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Oliver J, Alapati R, Lee J, Bur A. Artificial Intelligence in Head and Neck Surgery. Otolaryngol Clin North Am 2024:S0030-6665(24)00070-7. [PMID: 38910064 DOI: 10.1016/j.otc.2024.05.001] [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] [Indexed: 06/25/2024]
Abstract
This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.
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Affiliation(s)
- Jamie Oliver
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Rahul Alapati
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Jason Lee
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Andrés Bur
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
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2
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Liang S, Dong X, Yang K, Chu Z, Tang F, Ye F, Chen B, Guan J, Zhang Y. A multi-perspective information aggregation network for automated T-staging detection of nasopharyngeal carcinoma. Phys Med Biol 2022; 67. [PMID: 36541557 DOI: 10.1088/1361-6560/aca516] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
AccurateT-staging is important when planning personalized radiotherapy. However,T-staging via manual slice-by-slice inspection is time-consuming while tumor sizes and shapes are heterogeneous, and junior physicians find such inspection challenging. With inspiration from oncological diagnostics, we developed a multi-perspective aggregation network that incorporated various diagnosis-oriented knowledge which allowed automated nasopharyngeal carcinomaT-staging detection (TSD Net). Specifically, our TSD Net was designed in multi-branch architecture, which can capture tumor size and shape information (basic knowledge), strongly correlated contextual features, and associations between the tumor and surrounding tissues. We defined the association between the tumor and surrounding tissues by a signed distance map which can embed points and tumor contours in higher-dimensional spaces, yielding valuable information regarding the locations of tissue associations. TSD Net finally outputs aT1-T4 stage prediction by aggregating data from the three branches. We evaluated TSD Net by using the T1-weighted contrast-enhanced magnetic resonance imaging database of 320 patients in a three-fold cross-validation manner. The results show that the proposed method achieves a mean area under the curve (AUC) as high as 87.95%. We also compared our method to traditional classifiers and a deep learning-based method. Our TSD Net is efficient and accurate and outperforms other methods.
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Affiliation(s)
- Shujun Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Xiuyu Dong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Kaifan Yang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Zhiqin Chu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Fan Tang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Feng Ye
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Bei Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Jian Guan
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
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3
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Santer M, Kloppenburg M, Gottfried TM, Runge A, Schmutzhard J, Vorbach SM, Mangesius J, Riedl D, Mangesius S, Widmann G, Riechelmann H, Dejaco D, Freysinger W. Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review. Cancers (Basel) 2022; 14:5397. [PMID: 36358815 PMCID: PMC9654953 DOI: 10.3390/cancers14215397] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 07/22/2023] Open
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.
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Affiliation(s)
- Matthias Santer
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Marcel Kloppenburg
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Timo Maria Gottfried
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Annette Runge
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Joachim Schmutzhard
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Samuel Moritz Vorbach
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Julian Mangesius
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - David Riedl
- University Hospital of Psychiatry II, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Ludwig-Boltzmann Institute for Rehabilitation Research, 1100 Vienna, Austria
| | - Stephanie Mangesius
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gerlig Widmann
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Herbert Riechelmann
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Daniel Dejaco
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Wolfgang Freysinger
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Martinino A, Aloulou M, Chatterjee S, Scarano Pereira JP, Singhal S, Patel T, Kirchgesner TPE, Agnes S, Annunziata S, Treglia G, Giovinazzo F. Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review. J Clin Med 2022; 11:6368. [PMID: 36362596 PMCID: PMC9655417 DOI: 10.3390/jcm11216368] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 09/21/2023] Open
Abstract
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.
