorithms hinders clinical application, reproducibility, and understanding of the data used for model training. To overcome these limitations and improve patient and clinician trust, ML developers should provide open access to models, code, and source data, fostering iterative progress through community critique, thus enhancing model accuracy and mitigating biases. LEVEL OF EVIDENCE NA Laryngoscope, 2024.
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Affiliation(s)
- Rahul Alapati
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Bryan Renslo
- Department of Otolaryngology-Head & Neck Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A
| | - Sarah F Wagoner
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Omar Karadaghy
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Aisha Serpedin
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Yeo Eun Kim
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Maria Feucht
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Naomi Wang
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Uma Ramesh
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Antonio Bon Nieves
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Amelia Lawrence
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Celina Virgen
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Tuleen Sawaf
- Department of Otolaryngology-Head & Neck Surgery, University of Maryland, Baltimore, Maryland, U.S.A
| | - Anaïs Rameau
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Andrés M Bur
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
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Adachi M, Taki T, Kojima M, Sakamoto N, Matsuura K, Hayashi R, Tabuchi K, Ishikawa S, Ishii G, Sakashita S. Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists. J Pathol Clin Res 2024; 10:e12392. [PMID: 39159053 PMCID: PMC11332396 DOI: 10.1002/2056-4538.12392] [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: 04/02/2024] [Revised: 07/07/2024] [Accepted: 07/16/2024] [Indexed: 08/21/2024]
Abstract
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1-2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI-extracted information from whole slide images (WSIs), human-assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non-recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non-recurrence cases. The model integrating AI-extracted histopathological and human-assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1-2N0 tongue SCC.
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Affiliation(s)
- Masahiro Adachi
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Department of Otolaryngology, Head and Neck SurgeryUniversity of TsukubaTsukubaJapan
| | - Tetsuro Taki
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
| | - Motohiro Kojima
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Naoya Sakamoto
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Kazuto Matsuura
- Department of Head and Neck SurgeryNational Cancer Center Hospital EastKashiwaJapan
| | - Ryuichi Hayashi
- Department of Head and Neck SurgeryNational Cancer Center Hospital EastKashiwaJapan
| | - Keiji Tabuchi
- Department of Otolaryngology, Head and Neck SurgeryUniversity of TsukubaTsukubaJapan
| | - Shumpei Ishikawa
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
- Department of Preventive Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Genichiro Ishii
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of Innovative Pathology and Laboratory MedicineNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Shingo Sakashita
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
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Xu Y, Chu C, Wang Q, Xiang L, Lu M, Yan W, Huang L. Using T2-weighted magnetic resonance imaging-derived radiomics to classify cervical lymphadenopathy in children. Pediatr Radiol 2024; 54:1302-1314. [PMID: 38937304 PMCID: PMC11255022 DOI: 10.1007/s00247-024-05954-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Cervical lymphadenopathy is common in children and has diverse causes varying from benign to malignant, their similar manifestations making differential diagnosis difficult. OBJECTIVE This study aimed to investigate whether radiomic models using conventional magnetic resonance imaging (MRI) could classify pediatric cervical lymphadenopathy. METHODS A total of 419 cervical lymph nodes from 146 patients, and encompassing four common etiologies (Kikuchi disease, reactive hyperplasia, suppurative lymphadenitis and malignancy), were randomly divided into training and testing sets in a ratio of 7:3. For each lymph node, 1,218 features were extracted from T2-weighted images. Then, the least absolute shrinkage and selection operator (LASSO) models were used to select the most relevant ones. Two models were built using a support vector machine classifier, one was to classify benign and malignant lymph nodes and the other further distinguished four different diseases. The performance was assessed by receiver operating characteristic curves and decision curve analysis. RESULTS By LASSO, 20 features were selected to construct a model to distinguish benign and malignant lymph nodes, which achieved an area under the curve (AUC) of 0.89 and 0.80 in the training and testing sets, respectively. Sixteen features were selected to construct a model to distinguish four different cervical lymphadenopathies. For each etiology, Kikuchi disease, reactive hyperplasia, suppurative lymphadenitis, and malignancy, an AUC of 0.97, 0.91, 0.88, and 0.87 was achieved in the training set, and an AUC of 0.96, 0.80, 0.82, and 0.82 was achieved in the testing set, respectively. CONCLUSION MRI-derived radiomic analysis provides a promising non-invasive approach for distinguishing causes of cervical lymphadenopathy in children.
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Affiliation(s)
- Yanwen Xu
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Caiting Chu
- Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qun Wang
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linjuan Xiang
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meina Lu
- Department of Infectious Diseases, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310003, Zhejiang, China
| | - Weihui Yan
- Division of Pediatric Gastroenterology and Nutrition, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lisu Huang
- Department of Infectious Diseases, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310003, Zhejiang, China.
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Wang D, He X, Huang C, Li W, Li H, Huang C, Hu C. Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:214-224. [PMID: 38378316 DOI: 10.1016/j.oooo.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/14/2024] [Accepted: 01/28/2024] [Indexed: 02/22/2024]
Abstract
OBJECTIVE This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue. STUDY DESIGN In total, MR images of 196 patients with lingual squamous cell carcinoma were divided into training (n = 156) and test (n = 40) cohorts. Radiomics and DL features were extracted from MR images and selected to construct machine learning models. A DL radiomics nomogram was established via multivariate logistic regression by incorporating the radiomics signature, the DL signature, and MRI-reported LN status. RESULTS Nine radiomics and 3 DL features were selected. In the radiomics test cohort, the multilayer perceptron model performed best with an area under the receiver operating characteristic curve (AUC) of 0.747, but in the DL cohort, the best model (logistic regression) performed less well (AUC = 0.655). The DL radiomics nomogram showed good calibration and performance with an AUC of 0.934 (outstanding discrimination ability) in the training cohort and 0.757 (acceptable discrimination ability) in the test cohort. The decision curve analysis demonstrated that the nomogram could offer more net benefit than a single radiomics or DL signature. CONCLUSION The DL radiomics nomogram exhibited promising performance in predicting LNM, which facilitates personalized treatment of tongue cancer.
