1
|
Liao NQ, Deng ZJ, Wei W, Lu JH, Li MJ, Ma L, Chen QF, Zhong JH. Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma. Comput Struct Biotechnol J 2024; 24:247-257. [PMID: 38617891 PMCID: PMC11015163 DOI: 10.1016/j.csbj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
Objectives Combination therapy of lenvatinib and immune checkpoint inhibitors (CLICI) has emerged as a promising approach for managing unresectable hepatocellular carcinoma (HCC). However, the response to such treatment is observed in only a subset of patients, underscoring the pressing need for reliable methods to identify potential responders. Materials & methods This was a retrospective analysis involving 120 patients with unresectable HCC. They were divided into training (n = 72) and validation (n = 48) cohorts. We developed an interpretable deep learning model using multiphase computed tomography (CT) images to predict whether patients will respond or not to CLICI treatment, based on the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1). We evaluated the models' performance and analyzed the impact of each CT phase. Critical regions influencing predictions were identified and visualized through heatmaps. Results The multiphase model outperformed the best biphase and uniphase models, achieving an area under the curve (AUC) of 0.802 (95% CI = 0.780-0.824). The portal phase images were found to significantly enhance the model's predictive accuracy. Heatmaps identified six critical features influencing treatment response, offering valuable insights to clinicians. Additionally, we have made this model accessible via a web server at http://uhccnet.com/ for ease of use. Conclusions The integration of multiphase CT images with deep learning-generated heatmaps for predicting treatment response provides a robust and practical tool for guiding CLICI therapy in patients with unresectable HCC.
Collapse
Affiliation(s)
- Nan-Qing Liao
- School of Medical, Guangxi University, Nanning, China
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Zhu-Jian Deng
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Wei Wei
- Radiology Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jia-Hui Lu
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Min-Jun Li
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Liang Ma
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Qing-Feng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jian-Hong Zhong
- Hepatobiliary Surgery Department, Guangxi Medical University Cancer Hospital, Nanning, China
| |
Collapse
|
2
|
Cheng J, Su W, Wang Y, Zhan Y, Wang Y, Yan S, Yuan Y, Chen L, Wei Z, Zhang S, Gao X, Tang Z. Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study. Jpn J Radiol 2024; 42:709-719. [PMID: 38409300 DOI: 10.1007/s11604-024-01544-0] [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: 10/21/2023] [Accepted: 01/29/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). MATERIALS AND METHODS This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets. RESULTS Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets. CONCLUSION The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.
Collapse
Affiliation(s)
- Jingfeng Cheng
- Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Wenzhe Su
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Yuzhe Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yang Zhan
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yin Wang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Shuyu Yan
- Fudan University, Shanghai, 200032, China
| | - Yuan Yuan
- Fudan University, Shanghai, 200032, China
| | | | - Zixun Wei
- Fudan University, Shanghai, 200032, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, 200233, China.
| | - Zuohua Tang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China.
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Yang K, Dong X, Tang F, Ye F, Chen B, Liang S, Zhang Y, Xu Y. A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma. Front Oncol 2024; 14:1377366. [PMID: 38947898 PMCID: PMC11211537 DOI: 10.3389/fonc.2024.1377366] [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: 01/27/2024] [Accepted: 05/15/2024] [Indexed: 07/02/2024] Open
Abstract
Background Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. Objectives To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. Materials and methods We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. Conclusions This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.
Collapse
Affiliation(s)
- Kaifan Yang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xiuyu Dong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Fan Tang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Feng Ye
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Bei Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Shujun Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
5
|
Ayoub NF, Glicksman JT. Artificial Intelligence in Rhinology. Otolaryngol Clin North Am 2024:S0030-6665(24)00068-9. [PMID: 38821734 DOI: 10.1016/j.otc.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical care and promote personalized medicine. Numerous research studies have been published demonstrating the development and clinical potential of AI models within the field. Most describe in silico evaluation models without direct clinical implementation. The major themes of existing studies include diagnostic or clinical decisions support, clustering patients into specific phenotypes or endotypes, predicting post-treatment outcomes, and surgical planning.
Collapse
Affiliation(s)
- Noel F Ayoub
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye and Ear/Harvard Medical School, Boston, MA, USA.
| | - Jordan T Glicksman
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye and Ear/Harvard Medical School, Boston, MA, USA
| |
Collapse
|
6
|
Zhang Y, Deng Y, Zou Q, Jing B, Cai P, Tian X, Yang Y, Li B, Liu F, Li Z, Liu Z, Feng S, Peng T, Dong Y, Wang X, Ruan G, He Y, Cui C, Li J, Luo X, Huang H, Chen H, Li S, Sun Y, Xie C, Wang L, Li C, Cai Q. Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging. Cell Rep Med 2024; 5:101551. [PMID: 38697104 PMCID: PMC11148767 DOI: 10.1016/j.xcrm.2024.101551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/05/2024] [Accepted: 04/11/2024] [Indexed: 05/04/2024]
Abstract
Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL.