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Affiliation(s)
| | | | - Surobhi Chatterjee
- Department of Internal Medicine, King George’s Medical University, Lucknow 226003, Uttar Pradesh, India
| | | | - Saurabh Singhal
- Department of HPB Surgery and Liver Transplantation, BLK-MAX Superspeciality Hospital, New Delhi 110005, Delhi, India
| | - Tapan Patel
- Department of Surgery, Baroda Medical College and SSG Hospital, Vadodara 390001, Gujarat, India
| | - Thomas Paul-Emile Kirchgesner
- Département of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1348 Brussels, Belgium
| | - Salvatore Agnes
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Salvatore Annunziata
- Unit of Nuclear Medicine, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Giorgio Treglia
- Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Francesco Giovinazzo
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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Liu Y, Han G, Liu X. Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:5875. [PMID: 35957432 PMCID: PMC9371217 DOI: 10.3390/s22155875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/23/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.
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Affiliation(s)
- Yi Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
- Sun Yat-sen University, Guangzhou 510275, China
| | - Guanghui Han
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
- Sun Yat-sen University, Guangzhou 510275, China
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Xiujian Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
- Sun Yat-sen University, Guangzhou 510275, China
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6
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Huang Z, Tang S, Chen Z, Wang G, Shen H, Zhou Y, Wang H, Fan W, Liang D, Hu Y, Hu Z. TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner. Comput Biol Med 2022; 148:105869. [PMID: 35905660 DOI: 10.1016/j.compbiomed.2022.105869] [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: 03/16/2022] [Revised: 06/20/2022] [Accepted: 07/09/2022] [Indexed: 11/17/2022]
Abstract
Nasopharyngeal carcinoma (NPC) is a malignant tumor, and the main treatment is radiotherapy. Accurate delineation of the target tumor is essential for radiotherapy of NPC. NPC tumors are small in size and vary widely in shape and structure, making it a time-consuming and laborious task for even experienced radiologists to manually outline tumors. However, the segmentation performance of current deep learning models is not satisfactory, mainly manifested by poor segmentation boundaries. To solve this problem, this paper proposes a segmentation method for nasopharyngeal carcinoma based on dynamic PET-CT image data, whose input data include CT, PET, and parametric images (Ki images). This method uses a generative adversarial network with a modified UNet integrated with a Transformer as the generator (TG-Net) to achieve automatic segmentation of NPC on combined CT-PET-Ki images. In the coding stage, TG-Net uses moving windows to replace traditional pooling operations to obtain patches of different sizes, which can reduce information loss in the coding process. Moreover, the introduction of Transformer can make the network learn more representative features and improve the discriminant ability of the model, especially for tumor boundaries. Finally, the results of fivefold cross validation with an average Dice similarity coefficient score of 0.9135 show that our method has good segmentation performance. Comparative experiments also show that our network structure is superior to the most advanced methods in the segmentation of NPC. In addition, this work is the first to use Ki images to assist tumor segmentation. We also demonstrated the usefulness of adding Ki images to aid in tumor segmentation.
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Affiliation(s)
- Zhengyong Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Si Tang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Zixiang Chen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Guoshuai Wang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Hao Shen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Haining Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Wei Fan
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yingying Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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Marcu LG, Marcu DC. Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy. J Pers Med 2021; 11:jpm11111094. [PMID: 34834445 PMCID: PMC8625829 DOI: 10.3390/jpm11111094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 12/12/2022] Open
Abstract
Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.
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Affiliation(s)
- Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia
- Correspondence:
| | - David C. Marcu
- Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania;
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Mahmood H, Shaban M, Rajpoot N, Khurram SA. Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer 2021; 124:1934-1940. [PMID: 33875821 PMCID: PMC8184820 DOI: 10.1038/s41416-021-01386-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/11/2021] [Accepted: 03/31/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. METHODS Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009-2020). No restrictions were placed on the AI/ML method or imaging modality used. RESULTS In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). CONCLUSIONS There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.