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Affiliation(s)
- Dawei Wang
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao He
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunming Huang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenqiang Li
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haosen Li
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cicheng Huang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuanyu Hu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 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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Al Hasan MM, Ghazimoghadam S, Tunlayadechanont P, Mostafiz MT, Gupta M, Roy A, Peters K, Hochhegger B, Mancuso A, Asadizanjani N, Forghani R. Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01114-w. [PMID: 38937342 DOI: 10.1007/s10278-024-01114-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 06/29/2024]
Abstract
Early and accurate detection of cervical lymph nodes is essential for the optimal management and staging of patients with head and neck malignancies. Pilot studies have demonstrated the potential for radiomic and artificial intelligence (AI) approaches in increasing diagnostic accuracy for the detection and classification of lymph nodes, but implementation of many of these approaches in real-world clinical settings would necessitate an automated lymph node segmentation pipeline as a first step. In this study, we aim to develop a non-invasive deep learning (DL) algorithm for detecting and automatically segmenting cervical lymph nodes in 25,119 CT slices from 221 normal neck contrast-enhanced CT scans from patients without head and neck cancer. We focused on the most challenging task of segmentation of small lymph nodes, evaluated multiple architectures, and employed U-Net and our adapted spatial context network to detect and segment small lymph nodes measuring 5-10 mm. The developed algorithm achieved a Dice score of 0.8084, indicating its effectiveness in detecting and segmenting cervical lymph nodes despite their small size. A segmentation framework successful in this task could represent an essential initial block for future algorithms aiming to evaluate small objects such as lymph nodes in different body parts, including small lymph nodes looking normal to the naked human eye but harboring early nodal metastases.
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Affiliation(s)
- Md Mahfuz Al Hasan
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Electrical and Computer Engineering, University of Florida College of Medicine, Gainesville, FL, USA
| | - Saba Ghazimoghadam
- Augmented Intelligence and Precision Health Laboratory, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Padcha Tunlayadechanont
- Augmented Intelligence and Precision Health Laboratory, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Mohammed Tahsin Mostafiz
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Electrical and Computer Engineering, University of Florida College of Medicine, Gainesville, FL, USA
| | - Manas Gupta
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
| | - Antika Roy
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Electrical and Computer Engineering, University of Florida College of Medicine, Gainesville, FL, USA
| | - Keith Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Navid Asadizanjani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA
- Department of Electrical and Computer Engineering, University of Florida College of Medicine, Gainesville, FL, USA
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA.
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA.
- Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL, USA.
- Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL, USA.
- Augmented Intelligence and Precision Health Laboratory, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
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Deng C, Hu J, Tang P, Xu T, He L, Zeng Z, Sheng J. Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis. Front Oncol 2024; 14:1395159. [PMID: 38957322 PMCID: PMC11217320 DOI: 10.3389/fonc.2024.1395159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 05/30/2024] [Indexed: 07/04/2024] Open
Abstract
Background The performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC. Methods We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities. Results Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists. Discussion In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).
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Affiliation(s)
| | | | | | | | | | | | - Jianfeng Sheng
- Department of Thyroid, Head, Neck and Maxillofacial Surgery, the Third Hospital of Mianyang & Sichuan Mental Health Center, Mianyang, Sichuan, China
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Han W, Wang Y, Li T, Dong Y, Dang Y, He L, Xu L, Zhou Y, Li Y, Wang X. A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer. PeerJ 2024; 12:e17254. [PMID: 38685941 PMCID: PMC11057426 DOI: 10.7717/peerj.17254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 05/02/2024] Open
Abstract
Background Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.
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Affiliation(s)
- Wei Han
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yingshu Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Tao Li
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yuke Dong
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yanwei Dang
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Liang He
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Lianfang Xu
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yuhao Zhou
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yujie Li
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Xudong Wang
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Liu B, Li M, Chen S, Cui Q. A study on the survival prediction for patients with oral cancer in southwest China. Oral Dis 2024; 30:966-976. [PMID: 36630586 DOI: 10.1111/odi.14500] [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: 06/27/2022] [Revised: 12/21/2022] [Accepted: 01/04/2023] [Indexed: 01/13/2023]
Abstract
OBJECTIVE The prognostic value of the variables were evaluated in 418 postoperative oral cancer patients with oral cancer in southwest China between January 2013 and December 2020. Nomogram was developed based on the study, and its predictive performance and clinical utility were evaluated. RESULTS The univariate analysis showed gender, preoperative fibrinogen, preoperative platelet-to-lymphocyte ratio (PLR), and preoperative neutrophil-to-lymphocyte ratio, flap repair of defect, functional neck dissection (FND), tumor differentiation, tumor, node, metastasis stage, lymph node metastasis, the maximum tumor diameter, and postoperative radiotherapy had a significant influence on the survival of patients with oral cancer in southwest China (p < 0.05).The multivariate analysis showed preoperative PLR value, FND, and tumor differentiation had significant influence on the prediction of survival (p < 0.05). However, smoking and drinking are not prognostic risk factors for oral cancer. The discriminant analysis showed 66.3% of the patients could be correctly predicted for postoperative survival, while both the C-index and decision curve analysis (DCA) showed this study may be taken as a reference for predicting the postoperative survival of patients with oral cancer. CONCLUSION Preoperative PLR, FND, and tumor differentiation are independent prognostic factors for patients with oral cancer in southwest China. The results of this study have been visualized using a nomogram and a DCA.