Collapse
Affiliation(s)
- YuChen Zhang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China
| | - YiShu Deng
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, P.R. China
| | - QiHua Zou
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China
| | - BingZhong Jing
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - PeiQiang Cai
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
| | - XiaoPeng Tian
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China
| | - Yu Yang
- Department of Lymphadenoma and Head & Neck Medical Oncology, Fujian Provincial Cancer Hospital & Institute, Fuzhou, P.R. China
| | - BingZong Li
- Department of Hematology, The Second Affiliated Hospital of Suzhou University, Jiangsu, P.R. China
| | - Fang Liu
- Department of Pathology, The First People's Hospital of Foshan, Foshan, P.R. China
| | - ZhiHua Li
- Department of Oncology, Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, P.R. China
| | - ZaiYi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, P.R. China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, P.R. China
| | - ShiTing Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, P.R. China
| | - TingSheng Peng
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, P.R. China
| | - YuJun Dong
- Department of Hematology, Peking University First Hospital, Beijing 100034, P.R. China
| | - XinYan Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, P.R. China
| | - GuangYing Ruan
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
| | - Yun He
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
| | - ChunYan Cui
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
| | - Jiao Li
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
| | - Xiao Luo
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China
| | - HuiQiang Huang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China
| | - HaoHua Chen
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - SongQi Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, P.R. China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China
| | - ChuanMiao Xie
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, P.R. China.
| | - Liang Wang
- Department of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, P.R. China.
| | - ChaoFeng Li
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Information Technology Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China.
| | - QingQing Cai
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
| |
Collapse
|
7
|
King AD, Ai QYH, Lam WKJ, Tse IOL, So TY, Wong LM, Tsang JYM, Leung HS, Zee BCY, Hui EP, Ma BBY, Vlantis AC, van Hasselt AC, Chan ATC, Woo JKS, Chan KCA. Early detection of nasopharyngeal carcinoma: performance of a short contrast-free screening magnetic resonance imaging. J Natl Cancer Inst 2024; 116:665-672. [PMID: 38171488 DOI: 10.1093/jnci/djad260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/31/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Although contrast-enhanced magnetic resonance imaging (MRI) detects early-stage nasopharyngeal carcinoma (NPC) not detected by endoscopic-guided biopsy (EGB), a short contrast-free screening MRI would be desirable for NPC screening programs. This study evaluated a screening MRI in a plasma Epstein-Barr virus (EBV)-DNA NPC screening program. METHODS EBV-DNA-screen-positive patients underwent endoscopy, and endoscopy-positive patients underwent EGB. EGB was negative if the biopsy was negative or was not performed. Patients also underwent a screening MRI. Diagnostic performance was based on histologic confirmation of NPC in the initial study or during a follow-up period of at least 2 years. RESULTS The study prospectively recruited 354 patients for MRI and endoscopy; 40/354 (11.3%) endoscopy-positive patients underwent EGB. Eighteen had NPC (5.1%), and 336 without NPC (94.9%) were followed up for a median of 44.8 months. MRI detected additional NPCs in 3/18 (16.7%) endoscopy-negative and 2/18 (11.1%) EGB-negative patients (stage I/II, n = 4; stage III, n = 1). None of the 24 EGB-negative patients who were MRI-negative had NPC. MRI missed NPC in 2/18 (11.1%), one of which was also endoscopy-negative. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI, endoscopy, and EGB were 88.9%, 91.1%, 34.8%, 99.4%, and 91.0%; 77.8%, 92.3%, 35.0%, 98.7%, and 91.5%; and 66.7%, 92.3%, 31.6%, 98.1%, and 91.0%, respectively. CONCLUSION A quick contrast-free screening MRI complements endoscopy in NPC screening programs. In EBV-screen-positive patients, MRI enables early detection of NPC that is endoscopically occult or negative on EGB and increases confidence that NPC has not been missed.