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Affiliation(s)
- Hanya Mahmood
- Academic Unit of Oral & Maxillofacial Surgery, School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
| | - Muhammad Shaban
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Syed A Khurram
- Unit of Oral & Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
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Bi L, Fulham M, Li N, Liu Q, Song S, Dagan Feng D, Kim J. Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106043. [PMID: 33744750 DOI: 10.1016/j.cmpb.2021.106043] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/04/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE [18f]-fluorodeoxyglucose (fdg) positron emission tomography - computed tomography (pet-ct) is now the preferred imaging modality for staging many cancers. Pet images characterize tumoral glucose metabolism while ct depicts the complementary anatomical localization of the tumor. Automatic tumor segmentation is an important step in image analysis in computer aided diagnosis systems. Recently, fully convolutional networks (fcns), with their ability to leverage annotated datasets and extract image feature representations, have become the state-of-the-art in tumor segmentation. There are limited fcn based methods that support multi-modality images and current methods have primarily focused on the fusion of multi-modality image features at various stages, i.e., early-fusion where the multi-modality image features are fused prior to fcn, late-fusion with the resultant features fused and hyper-fusion where multi-modality image features are fused across multiple image feature scales. Early- and late-fusion methods, however, have inherent, limited freedom to fuse complementary multi-modality image features. The hyper-fusion methods learn different image features across different image feature scales that can result in inaccurate segmentations, in particular, in situations where the tumors have heterogeneous textures. METHODS we propose a recurrent fusion network (rfn), which consists of multiple recurrent fusion phases to progressively fuse the complementary multi-modality image features with intermediary segmentation results derived at individual recurrent fusion phases: (1) the recurrent fusion phases iteratively learn the image features and then refine the subsequent segmentation results; and, (2) the intermediary segmentation results allows our method to focus on learning the multi-modality image features around these intermediary segmentation results, which minimize the risk of inconsistent feature learning. RESULTS we evaluated our method on two pathologically proven non-small cell lung cancer pet-ct datasets. We compared our method to the commonly used fusion methods (early-fusion, late-fusion and hyper-fusion) and the state-of-the-art pet-ct tumor segmentation methods on various network backbones (resnet, densenet and 3d-unet). Our results show that the rfn provides more accurate segmentation compared to the existing methods and is generalizable to different datasets. CONCLUSIONS we show that learning through multiple recurrent fusion phases allows the iterative re-use of multi-modality image features that refines tumor segmentation results. We also identify that our rfn produces consistent segmentation results across different network architectures.
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Affiliation(s)
- Lei Bi
- School of Computer Science, University of Sydney, NSW, Australia; Australian Research Council Training Centre for Innovative Bioengineering, NSW, Australia.
| | - Michael Fulham
- School of Computer Science, University of Sydney, NSW, Australia; Australian Research Council Training Centre for Innovative Bioengineering, NSW, Australia; Department of Molecular Imaging, Royal Prince Alfred Hospital, NSW, Australia
| | - Nan Li
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiufang Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - David Dagan Feng
- School of Computer Science, University of Sydney, NSW, Australia; Australian Research Council Training Centre for Innovative Bioengineering, NSW, Australia; Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Jinman Kim
- School of Computer Science, University of Sydney, NSW, Australia; Australian Research Council Training Centre for Innovative Bioengineering, NSW, Australia.
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10
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Herskovits EH. Artificial intelligence in molecular imaging. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:824. [PMID: 34268437 PMCID: PMC8246206 DOI: 10.21037/atm-20-6191] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
AI has, to varying degrees, affected all aspects of molecular imaging, from image acquisition to diagnosis. During the last decade, the advent of deep learning in particular has transformed medical image analysis. Although the majority of recent advances have resulted from neural-network models applied to image segmentation, a broad range of techniques has shown promise for image reconstruction, image synthesis, differential-diagnosis generation, and treatment guidance. Applications of AI for drug design indicate the way forward for using AI to facilitate molecular-probe design, which is still in its early stages. Deep-learning models have demonstrated increased efficiency and image quality for PET reconstruction from sinogram data. Generative adversarial networks (GANs), which are paired neural networks that are jointly trained to generate and classify images, have found applications in modality transformation, artifact reduction, and synthetic-PET-image generation. Some AI applications, based either partly or completely on neural-network approaches, have demonstrated superior differential-diagnosis generation relative to radiologists. However, AI models have a history of brittleness, and physicians and patients may not trust AI applications that cannot explain their reasoning. To date, the majority of molecular-imaging applications of AI have been confined to research projects, and are only beginning to find their ways into routine clinical workflows via commercialization and, in some cases, integration into scanner hardware. Evaluation of actual clinical products will yield more realistic assessments of AI’s utility in molecular imaging.
