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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024; 57:719-751. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [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] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Tagliabue M, Ruju F, Mossinelli C, Gaeta A, Raimondi S, Volpe S, Zaffaroni M, Isaksson LJ, Garibaldi C, Cremonesi M, Rapino A, Chiocca S, Pietrobon G, Alterio D, Trisolini G, Morbini P, Rampinelli V, Grammatica A, Petralia G, Jereczek-Fossa BA, Preda L, Ravanelli M, Maroldi R, Piazza C, Benazzo M, Ansarin M. The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study. LA RADIOLOGIA MEDICA 2024; 129:1369-1381. [PMID: 39096355 PMCID: PMC11379741 DOI: 10.1007/s11547-024-01859-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 07/17/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). MATERIALS AND METHODS Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. RESULTS We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. CONCLUSION Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
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Affiliation(s)
- Marta Tagliabue
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Ruju
- Division of Radiology, European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Mossinelli
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy.
| | - Aurora Gaeta
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca Degli Arcimboldi, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Cristina Garibaldi
- Unit of Radiation Research, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Marta Cremonesi
- Unit of Radiation Research, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Anna Rapino
- Postgraduate School of Radiodiagnostic, University of Milan, Milan, Italy
| | - Susanna Chiocca
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Giacomo Pietrobon
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giuseppe Trisolini
- Department of Otorhinolaryngology and Skull Base Microsurgery-Neurosciences, ASST Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | | | - Vittorio Rampinelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Alberto Grammatica
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Giuseppe Petralia
- Division of Radiology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lorenzo Preda
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Cesare Piazza
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Marco Benazzo
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
- Department of Otorhinolaryngology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Mohssen Ansarin
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
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Yao Y, Jin X, Peng T, Song P, Ye Y, Song L, Li H, An P. A novel nomogram for predicting overall survival in patients with tongue squamous cell carcinoma using clinical features and MRI radiomics data: a pilot study. World J Surg Oncol 2024; 22:227. [PMID: 39198807 PMCID: PMC11360835 DOI: 10.1186/s12957-024-03508-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 08/25/2024] [Indexed: 09/01/2024] Open
Abstract
OBJECTIVE Tongue squamous cell carcinoma (TSCC) accounts for 43.4% of oral cancers in China and has a poor prognosis. This study aimed to explore whether radiomics features extracted from preoperative magnetic resonance imaging (MRI) could predict overall survival (OS) in patients with TSCC. METHODS The clinical imaging data of 232 patients with pathologically confirmed TSCC at Xiangyang No. 1 People's Hospital were retrospectively analyzed from February 2010 to October 2022. Based on 2-10 years of follow-up, patients were categorized into two groups: control (healthy survival, n = 148) and research (adverse events: recurrence or metastasis-related death, n = 84). A training and a test set were established using a 7:3 ratio and a time node. Radiomics features were extracted from axial T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging (DWI) sequences. The corresponding radiomics scores were generated using the least absolute shrinkage and selection operator algorithm. Kaplan-Meier and multivariate Cox regression analyses were used to screen for independent factors affecting adverse events in patients with TSCC using clinical and pathological results. A novel nomogram was established to predict the probability of adverse events and OS in patients with TSCC. RESULTS The incidence of adverse events within 2-10 years after surgery was 36.21%. Kaplan-Meier analysis revealed that hot pot consumption, betel nut chewing, platelet-lymphocyte ratio, drug use, neutrophil-lymphocyte ratio, Radscore, and other factors impacted TSCC survival. Multivariate Cox regression analysis revealed that the clinical stage (P < 0.001), hot pot consumption (P < 0.001), Radscore 1 (P = 0.01), and Radscore 2 (P < 0.001) were independent factors affecting TSCC-OS. The same result was validated by the XGBoost algorithm. The nomogram based on the aforementioned factors exhibited good discrimination (C-index 0.86/0.81) and calibration (P > 0.05) in the training and test sets, accurately predicting the risk of adverse events and survival. CONCLUSION The nomogram constructed using clinical data and MRI radiomics parameters may accurately predict TSCC-OS noninvasively, thereby assisting clinicians in promptly modifying treatment strategies to improve patient prognosis.
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Affiliation(s)
- Yongling Yao
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Xin Jin
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Tianfang Peng
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China
| | - Ping Song
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Yingjian Ye
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Lina Song
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China
| | - Huantian Li
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China.
| | - Peng An
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China.
- Department of epidemiology, Xiangyang Key Laboratory of Maternal-fetal Medicine on Fetal Congenital Heart Disease, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Hubei, P.R.C, Xiangyang, 441000, Hubei Province, China.