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Affiliation(s)
- Bo Liu
- Department of Oral and Maxillofacial Surgery, Kunming Medical University School and Hospital of Stomatology, Kunming, China
- Yunnan Key Laboratory of Stomatology, Kunming, China
| | - Ming Li
- Department of Oral and Maxillofacial Surgery, Kunming Medical University School and Hospital of Stomatology, Kunming, China
- Yunnan Key Laboratory of Stomatology, Kunming, China
| | - Siyu Chen
- Yunnan Key Laboratory of Stomatology, Kunming, China
- Department of the First Outpatient, Kunming Medical University School and Hospital of Stomatology, Kunming, China
| | - Qingying Cui
- Department of Oral and Maxillofacial Surgery, Kunming Medical University School and Hospital of Stomatology, Kunming, China
- Yunnan Key Laboratory of Stomatology, Kunming, China
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Lin YH, Lin CT, Chang YH, Lin YY, Chen JJ, Huang CR, Hsu YW, You WC. Development and Validation of a 3D Resnet Model for Prediction of Lymph Node Metastasis in Head and Neck Cancer Patients. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:679-687. [PMID: 38343258 PMCID: PMC11031546 DOI: 10.1007/s10278-023-00938-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 04/20/2024]
Abstract
The accurate diagnosis and staging of lymph node metastasis (LNM) are crucial for determining the optimal treatment strategy for head and neck cancer patients. We aimed to develop a 3D Resnet model and investigate its prediction value in detecting LNM. This study enrolled 156 head and neck cancer patients and analyzed 342 lymph nodes segmented from surgical pathologic reports. The patients' clinical and pathological data related to the primary tumor site and clinical and pathology T and N stages were collected. To predict LNM, we developed a dual-pathway 3D Resnet model incorporating two Resnet models with different depths to extract features from the input data. To assess the model's performance, we compared its predictions with those of radiologists in a test dataset comprising 38 patients. The study found that the dimensions and volume of LNM + were significantly larger than those of LNM-. Specifically, the Y and Z dimensions showed the highest sensitivity of 84.6% and specificity of 72.2%, respectively, in predicting LNM + . The analysis of various variations of the proposed 3D Resnet model demonstrated that Dual-3D-Resnet models with a depth of 34 achieved the highest AUC values of 0.9294. In the validation test of 38 patients and 86 lymph nodes dataset, the 3D Resnet model outperformed both physical examination and radiologists in terms of sensitivity (80.8% compared to 50.0% and 91.7%, respectively), specificity(90.0% compared to 88.5% and 65.4%, respectively), and positive predictive value (77.8% compared to 66.7% and 55.0%, respectively) in detecting individual LNM + . These results suggest that the 3D Resnet model can be valuable for accurately identifying LNM + in head and neck cancer patients. A prospective trial is needed to evaluate further the role of the 3D Resnet model in determining LNM + in head and neck cancer patients and its impact on treatment strategies and patient outcomes.
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Affiliation(s)
- Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung City, Taiwan
| | - Chieh-Ting Lin
- College of Artificial Intelligence, National Yang-Ming Chiao Tung University, Hsinchu City, Taiwan
| | - Ya-Han Chang
- Department of Computer Science, National Yang-Ming Chiao Tung University, Hsinchu City, Taiwan
| | - Yen-Yu Lin
- Department of Computer Science, National Yang-Ming Chiao Tung University, Hsinchu City, Taiwan
| | - Jen-Jee Chen
- College of Artificial Intelligence, National Yang-Ming Chiao Tung University, Hsinchu City, Taiwan
| | - Chun-Rong Huang
- Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City, Taiwan
| | - Yu-Wei Hsu
- Cancer Prevention and Control Center, Taichung Veterans General Hospital, Taichung City, Taiwan
| | - Weir-Chiang You
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung City, Taiwan.
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11
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Ren J, Yang G, Song Y, Zhang C, Yuan Y. Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study. BMC Med Imaging 2024; 24:33. [PMID: 38317076 PMCID: PMC10845803 DOI: 10.1186/s12880-024-01210-x] [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: 10/16/2023] [Accepted: 01/22/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND To investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC). METHODS The study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing the entire tumor, a total of 750 radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted (ceT1WI) imaging. To reduce dimensionality, reproducibility analysis by two radiologists and collinearity analysis were performed. The top six features were selected from each sequence alone, as well as their combination, using the minimum-redundancy maximum-relevance algorithm. Random forest, logistic regression, and support vector machine models were used to predict TIL levels in OTSCC, and 10-fold cross-validation was employed to assess the performance of the classifiers. RESULTS Based on the features selected from each sequence alone, the ceT1WI models outperformed the T2WI models, with a maximum area under the curve (AUC) of 0.820 versus 0.754. When combining the two sequences, the optimal features consisted of one T2WI and five ceT1WI features, all of which exhibited significant differences between patients with low and high TILs (all P < 0.05). The logistic regression model constructed using these features demonstrated the best predictive performance, with an AUC of 0.846 and an accuracy of 80.9%. CONCLUSIONS ML-based T2WI and ceT1WI radiomics can serve as valuable tools for determining the level of TILs in patients with OTSCC.