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Affiliation(s)
- Edward H Herskovits
- Department of Diagnostic Radiology and Nuclear Medicine, The University of Maryland, Baltimore, School of Medicine, Baltimore, MD, USA
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11
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Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102178] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Zhao L, Lu Z, Jiang J, Zhou Y, Wu Y, Feng Q. Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images. J Digit Imaging 2020; 32:462-470. [PMID: 30719587 DOI: 10.1007/s10278-018-00173-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography-computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance.
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Affiliation(s)
- Lijun Zhao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Zixiao Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Jun Jiang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yujia Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yi Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
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Shaikh F, Dehmeshki J, Bisdas S, Roettger-Dupont D, Kubassova O, Aziz M, Awan O. Artificial Intelligence-Based Clinical Decision Support Systems Using Advanced Medical Imaging and Radiomics. Curr Probl Diagn Radiol 2020; 50:262-267. [PMID: 32591104 DOI: 10.1067/j.cpradiol.2020.05.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/04/2020] [Accepted: 05/26/2020] [Indexed: 01/09/2023]
Abstract
Artificial intelligence (AI) is poised to make a veritable impact in medicine. Clinical decision support (CDS) is an important area where AI can augment the clinician's capability to collect, understand and make inferences on an overwhelming volume of patient data to reach the optimal clinical decision. Advancements in medical image analysis, such as Radiomics, and data computation, such as machine learning, have expanded our understanding of disease processes and their management. In this article, we review the most relevant concepts of AI as applicable to advanced imaging-based clinical decision support systems.
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Affiliation(s)
| | | | | | | | | | | | - Omer Awan
- Department of Radiology, University of Maryland, Baltimore, MD
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Radiomic biomarkers for head and neck squamous cell carcinoma. Strahlenther Onkol 2020; 196:868-878. [PMID: 32495038 DOI: 10.1007/s00066-020-01638-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 05/13/2020] [Indexed: 12/22/2022]
Abstract
Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell carcinoma (HNSCC). A major limitation of tissue- and blood-derived tumor markers is the lack of spatial resolution to image tumor heterogeneity. Tissue markers derived from tumor biopsies usually represent only a small tumor subregion at a single timepoint and are therefore often not representative of the tumors' biology or the biological alterations during and after treatment. Similarly, liquid biopsies give an overall picture of the tumors' secreted factors but completely lack any spatial resolution. Radiomics has the potential to give complete three-dimensional information about the tumor. We conducted a comprehensive literature search to assess the correlation of radiomics to tumor biology and treatment outcome in HNSCC and to assess current limitations of the radiomic biomarkers. In total, 25 studies that explored the ability of radiomics to predict tumor biology and phenotype in HNSCC and 28 studies that explored radiomics to predict post-treatment events were identified. Out of these 53 studies, only three failed to show a significant correlation. The major technical challenges are currently artifacts due to metal implants, non-standardized contrast injection, and delineation uncertainties. All studies to date were retrospective and none of the above-mentioned radiomics signatures have been validated in an independent cohort using an independent software implementation, which shows that transferability due to the numerous technical challenges is currently a major limitation. However, radiomics is a very young field and these studies hopefully pave the way for clinical implementation of radiomics for HNSCC in the future.