Collapse
Affiliation(s)
- Ann D King
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - W K Jacky Lam
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Irene O L Tse
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lun M Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jayden Yip Man Tsang
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ho Sang Leung
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Benny C Y Zee
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Edwin P Hui
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Brigette B Y Ma
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Alexander C Vlantis
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andrew C van Hasselt
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Anthony T C Chan
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - John K S Woo
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - K C Allen Chan
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
8
|
Nakagawa J, Fujima N, Hirata K, Harada T, Wakabayashi N, Takano Y, Homma A, Kano S, Minowa K, Kudo K. Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach. Jpn J Radiol 2024; 42:450-459. [PMID: 38280100 PMCID: PMC11056334 DOI: 10.1007/s11604-023-01527-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/24/2023] [Indexed: 01/29/2024]
Abstract
PURPOSE To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance. MATERIALS AND METHODS We divided 100 malignant nasopharyngeal tumor lesions into a training (n = 70) and a test (n = 30) dataset. Two head/neck radiologists reviewed CT and MRI images and determined the positive/negative skull-base invasion status of each case (training dataset: 29 invasion-positive and 41 invasion-negative; test dataset: 13 invasion-positive and 17 invasion-negative). Preprocessing involved extracting continuous slices of the nasopharynx and clivus. The preprocessed training dataset was used for transfer learning with Residual Neural Networks 50 to create a diagnostic CNN model, which was then tested on the preprocessed test dataset to determine the invasion status and model performance. Original CT images from the test dataset were reviewed by a radiologist with extensive head/neck imaging experience (senior reader: SR) and another less-experienced radiologist (junior reader: JR). Gradient-weighted class activation maps (Grad-CAMs) were created to visualize the explainability of the invasion status classification. RESULTS The CNN model's diagnostic accuracy was 0.973, significantly higher than those of the two radiologists (SR: 0.838; JR: 0.595). Receiver operating characteristic curve analysis gave an area under the curve of 0.953 for the CNN model (versus 0.832 and 0.617 for SR and JR; both p < 0.05). The Grad-CAMs suggested that the invasion-negative cases were present predominantly in bone marrow, while the invasion-positive cases exhibited osteosclerosis and nasopharyngeal masses. CONCLUSIONS This CNN technique would be useful for CT-based diagnosis of skull-base invasion by nasopharyngeal malignancies.
Collapse
Affiliation(s)
- Junichi Nakagawa
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan.
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Nuclear Medicine, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Medical AI Research and Development Center, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Taisuke Harada
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Naoto Wakabayashi
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Yuki Takano
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Kazuyuki Minowa
- Faculty of Dental Medicine Department of Radiology, Hokkaido University, N13 W7, Kita-Ku, Sapporo, Hokkaido, 060-8586, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Department of Nuclear Medicine, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Medical AI Research and Development Center, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| |
Collapse
|
9
|
Rokhshad R, Salehi SN, Yavari A, Shobeiri P, Esmaeili M, Manila N, Motamedian SR, Mohammad-Rahimi H. Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis. Oral Radiol 2024; 40:1-20. [PMID: 37855976 DOI: 10.1007/s11282-023-00715-5] [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: 05/09/2023] [Accepted: 09/23/2023] [Indexed: 10/20/2023]
Abstract
PURPOSE This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data. METHODS Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks' funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA. RESULTS From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87-0.96). The DORs were 103 (27-251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis. CONCLUSION With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity.
Collapse
Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
| | - Seyyede Niloufar Salehi
- Executive Secretary of Research Committee, Board Director of Scientific Society, Dental Faculty, Azad University, Tehran, Iran
| | - Amirmohammad Yavari
- Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Nisha Manila
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
- Department of Diagnostic Sciences, Louisiana State University Health Science Center School of Dentistry, Louisiana, USA
| | - Saeed Reza Motamedian
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany.
- Dentofacial Deformities Research Center, Research Institute of Dental, Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjou Blvd, Tehran, Iran.
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
| |
Collapse
|
10
|
Huang YY, Deng YS, Liu Y, Qiang MY, Qiu WZ, Xia WX, Jing BZ, Feng CY, Chen HH, Cao X, Zhou JY, Huang HY, Zhan ZJ, Deng Y, Tang LQ, Mai HQ, Sun Y, Xie CM, Guo X, Ke LR, Lv X, Li CF. A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma. iScience 2023; 26:108347. [PMID: 38125021 PMCID: PMC10730347 DOI: 10.1016/j.isci.2023.108347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/08/2023] [Accepted: 10/24/2023] [Indexed: 12/23/2023] Open
Abstract
It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.