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Affiliation(s)
- Jiliang Ren
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, 200010, Shanghai, China
| | - Gongxin Yang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, 200010, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd, 200126, Shanghai, China
| | - Chunye Zhang
- Department of Oral Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, 200010, Shanghai, China.
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, 200010, Shanghai, China.
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12
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Zhang W, Liu J, Jin W, Li R, Xie X, Zhao W, Xia S, Han D. Radiomics from dual-energy CT-derived iodine maps predict lymph node metastasis in head and neck squamous cell carcinoma. LA RADIOLOGIA MEDICA 2024; 129:252-267. [PMID: 38015363 DOI: 10.1007/s11547-023-01750-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023]
Abstract
OBJECTIVE To develop and validate an iodine maps-based radiomics nomogram for preoperatively predicting cervical lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS A total of 278 patients who pathologically confirmed as HNSCC were retrospectively recruited from two medical centers between June 2012 and July 2022. The training set (n = 152) and internal set (n = 67) were randomly selected from medical center A, and the patients from medical center B were enrolled as the external set (n = 69). The minority group in the training set was balanced by the adaptive synthetic sampling (ADASYN) approach. Radiomics features were extracted from dual-energy CT-derived iodine maps at arterial phase (AP) and venous phase (VP), respectively. Three radiomics signatures were constructed to predict the LNM by using a random forest algorithm. The independent clinical predictors for LNM were identified by multivariate analysis and combined with radiomics signatures to establish a radiomic-clinical nomogram. The performance of radiomic-clinical nomogram was evaluated with respect to its discrimination and clinical usefulness. RESULTS The AP-VP-incorporated radiomics model exhibited a great predictive performance for LNM prediction with an area under curve (AUC) of 0.885 (95% CI, 0.836-0.933) in ADASYN-training set and confirmed in all validation sets. The nomogram that incorporated AP-VP radiomics signatures, CT-reported LN status, and histological grades yielded AUCs of 0.920 (95% CI, 0.881-0.959) in ADASYN-training set, 0.858 (95% CI, 0.771-0.944) in internal validation, and 0.849 (95% CI, 0.752-0.946) in external validation, with good calibration in all cohorts (p > 0.05). Decision curve analyses indicated the nomogram was clinically useful. In addition, the predictive performance of clinical-radiomics nomogram was also validation in combing cohorts. Stratified analysis confirmed the stability of nomogram, particularly in group negative for CT-reported LNM. CONCLUSION Clinical-radiomics nomogram based on iodine maps exhibited promising performance in predicting LNM and providing valuable information for making individualized therapy decisions.
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Affiliation(s)
- Weiyuan Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Jin Liu
- Center of PET/CT, 920th Hospital of Joint Logistics Support Force, PLA, Kunming, 650032, China
| | - Wenfeng Jin
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Ruihong Li
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Xiaojie Xie
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Wen Zhao
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Shuang Xia
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, Tianjin, 300192, China
| | - Dan Han
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China.
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Wang W, Liang H, Zhang Z, Xu C, Wei D, Li W, Qian Y, Zhang L, Liu J, Lei D. Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study. EClinicalMedicine 2024; 67:102385. [PMID: 38261897 PMCID: PMC10796944 DOI: 10.1016/j.eclinm.2023.102385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/29/2023] [Accepted: 12/04/2023] [Indexed: 01/25/2024] Open
Abstract
Background The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC. Methods In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n = 300), an internal test set (n = 89), and two external test sets (n = 120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis. Findings The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89-0.90) among all the study sets, significantly outperforming the clinical model (AUC = 0.73-0.78, P = 0.0001-0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82-0.84 (P = 0.025-0.46), 0.86-0.89 (P = 0.75-0.97), 0.83-0.86 (P = 0.029-0.66), and 0.79-0.82 (P = 0.0072-0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82-88%) and specificity (79-85%) in the test sets. Interpretation The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease. Funding National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.
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Affiliation(s)
- Wenlun Wang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Hui Liang
- Department of Otorhinolaryngology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Ji’nan 250014, Shandong, China
| | - Zhouyi Zhang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Chenyang Xu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Dongmin Wei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Wenming Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Ye Qian
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Lihong Zhang
- Department of Otorhinolaryngology Head & Neck Surgery, Peking University People’s Hospital, Beijing 100044, China
| | - Jun Liu
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Dapeng Lei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
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van Staalduinen EK, Matthews R, Khan A, Punn I, Cattell RF, Li H, Franceschi A, Samara GJ, Czerwonka L, Bangiyev L, Duong TQ. Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone. Diagnostics (Basel) 2023; 14:71. [PMID: 38201380 PMCID: PMC10802850 DOI: 10.3390/diagnostics14010071] [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: 11/28/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.
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Affiliation(s)
- Eric K. van Staalduinen
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Robert Matthews
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Adam Khan
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Isha Punn
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Renee F. Cattell
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Haifang Li
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Ana Franceschi
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Ghassan J. Samara
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Lukasz Czerwonka
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Lev Bangiyev
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Tim Q. Duong
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA
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Vidiri A, Marzi S, Piludu F, Lucchese S, Dolcetti V, Polito E, Mazzola F, Marchesi P, Merenda E, Sperduti I, Pellini R, Covello R. Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma. Comput Struct Biotechnol J 2023; 21:4277-4287. [PMID: 37701020 PMCID: PMC10493896 DOI: 10.1016/j.csbj.2023.08.020] [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: 06/13/2023] [Revised: 08/10/2023] [Accepted: 08/22/2023] [Indexed: 09/14/2023] Open
Abstract
Purpose To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC). Materials and methods 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared. Results MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy. Conclusion MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.