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Nikkuni Y, Nishiyama H, Hayashi T. Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied to 18F-FDG-PET Radiomics. Biomedicines 2024; 12:1411. [PMID: 39061984 PMCID: PMC11273837 DOI: 10.3390/biomedicines12071411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/07/2024] [Accepted: 06/13/2024] [Indexed: 07/28/2024] Open
Abstract
The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from 18F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an 18F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma.
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Affiliation(s)
- Yutaka Nikkuni
- Division of Oral and Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8510, Japan; (H.N.); (T.H.)
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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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Yumii K, Ueda T, Kawahara D, Chikuie N, Taruya T, Hamamoto T, Takeno S. Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer. Auris Nasus Larynx 2024; 51:417-424. [PMID: 37838567 DOI: 10.1016/j.anl.2023.09.001] [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: 07/05/2023] [Revised: 08/27/2023] [Accepted: 09/08/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVE Transoral surgery (TOS) is a widely used treatment for laryngopharyngeal cancer. There are some difficult cases of setting the extent of resection in TOS, particularly in setting the vertical margins. However, positive vertical margins require additional treatment. Further, excessive resection should be avoided as it increases the risk of bleeding as a postoperative complication and may lead to decreased quality of life, such as dysphagia. Considering these issues, determining the extent of resection in TOS is an important consideration. In this study, we investigated the possibility of accurately diagnosing the depth of laryngopharyngeal cancer using radiomics, an image analysis method based on artificial intelligence (AI). METHODS We included esophagogastroduodenoscopic images of 95 lesions that were pathologically diagnosed as squamous cell carcinoma (SCC) and treated with transoral surgery at our institution between August 2009 and April 2020. Of the 95 lesions, 54 were SCC in situ, and 41 were SCC. Radiomics analysis was performed on 95 upper gastrointestinal endoscopic NBI images of these lesions to evaluate their diagnostic performance for the presence of subepithelial invasion. The lesions in the endoscopic images were manually delineated, and the accuracy, sensitivity, specificity, and area under the curve (AUC) were evaluated from the features obtained using least absolute shrinkage and selection operator analysis. In addition, the results were compared with the depth predictions made by skilled endoscopists. RESULTS In the Radiomics study, the average cross-validation was 0.833. The mean AUC for cross-validation calculated from the receiver operating characteristic curve was 0.868. These results were equivalent to those of the diagnosis made by a skilled endoscopist. CONCLUSION The diagnosis of laryngopharyngeal cancer depth using radiomics analysis has potential clinical applications. We plan to use it in actual surgery in the future and prospectively study whether it can be used for diagnosis.
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Affiliation(s)
- Kohei Yumii
- Department of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi Minami-ku, Hiroshima, Japan
| | - Tsutomu Ueda
- Department of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi Minami-ku, Hiroshima, Japan.
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuyuki Chikuie
- Department of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi Minami-ku, Hiroshima, Japan
| | - Takayuki Taruya
- Department of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi Minami-ku, Hiroshima, Japan
| | - Takao Hamamoto
- Department of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi Minami-ku, Hiroshima, Japan
| | - Sachio Takeno
- Department of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi Minami-ku, Hiroshima, Japan
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Liu S, Zhang A, Xiong J, Su X, Zhou Y, Li Y, Zhang Z, Li Z, Liu F. The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients. Head Neck 2024; 46:513-527. [PMID: 38108536 DOI: 10.1002/hed.27605] [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: 08/21/2023] [Revised: 11/15/2023] [Accepted: 12/06/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND The purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients. METHODS A total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1-weighted images (T1WI) and FS-T2WI (fat-suppressed T2-weighted images), group II consisted of patients with T1WI, FS-T2WI, and contrast enhanced MRI (CE-MRI), group III consisted of patients with T1WI, FS-T2WI, and T2-weighted images (T2WI), group IV consisted of patients with T1WI, FS-T2WI, CE-MRI, and T2WI, group V consisted of patients with T1WI, FS-T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS-T2WI, CE-MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group. RESULTS The machine learning model in group IV including T1WI, FS-T2WI, T2WI, and CE-MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE-MRI performed better than the models without CE-MRI(I vs. II, III vs. IV, V vs. VI). CONCLUSIONS The radiomics machine learning models based on CE-MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients.
