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Xu Z, Dai Y, Liu F, Li S, Liu S, Shi L, Fu J. Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI. Ann Biomed Eng 2024:10.1007/s10439-024-03510-3. [PMID: 38691234 DOI: 10.1007/s10439-024-03510-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/03/2024] [Indexed: 05/03/2024]
Abstract
Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance images is essential in accurately diagnosing and selecting appropriate surgical plans. However, segmentation of parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently, deep learning has developed rapidly, and Transformer-based networks have performed well on many computer vision tasks. However, Transformer-based networks have yet to be well used in parotid gland segmentation tasks. We collected a multi-center multimodal parotid gland MRI dataset and implemented parotid gland segmentation using a purely Transformer-based U-shaped segmentation network. We used both absolute and relative positional encoding to improve parotid gland segmentation and achieved multimodal information fusion without increasing the network computation. In addition, our novel training approach reduces the clinician's labeling workload by nearly half. Our method achieved good segmentation of both parotid glands and tumors. On the test set, our model achieved a Dice-Similarity Coefficient of 86.99%, Pixel Accuracy of 99.19%, Mean Intersection over Union of 81.79%, and Hausdorff Distance of 3.87. The purely Transformer-based U-shaped segmentation network we used outperforms other convolutional neural networks. In addition, our method can effectively fuse the information from multi-center multimodal MRI dataset, thus improving the parotid gland segmentation.
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Affiliation(s)
- Zi'an Xu
- Northeastern University, Shenyang, China
| | - Yin Dai
- Northeastern University, Shenyang, China.
| | - Fayu Liu
- China Medical University, Shenyang, China
| | - Siqi Li
- China Medical University, Shenyang, China
| | - Sheng Liu
- China Medical University, Shenyang, China
| | - Lifu Shi
- Liaoning Jiayin Medical Technology Co., Shenyang, China
| | - Jun Fu
- Northeastern University, Shenyang, China
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Liu X, Pan Y, Zhang X, Sha Y, Wang S, Li H, Liu J. A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences. Laryngoscope 2023; 133:327-335. [PMID: 35575610 PMCID: PMC10083903 DOI: 10.1002/lary.30154] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/17/2022] [Accepted: 04/12/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings. METHODS First, multimodal MRI sequences were collected from 266 patients with parotid neoplasms, and an artificial intelligence (AI)-based deep learning model was designed from scratch, combining the image classification network of Resnet and the Transformer network of Natural language processing. Second, the effectiveness of the deep learning model was improved through the multi-modality fusion of MRI sequences, and the fusion strategy of various MRI sequences was optimized. In addition, we compared the effectiveness of the model in the parotid neoplasm classification with experienced radiologists. RESULTS The deep learning model delivered reliable outcomes in differentiating benign and malignant parotid neoplasms. The model, which was trained by the fusion of T2-weighted, postcontrast T1-weighted, and diffusion-weighted imaging (b = 1000 s/mm2 ), produced the best result, with an accuracy score of 0.85, an area under the receiver operator characteristic (ROC) curve of 0.96, a sensitivity score of 0.90, and a specificity score of 0.84. In addition, the multi-modal paradigm exhibited reliable outcomes in diagnosing the pleomorphic adenoma and the Warthin tumor, but not in the identification of the basal cell adenoma. CONCLUSION An accurate and efficient AI based classification model was produced to classify parotid neoplasms, resulting from the fusion of multimodal MRI sequences. The effectiveness certainly outperformed the model with single MRI images or single MRI sequences as input, and potentially, experienced radiologists. LEVEL OF EVIDENCE 3 Laryngoscope, 133:327-335, 2023.
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Affiliation(s)
- Xu Liu
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, NHC Key Laboratory of Hearing Medicine (Fudan University), Shanghai, China
| | - Yucheng Pan
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Xin Zhang
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, NHC Key Laboratory of Hearing Medicine (Fudan University), Shanghai, China
| | - Yongfang Sha
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, NHC Key Laboratory of Hearing Medicine (Fudan University), Shanghai, China
| | - Shihui Wang
- Lab of Sensing and Computing, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
| | - Hongzhe Li
- Research Service, VA Loma Linda Healthcare System, Loma Linda, California, U.S.A.,Department of Otolaryngology-Head and Neck Surgery, Loma Linda University School of Medicine, Loma Linda, California, U.S.A
| | - Jianping Liu
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, NHC Key Laboratory of Hearing Medicine (Fudan University), Shanghai, China
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Geiger JL, Ismaila N, Beadle B, Caudell JJ, Chau N, Deschler D, Glastonbury C, Kaufman M, Lamarre E, Lau HY, Licitra L, Moore MG, Rodriguez C, Roshal A, Seethala R, Swiecicki P, Ha P. Management of Salivary Gland Malignancy: ASCO Guideline. J Clin Oncol 2021; 39:1909-1941. [PMID: 33900808 DOI: 10.1200/jco.21.00449] [Citation(s) in RCA: 154] [Impact Index Per Article: 51.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To provide evidence-based recommendations for practicing physicians and other healthcare providers on the management of salivary gland malignancy. METHODS ASCO convened an Expert Panel of medical oncology, surgical oncology, radiation oncology, neuroradiology, pathology, and patient advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2000 through 2020. Outcomes of interest included survival, diagnostic accuracy, disease recurrence, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. RESULTS The literature search identified 293 relevant studies to inform the evidence base for this guideline. Six main clinical questions were addressed, which included subquestions on preoperative evaluations, surgical diagnostic and therapeutic procedures, appropriate radiotherapy techniques, the role of systemic therapy, and follow-up evaluations. RECOMMENDATIONS When possible, evidence-based recommendations were developed to address the diagnosis and appropriate preoperative evaluations for patients with a salivary gland malignancy, therapeutic procedures, and appropriate treatment options in various salivary gland histologies.Additional information is available at www.asco.org/head-neck-cancer-guidelines.
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Affiliation(s)
| | | | | | | | | | | | | | - Marnie Kaufman
- Adenoid Cystic Carcinoma Research Foundation, Needham, MA
| | | | | | - Lisa Licitra
- Istituto Nazionale Tumori, Milan, Italy.,University of Milan, Milan, Italy
| | | | | | | | | | | | - Patrick Ha
- University of California San Francisco, San Francisco, CA
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