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Affiliation(s)
- Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
| | - Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 0 0144 Rome, Italy
| | - Francesca Piludu
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
| | - Sonia Lucchese
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
- Scuola di Specializzazione in Radiodiagnostica, Sapienza University of Rome, Policlinico Umberto I, Viale Regina Elena 324, 00161 Rome, Italy
| | - Vincenzo Dolcetti
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
- Scuola di Specializzazione in Radiodiagnostica, Sapienza University of Rome, Policlinico Umberto I, Viale Regina Elena 324, 00161 Rome, Italy
| | - Eleonora Polito
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
| | - Francesco Mazzola
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Paolo Marchesi
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Elisabetta Merenda
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy
| | - Isabella Sperduti
- Biostatistics Unit, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Raul Pellini
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Renato Covello
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
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Kawashima Y, Miyakoshi M, Kawabata Y, Indo H. Efficacy of texture analysis of ultrasonographic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol 2023:S2212-4403(23)00439-X. [PMID: 37353468 DOI: 10.1016/j.oooo.2023.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/13/2023] [Accepted: 04/23/2023] [Indexed: 06/25/2023]
Abstract
OBJECTIVE We investigated the efficacy of using texture analysis of ultrasonographic images of the cervical lymph nodes of patients with squamous cell carcinoma of the tongue to differentiate between metastatic and non-metastatic lymph nodes. STUDY DESIGN We analyzed 32 metastatic and 28 non-metastatic lymph nodes diagnosed by histopathologic examination on presurgical US images. Using the LIFEx texture analysis program, we extracted 36 texture features from the images and calculated the statistical significance of differences in texture features between metastatic and non-metastatic lymph nodes using the t test. To assess the diagnostic ability of the significantly different texture features to discriminate between metastatic and non-metastatic nodes, we performed receiver operating characteristic curve analysis and calculated the area under the curve. We set the cutoff points that maximized the sensitivity and specificity for each curve according to the Youden J statistic. RESULTS We found that 20 texture features significantly differed between metastatic and non-metastatic lymph nodes. Among them, only the gray-level run length matrix feature of run length non-uniformity and the gray-level zone length matrix features of gray-level non-uniformity and zone length non-uniformity showed an excellent ability to discriminate between metastatic and non-metastatic lymph nodes as indicated by the area under the curve and the sum of sensitivity and specificity. CONCLUSIONS Analysis of the texture features of run length non-uniformity, gray-level non-uniformity, and zone length non-uniformity values allows for differentiation between metastatic and non-metastatic lymph nodes, with the use of gray-level non-uniformity appearing to be the best means of predicting metastatic lymph nodes.
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Affiliation(s)
- Yusuke Kawashima
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan.
| | - Masaaki Miyakoshi
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
| | - Yoshihiro Kawabata
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
| | - Hiroko Indo
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
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Xu F, Peng L, Feng J, Zhu X, Pan Y, Hu Y, Gao X, Ma Y, He Y. A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables. Cancer Imaging 2023; 23:34. [PMID: 37016465 PMCID: PMC10074690 DOI: 10.1186/s40644-023-00552-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/27/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND The efficacy of 18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography(PET/CT) in evaluating the neck status in clinically node-negative (cN0) oral squamous cell carcinoma(OSCC) patients was still unsatisfying. We tried to develop a prediction model for nodal metastasis in cN0 OSCC patients by using metabolic and pathological variables. METHODS Consecutive cN0 OSCC patients with preoperative 18F-FDG PET/CT, subsequent surgical resection of primary tumor and neck dissection were included. Ninety-five patients who underwent PET/CT scanning in Shanghai ninth people's hospital were identified as training cohort, and another 46 patients who imaged in Shanghai Universal Medical Imaging Diagnostic Center were selected as validation cohort. Nodal-status-related variables in the training cohort were selected by multivariable regression after using the least absolute shrinkage and selection operator (LASSO). A nomogram was constructed with significant variables for the risk prediction of nodal metastasis. Finally, nomogram performance was determined by its discrimination, calibration, and clinical usefulness. RESULTS Nodal maximum standardized uptake value(nodal SUVmax) and pathological T stage were selected as significant variables. A prediction model incorporating the two variables was used to plot a nomogram. The area under the curve was 0.871(Standard Error [SE], 0.035; 95% Confidence Interval [CI], 0.787-0.931) in the training cohort, and 0.809(SE, 0.069; 95% CI, 0.666-0.910) in the validation cohort, with good calibration demonstrated. CONCLUSIONS A prediction model incorporates metabolic and pathological variables has good performance for predicting nodal metastasis in cN0 OSCC patients. However, further studies with large populations are needed to verify our findings.
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Affiliation(s)
- Feng Xu
- Department of Nuclear Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liling Peng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Junyi Feng
- Department of Nuclear Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochun Zhu
- Department of Nuclear Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifan Pan
- Department of Nuclear Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuhua Hu
- Department of Oral Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Yubo Ma
- Department of Nuclear Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yue He
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Muraoka H, Kaneda T, Hirahara N, Ito K, Okada S, Kondo T. Efficacy of magnetic resonance imaging texture features of the lateral pterygoid muscle in distinguishing rheumatoid arthritis and osteoarthritis of the temporomandibular joint. Dentomaxillofac Radiol 2023; 52:20220321. [PMID: 36594821 PMCID: PMC9944011 DOI: 10.1259/dmfr.20220321] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES The aim of this study was to assess whether magnetic resonance imaging (MRI) texture features of the lateral pterygoid muscle can distinguish between rheumatoid arthritis (RA) and osteoarthritis (OA) of the temporomandibular joint (TMJ). METHODS The authors extracted 279 texture features from 36 patients with RA and OA from the region of interest set for the lateral pterygoid muscle on short tau inversion recovery (STIR) images using MaZda Ver.3.3. A total of 10 texture features were selected using Fisher's coefficients, as well as probability of error and average correlation coefficients. Data observed to have a non-normal distribution using the Kolmogorov-Smirnov test were compared using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves were used to assess the ability of the 10 texture features to distinguish RA and OA of the TMJ. RESULTS A total of 10 features (5 Correlation, 3 Run Length Nonuniformity, 1 Sigma, and 1 Teta) were selected from 279 texture features. These texture features revealed significant differences between the RA and OA groups (p < 0.01). The sensitivity, specificity, accuracy, and area under the ROC curve of the texture features for distinguishing RA from OA were 0.78-0.94, 0.89-1.0, 0.86-0.92, and 0.89-0.95, respectively. CONCLUSION MRI texture analysis of the lateral pterygoid muscle may be useful for distinguishing between RA and OA of the TMJ.