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Feeding the Data Monster: Data Science in Head and Neck Cancer for Personalized Therapy. J Am Coll Radiol 2019; 16:1695-1701. [PMID: 31238024 DOI: 10.1016/j.jacr.2019.05.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 05/18/2019] [Accepted: 05/28/2019] [Indexed: 01/14/2023]
Abstract
OBJECTIVE Head and neck carcinomas are clinically challenging malignancies because of tumor heterogeneities and resilient tumor subvolumes that require individualized treatment planning and delivery for an improved outcome. Although current approaches to diagnosis and therapy have boosted locoregional control, the long-term survival in this patient group remains unchanged over the last decades. A new approach to head and neck cancer management is therefore needed to better identify patient subgroups that are responsive to specific therapies. The aim of this article is to review the current status of knowledge and practice utilizing big data toward personalized therapy in head and neck cancers based on CT and PET imaging modalities. METHODS Literature published in English since 2000 was searched using Medline. Additional articles were retrieved via pearling of identified literature. Publications were reviewed and summarized in tabulated format. RESULTS Studies based on big data in head and neck cancer are limited; however, the field of radiomics is under continuous development and provides valuable input for personalized treatment. Using PET/PET CT biomarkers for patient treatment individualization and response prediction seems promising, especially in regard to detection of hypoxia and clonogenic cancer stem cells. Literature shows that macroscopic changes in medical images (whether structural or functional) are correlated with biologic and biochemical changes within a tumor. CONCLUSION Current trends in data science suggest that the ideal model for decision support in head and neck cancers should be based on human-machine collaboration, namely, on (1) software-based algorithms, (2) physician innovation collaboratives, and (3) clinician mix optimization.
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Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3882-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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17
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Resteghini C, Trama A, Borgonovi E, Hosni H, Corrao G, Orlandi E, Calareso G, De Cecco L, Piazza C, Mainardi L, Licitra L. Big Data in Head and Neck Cancer. Curr Treat Options Oncol 2018; 19:62. [DOI: 10.1007/s11864-018-0585-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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18
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A smart and operator independent system to delineate tumours in Positron Emission Tomography scans. Comput Biol Med 2018; 102:1-15. [PMID: 30219733 DOI: 10.1016/j.compbiomed.2018.09.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 08/20/2018] [Accepted: 09/06/2018] [Indexed: 12/30/2022]
Abstract
Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R2 = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.
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Comelli A, Stefano A, Benfante V, Russo G. Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies. PATTERN RECOGNITION AND IMAGE ANALYSIS 2018. [DOI: 10.1134/s1054661818010054] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies. Comput Med Imaging Graph 2017; 60:3-10. [DOI: 10.1016/j.compmedimag.2016.11.008] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 11/29/2016] [Accepted: 11/30/2016] [Indexed: 11/20/2022]
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22
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Computer-aided diagnosis from weak supervision: a benchmarking study. Comput Med Imaging Graph 2014; 42:44-50. [PMID: 25475486 DOI: 10.1016/j.compmedimag.2014.11.010] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Revised: 05/28/2014] [Accepted: 11/10/2014] [Indexed: 11/20/2022]
Abstract
Supervised machine learning is a powerful tool frequently used in computer-aided diagnosis (CAD) applications. The bottleneck of this technique is its demand for fine grained expert annotations, which are tedious for medical image analysis applications. Furthermore, information is typically localized in diagnostic images, which makes representation of an entire image by a single feature set problematic. The multiple instance learning framework serves as a remedy to these two problems by allowing labels to be provided for groups of observations, called bags, and assuming the group label to be the maximum of the instance labels within the bag. This setup can effectively be applied to CAD by splitting a given diagnostic image into a Cartesian grid, treating each grid element (patch) as an instance by representing it with a feature set, and grouping instances belonging to the same image into a bag. We quantify the power of existing multiple instance learning methods by evaluating their performance on two distinct CAD applications: (i) Barrett's cancer diagnosis and (ii) diabetic retinopathy screening. In the experiments, mi-Graph appears as the best-performing method in bag-level prediction (i.e. diagnosis) for both of these applications that have drastically different visual characteristics. For instance-level prediction (i.e. disease localization), mi-SVM ranks as the most accurate method.
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