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Affiliation(s)
- Sheng Liu
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Aihua Zhang
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Jianjun Xiong
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Xingzhou Su
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Yuhang Zhou
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Yang Li
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Zheng Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhenning Li
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Fayu Liu
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
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9
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Konishi M, Kakimoto N. Radiomics analysis of intraoral ultrasound images for prediction of late cervical lymph node metastasis in patients with tongue cancer. Head Neck 2023; 45:2619-2626. [PMID: 37584449 DOI: 10.1002/hed.27487] [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: 04/15/2023] [Revised: 07/25/2023] [Accepted: 08/07/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND We investigated the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer. METHODS We selected 120 patients with tongue cancer who underwent intraoral ultrasonography, 30 of which had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Bootstrap forest (BF), support vector machine (SVM), and neural tanh boost (NTB) were used as the machine learning models, and receiver operating characteristic curve analysis was conducted to determine diagnostic performance. RESULTS The sensitivity, specificity, accuracy, and AUC in the validation group were, respectively, 0.600, 0.967, 0.875, and 0.923 for the BF model; 0.700, 0.967, 0.900, and 0.950 for the SVM model; and 0.900, 0.967, 0.950, and 0.967 for NTB model. CONCLUSIONS Radiomics analysis and machine learning models using ultrasonographic images of pretreated tongue cancer could predict late cervical lymph node metastasis with high accuracy.
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Affiliation(s)
- 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
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10
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Jiang S, Locatello LG, Maggiore G, Gallo O. Radiomics-Based Analysis in the Prediction of Occult Lymph Node Metastases in Patients with Oral Cancer: A Systematic Review. J Clin Med 2023; 12:4958. [PMID: 37568363 PMCID: PMC10419487 DOI: 10.3390/jcm12154958] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Tumor extension and metastatic cervical lymph nodes' (LNs) number and dimensions are major prognostic factors in patients with oral squamous cell carcinoma (OSCC). Radiomics-based models are being integrated into clinical practice in the prediction of LN status prior to surgery in order to optimize the treatment, yet their value is still debated. METHODS A systematic review of the literature was conducted according to the PRISMA guideline. Baseline study characteristics, and methodological items were extracted and summarized. RESULTS A total of 10 retrospective studies were included into the present study, each of them exploiting a single imaging modality. Data from a cohort of 1489 patients were analyzed: the highest AUC value was 99.5%, ACC ranges from 68% to 97.5%, and sensibility and specificity were over 0.65 and 0.70, respectively. CONCLUSION Radiomics may be a noninvasive tool to predict occult LN metastases (LNM) in OSCC patients prior to treatment; further prospective studies are warranted to create a reproducible and reliable method for the detection of LNM in OSCC.
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Affiliation(s)
- Serena Jiang
- Department of Otorhinolaryngology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Luca Giovanni Locatello
- Department of Otorhinolaryngology, University Hospital “Santa Maria Della Misericordia”, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), 33100 Udine, Italy;
| | - Giandomenico Maggiore
- Department of Otorhinolaryngology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Oreste Gallo
- Department of Otorhinolaryngology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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11
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Liu J, Song L, Zhou J, Yu M, Hu Y, Zhang J, Song P, Ye Y, Wang J, Feng G, Guo H, An P. Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling. Technol Cancer Res Treat 2023; 22:15330338231207006. [PMID: 37872687 PMCID: PMC10594972 DOI: 10.1177/15330338231207006] [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: 07/03/2023] [Revised: 09/04/2023] [Accepted: 09/19/2023] [Indexed: 10/25/2023] Open
Abstract
Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis (P < .05). In the test set, the combined model based on these factors had the highest predictive performance for TSCC prognosis (area under curve (AUC) AUC: 0.870, 95% CI [0.761-0.942]), which was significantly higher than the clinical model (AUC: 0.730, 95% CI [0.602-0.835], P = .033) and imaging model (AUC: 0.765, 95% CI [0.640-0.863], P = .074). The decision curve also confirmed the higher clinical net benefit of the combined model, and these results were validated in the test set. The nomogram developed based on the combined model received good evaluation in clinical application. Conclusion: MR-LASSO extracted texture parameters can help improve the performance of TSCC prognosis models. The combined model and nomogram provide support for postoperative clinical treatment management of TSCC.
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Affiliation(s)
- Junjie Liu
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Oncology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Lina Song
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Oncology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jingran Zhou
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Mengxing Yu
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yan Hu
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyan Zhang
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ping Song
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yingjian Ye
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Gynaecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jinsong Wang
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Gynaecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Guoyan Feng
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Hubei Provincial Clinical Research Center for Accurate Fetus Malformation Diagnosis, Hubei University of Medicine, Xiangyang, Hubei Province, China
| | - Hongyan Guo
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Hubei Provincial Clinical Research Center for Accurate Fetus Malformation Diagnosis, Hubei University of Medicine, Xiangyang, Hubei Province, China
| | - Peng An
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Gynaecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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