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Affiliation(s)
- Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Shunya Okada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Takumi Kondo
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
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Wang Y, Chen Y, Jing Y, Qiao F, Yin B, Liu J, Zhou J, Chen M, Wu L. Prediction of accessory canals on the apical third of mandibular second molar based on micro-computed tomography. Dentomaxillofac Radiol 2023; 52:20220057. [PMID: 36631421 PMCID: PMC9974238 DOI: 10.1259/dmfr.20220057] [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: 02/08/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE The aim of this study was to investigate the anatomical factors influencing the incidence of accessory canals (ACs) in the apical third of the mandibular second molar in Chinese population. METHODS Micro-CT was performed on 86 root canals. The five possible factors influencing the incidence of ACs in the apical third were named X1 to X5. These factors were the canal length of the apical third, fused roots, location of apical foramen, curvature of the root canals, and complexity of the canals. Statistical analysis was performed by the least absolute shrinkage and selection operator, receiver operating characteristic curve, and the χ2 test (α = 0.05). RESULTS The selected variables in the least absolute shrinkage and selection operator regression model were fused roots and complex root canals. The area under the curve was 0.737, indicating that the model had a certain predictive ability. ACs were mainly distributed in the buccal wall and mesial wall of root canals in the apical third of molars (p < 0.05). CONCLUSIONS For Chinese population, fused roots and complex root canals are anatomical factors influencing ACs in the apical one-third of mandibular second molars, and the ACs mainly occur in the buccal wall and mesial wall of the root canal.
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Affiliation(s)
- Yinghui Wang
- Department of Endodontics, School of Stomatology, Tianjin Medical University, Tianjin, China
| | - Yufan Chen
- Department of Stomatology, the First hospital of Hebei Medical University, Hebei, China
| | - Yao Jing
- Department of Endodontics, Affiliated Stomatological Hospital of Xuzhou Medical University, Xuzhou, China
| | - Feng Qiao
- Oral and Maxillofacial Surgery, School of Stomatology, Tianjin Medical University, Tianjin, China
| | - Bin Yin
- Department of Stomatology, Community Health Service Center, Meijiang Street, Hexi District, Tianjin, China
| | - Juan Liu
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jie Zhou
- Department of Stomatology, Wuqing People Hospital, Tianjin, China
| | - Min Chen
- Department of Endodontics, School of Stomatology, Tianjin Medical University, Tianjin, China
| | - Ligeng Wu
- Department of Endodontics, School of Stomatology, Tianjin Medical University, Tianjin, China
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Xu X, Xi L, Wei L, Wu L, Xu Y, Liu B, Li B, Liu K, Hou G, Lin H, Shao Z, Su K, Shang Z. Deep learning assisted contrast-enhanced CT-based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases. Eur Radiol 2022; 33:4303-4312. [PMID: 36576543 PMCID: PMC9795159 DOI: 10.1007/s00330-022-09355-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/23/2022] [Accepted: 11/29/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVES Lymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative LNs is not ideal. Here, we aimed to develop a deep learning model that can identify, locate, and distinguish LNs in contrast-enhanced CT (CECT) images with a higher accuracy. METHODS The preoperative CECT images and corresponding postoperative pathological diagnoses of 1466 patients with oral cancer from our hospital were retrospectively collected. In stage I, full-layer images (five common anatomical structures) were labeled; in stage II, negative and positive LNs were separately labeled. The stage I model was innovatively employed for stage II training to improve accuracy with the idea of transfer learning (TL). The Mask R-CNN instance segmentation framework was selected for model construction and training. The accuracy of the model was compared with that of human observers. RESULTS A total of 5412 images and 5601 images were labeled in stage I and II, respectively. The stage I model achieved an excellent segmentation effect in the test set (AP50-0.7249). The positive LN accuracy of the stage II TL model was similar to that of the radiologist and much higher than that of the surgeons and students (0.7042 vs. 0.7647 (p = 0.243), 0.4216 (p < 0.001), and 0.3629 (p < 0.001)). The clinical accuracy of the model was highest (0.8509 vs. 0.8000, 0.5500, 0.4500, and 0.6658 of the Radiology Department). CONCLUSIONS The model was constructed using a deep neural network and had high accuracy in LN localization and metastasis discrimination, which could contribute to accurate diagnosis and customized treatment planning. KEY POINTS • Lymph node metastasis is not well recognized with modern medical imaging tools. • Transfer learning can improve the accuracy of deep learning model prediction. • Deep learning can aid the accurate identification of lymph node metastasis.
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Affiliation(s)
- Xiaoshuai Xu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Linlin Xi
- School of Computer Science, Wuhan University, 299 Bayi Road, Wuhan, 430072, Hubei, China
| | - Lili Wei
- Department of Radiology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Luping Wu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Yuming Xu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Bailve Liu
- School of Computer Science, Wuhan University, 299 Bayi Road, Wuhan, 430072, Hubei, China
| | - Bo Li
- Department of Radiology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Ke Liu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral and Maxillofacial Head Neck Surgery, School & Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, 430079, Hubei, China
| | - Gaigai Hou
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Hao Lin
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zhe Shao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
- Department of Oral and Maxillofacial Head Neck Surgery, School & Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, 430079, Hubei, China.
| | - Kehua Su
- School of Computer Science, Wuhan University, 299 Bayi Road, Wuhan, 430072, Hubei, China.
| | - Zhengjun Shang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
- Department of Oral and Maxillofacial Head Neck Surgery, School & Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, 430079, Hubei, China.
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Wang F, Zhang W, Chai Y, Wang H, Liu Z, He Y. Constrast-enhanced computed tomography radiomics predicts CD27 expression and clinical prognosis in head and neck squamous cell carcinoma. Front Immunol 2022; 13:1015436. [PMID: 36458007 PMCID: PMC9705340 DOI: 10.3389/fimmu.2022.1015436] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/28/2022] [Indexed: 09/08/2023] Open
Abstract
OBJECTIVE This study aimed to construct a radiomics model that predicts the expression level of CD27 in patients with head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS Genomic data and contrast-enhanced computed tomography (CT) images of patients with HNSCC were downloaded from the Cancer Genome Atlas and Cancer Imaging Archive for prognosis analysis, image feature extraction, and model construction. We explored the potential molecular mechanisms underlying CD27 expression and its relationship with the immune microenvironment and predicted CD27 mRNA expression in HNSCC tissues. Using non-invasive, CT-based radiomics technology, we generated a radiomics model and evaluated its correlation with the related genes and HNSCC prognosis. RESULTS AND CONCLUSION The expression level of CD27 in HNSCC may significantly influence the prognosis of patients with HNSCC. Radiomics based on contrast-enhanced CT is potentially effective in predicting the expression level of CD27.
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Affiliation(s)
- Fang Wang
- Department of Oral Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China
| | - Wenhao Zhang
- Department of Oral Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China
| | - Ying Chai
- Department of Oral Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China
| | - Hanshao Wang
- Department of Oral Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China
| | - Zhonglong Liu
- Department of Oromaxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China
| | - Yue He
- Department of Oromaxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China
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Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9646846. [PMID: 36267845 PMCID: PMC9578811 DOI: 10.1155/2022/9646846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022]
Abstract
Purpose. We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian cancer patients. We use the feature map formed by wavelet transform and the parameters obtained by image segmentation to build the model. The model is expected to help clinicians and provide additional information about what to do with first-visit patients. Materials and Methods. Our study included 224 patients with ovarian cancer. We have chosen two main methods to extract information from images. On the one hand, we segmented the image to extract the parameters to evaluate the clustering effect. On the other hand, we used wavelet transform to extract the image’s texture information to form the image’s feature map. Based on the above two kinds of information, we used residual neural network and support vector machine for modeling. Results. We established a model to predict lymph node metastasis in patients with primary ovarian cancer using PET images. On the training set, our accuracy was 0.8854, AUC: 0.9472, CI: 0.9098-0.9752, sensitivity was 0.9865, and specificity was 0.7952. On the test set, our accuracy was 0.9104, AUC: 0.9259, CI: 0.8417-0.9889, sensitivity was 0.8125, and specificity was 1.0000. Conclusions. We used wavelet transform to process the preoperative medical images of ovarian cancer patients, and the residual neural network can effectively predict the lymph node metastasis of ovarian cancer patients, which is undoubted of great significance for patients’ staging and treatment options.
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Kubo K, Kawahara D, Murakami Y, Takeuchi Y, Katsuta T, Imano N, Nishibuchi I, Saito A, Konishi M, Kakimoto N, Yoshioka Y, Toratani S, Ono S, Ueda T, Takeno S, Nagata Y. Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:93-101. [PMID: 35431177 DOI: 10.1016/j.oooo.2021.12.122] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/10/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography. STUDY DESIGN This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE). RESULTS For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98. CONCLUSIONS Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.
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Affiliation(s)
- Katsumaro Kubo
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
| | - Yuki Takeuchi
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsuyoshi Katsuta
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuki Imano
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ikuno Nishibuchi
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Akito Saito
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Masaru Konishi
- Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yukio Yoshioka
- Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shigeaki Toratani
- Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shigehiro Ono
- Department of Oral and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsutomu Ueda
- Department of Otolaryngology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Department of Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Sachio Takeno
- Department of Otolaryngology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Department of Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters. JOURNAL OF ONCOLOGY 2022; 2022:3335048. [PMID: 35813867 PMCID: PMC9262528 DOI: 10.1155/2022/3335048] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/08/2022] [Indexed: 01/17/2023]
Abstract
Objective To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.
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Yang X, Hu H, Zhang F, Li D, Yang Z, Shi G, Lu G, Jiang Y, Yang L, Wang Y, Duan X, Shen J. Preoperative Prediction of the Aggressiveness of Oral Tongue Squamous Cell Carcinoma with Quantitative Parameters from Dual-Energy Computed Tomography. Front Oncol 2022; 12:904471. [PMID: 35814448 PMCID: PMC9260668 DOI: 10.3389/fonc.2022.904471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 05/19/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To determine whether quantitative parameters derived from dual-energy computed tomography (DECT) were predictive of the aggressiveness of oral tongue squamous cell carcinoma (OTSCC) including the pathologic stages, histologic differentiation, lymph node status, and perineural invasion (PNI). Methods Between August 2019 and March 2021, 93 patients (mean age, 54.6 ± 13.8 years; 66 men) with pathologically diagnosed OTSCC were enrolled in this prospective study. Preoperative DECT was performed and quantitative parameters (e.g., slope of the spectral Hounsfield unit curve [λHu], normalized iodine concentration [nIC], normalized effective atomic number [nZeff], and normalized electron density [nRho]) were measured on arterial phase (AP) and venous phase (VP) DECT imaging. Quantitative parameters from DECT were compared between patients with different pathologic stages, histologic differentiation, lymph node statuses, and perineural invasion statuses. Logistic regression analysis was utilized to assess independent parameters and the diagnostic performance was analyzed by the receiver operating characteristic curves (ROC). Results λHu and nIC in AP and λHu, nZeff, and nIC in VP were significantly lower in stage III–IV lesions than in stage I–II lesions (p < 0.001 to 0.024). λHu in VP was an independent predictor of tumor stage with an odds ratio (OR) of 0.29, and area under the curve (AUC) of 0.80. λHu and nIC were higher in well-differentiated lesions than in poorly differentiated lesions (p < 0.001 to 0.021). The nIC in VP was an independent predictor of histologic differentiation with OR of 0.31, and AUC of 0.78. λHu and nIC in VP were lower in OTSCCs with lymph node metastasis than those without metastasis (p < 0.001 to 0.005). λHu in VP was the independent predictor of lymph node status with OR of 0.42, and AUC of 0.74. No significant difference was found between OTSCCs without PNI and those with PNI in terms of the quantitative DECT parameters. Conclusion DECT can be a complementary means for the preoperative prediction of the aggressiveness of OTSCC.
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Affiliation(s)
- Xieqing Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Huijun Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Fang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Dongye Li
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Guangzi Shi
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Guoxiong Lu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yusong Jiang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Lingjie Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yu Wang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- *Correspondence: Xiaohui Duan, ; Jun Shen,
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- *Correspondence: Xiaohui Duan, ; Jun Shen,
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Models of Head and Neck Squamous Cell Carcinoma Using Bioengineering Approaches. Crit Rev Oncol Hematol 2022; 175:103724. [DOI: 10.1016/j.critrevonc.2022.103724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/24/2022] [Accepted: 05/18/2022] [Indexed: 11/21/2022] Open
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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Wang F, Tan R, Feng K, Hu J, Zhuang Z, Wang C, Hou J, Liu X. Magnetic Resonance Imaging-Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer. J Magn Reson Imaging 2021; 56:196-209. [PMID: 34888985 PMCID: PMC9299921 DOI: 10.1002/jmri.28019] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 12/18/2022] Open
Abstract
Background Adequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear. Purpose To investigate whether magnetic resonance imaging (MRI)‐based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer. Study type Retrospective. Population Two hundred and thirty‐six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each). Field Strength/Sequence 1.5 T; T2‐weighted turbo spin‐echo images. Assessment Radiomics models (Rprim, Rprim+3, Rprim+5, Rprim+10, Rprim+15) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI‐reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim, CRprim+3, CRprim+5, CRprim+10, CRprim+15). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion. Statistical Tests Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant. Results Of all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, −0.157 to −0.336). The CRprim+10 was an independent indicator for poor disease‐free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set. Data Conclusion Radiomics analysis with a 10‐mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.
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Affiliation(s)
- Fei Wang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Rukeng Tan
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Kun Feng
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Jing Hu
- Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zehang Zhuang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Cheng Wang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Jinsong Hou
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Xiqiang Liu
- Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol 2021; 32:2739-2747. [PMID: 34642806 DOI: 10.1007/s00330-021-08310-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 08/24/2021] [Accepted: 08/30/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES To investigate the feasibility of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI for predicting occult lymph node metastasis (LNM) in early-stage oral tongue squamous cell cancer (OTSCC). MATERIALS AND METHODS This retrospective study included 55 early-stage OTSCC (cT1-2N0M0) patients; 34 with pathological LNM and 21 without. Eight whole-tumor histogram features were extracted from quantitative apparent diffusion coefficient (ADC) maps and two semi-quantitative DCE parametric maps (wash-in and wash-out). The clinicopathological factors and histogram features were compared between the two groups. Stepwise logistic regression was used to identify independent predictors. Receiver operating characteristic curves were generated to assess the performances of significant variables and a combined model for predicting occult LNM. RESULTS MRI-determined depth of invasion and ADCentropy was significantly higher in the LNM group, with respective areas under the curve (AUCs) of 0.67 and 0.69, and accuracies of 0.73 and 0.73. ADC10th. ADCuniformity and wash-inskewness were significantly lower in the LNM group, with respective AUCs of 0.68, 0.71, and 0.69, and accuracies of 0.65, 0.71, and 0.64. Histogram features from wash-out maps were not significantly associated with cervical node status. In the logistic regression analysis, ADC10th, ADCuniformity, and wash-inskewness were independent predictors. The combined model yielded the best predictive performance, with an AUC of 0.87 and an accuracy of 0.82. CONCLUSIONS Whole-tumor histogram analysis of ADC and wash-in maps is a feasible tool for preoperative evaluation of cervical node status in early-stage OTSCC. KEY POINTS • Histogram analysis of parametric maps from DWI and DCE-MRI may assist the prediction of occult LNM in early-stage OTSCC. • ADC10th, ADCuniformity, and wash-inskewness were independent predictors. • The combined model exhibited good predictive performance, with an accuracy of 0.82.
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