Collapse
Affiliation(s)
- Ying-Ying Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yi-Shu Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Meng-Yun Qiang
- Department of Radiation Oncology, Cancer Hospital of The University of Chinese Academy of Sciences, Hangzhou 310005, China
| | - Wen-Ze Qiu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou 510095, China
| | - Wei-Xiong Xia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Bing-Zhong Jing
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chen-Yang Feng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hao-Hua Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xun Cao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Critical Care Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Jia-Yu Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hao-Yang Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ze-Jiang Zhan
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ying Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Lin-Quan Tang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hai-Qiang Mai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chuan-Miao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xiang Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Liang-Ru Ke
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chao-Feng Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| |
Collapse
|
11
|
Shi Y, Wang H, Ji H, Liu H, Li Y, He N, Wei D, Huang Y, Dai Q, Wu J, Chen X, Zheng Y, Yu H. A deep weakly semi-supervised framework for endoscopic lesion segmentation. Med Image Anal 2023; 90:102973. [PMID: 37757643 DOI: 10.1016/j.media.2023.102973] [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: 09/05/2022] [Revised: 07/19/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023]
Abstract
In the field of medical image analysis, accurate lesion segmentation is beneficial for the subsequent clinical diagnosis and treatment planning. Currently, various deep learning-based methods have been proposed to deal with the segmentation task. Albeit achieving some promising performances, the fully-supervised learning approaches require pixel-level annotations for model training, which is tedious and time-consuming for experienced radiologists to collect. In this paper, we propose a weakly semi-supervised segmentation framework, called Point Segmentation Transformer (Point SEGTR). Particularly, the framework utilizes a small amount of fully-supervised data with pixel-level segmentation masks and a large amount of weakly-supervised data with point-level annotations (i.e., annotating a point inside each object) for network training, which largely reduces the demand of pixel-level annotations significantly. To fully exploit the pixel-level and point-level annotations, we propose two regularization terms, i.e., multi-point consistency and symmetric consistency, to boost the quality of pseudo labels, which are then adopted to train a student model for inference. Extensive experiments are conducted on three endoscopy datasets with different lesion structures and several body sites (e.g., colorectal and nasopharynx). Comprehensive experimental results finely substantiate the effectiveness and the generality of our proposed method, as well as its potential to loosen the requirements of pixel-level annotations, which is valuable for clinical applications.
Collapse
Affiliation(s)
- Yuxuan Shi
- ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China
| | - Hong Wang
- Tencent Jarvis Lab, Shenzhen 518000, China.
| | - Haoqin Ji
- Tencent Jarvis Lab, Shenzhen 518000, China
| | - Haozhe Liu
- Tencent Jarvis Lab, Shenzhen 518000, China
| | | | - Nanjun He
- Tencent Jarvis Lab, Shenzhen 518000, China
| | - Dong Wei
- Tencent Jarvis Lab, Shenzhen 518000, China
| | | | - Qi Dai
- ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China
| | - Jianrong Wu
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, China
| | - Xinrong Chen
- Academy for Engineering and Technology, Fudan University, 220 Handan Road, Shanghai 200033, China.
| | | | - Hongmeng Yu
- ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China; Research Units of New Technologies of Endoscopic Surgery in Skull Base Tumor, Chinese Academy of Medical Sciences, 2018RU003, China.
| |
Collapse
|
12
|
Deng Y, Huang Y, Jing B, Wu H, Qiu W, Chen H, Li B, Guo X, Xie C, Sun Y, Dai X, Lv X, Li C, Ke L. Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter study. Eur J Radiol 2023; 168:111084. [PMID: 37722143 DOI: 10.1016/j.ejrad.2023.111084] [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: 05/11/2023] [Revised: 08/18/2023] [Accepted: 09/04/2023] [Indexed: 09/20/2023]
Abstract
OBJECTIVES Accuracy in the detection of recurrent nasopharyngeal carcinoma (NPC) on follow-up magnetic resonance (MR) scans needs to be improved. MATERIAL AND METHODS A total of 5 035 follow-up MR scans from 5 035 survivors with treated NPC between April 2007 and July 2020 were retrospectively collected from three cancer centers for developing and evaluating the deep learning (DL) model MODERN (MR-based Deep learning model for dEtecting Recurrent Nasopharyngeal carcinoma). In a reader study with 220 scans, the accuracy of two radiologists in detecting recurrence on scans with vs without MODERN was evaluated. The performance was measured using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy with a 95% confidence interval (CI). RESULTS MODERN exhibited sound performance in the validation cohort (internal: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 1: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 2: ROC-AUC, 0.85, 95% CI, 0.82-0.88). In a reader study, MODERN alone achieved reliable accuracy compared to that of radiologists (MODERN: 84.1%, 95% CI, 79.3%-88.9%; competent: 80.9%, 95% CI, 75.7%-86.1%, P < 0.001; expert: 85.9%, 95% CI, 81.3%-90.5%, P < 0.001). The accuracy of radiologists was boosted by the MODERN score (competent with MODERN score: 84.6%, 95% CI, 79.8%-89.3%, P < 0.001; expert with MODERN score: 87.7%, 95% CI, 83.4%-92.1%, P < 0.001). CONCLUSION We developed a DL model for recurrence detection with reliable performance. Computer-human collaboration has the potential to refine the workflow in interpreting surveillant MR scans among patients with treated NPC.
Collapse
Affiliation(s)
- Yishu Deng
- School of Electronics and Information Technology, Sun Yat-sen University, No. 132 Waihuan East Road, Guangzhou 510006, Guangdong, China; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Information, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Yingying Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Bingzhong Jing
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Information, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Haijun Wu
- Department of Radiation Oncology, First People's Hospital of Foshan, No. 81 Lingnan North Road, Foshan 528000, Guangdong, China
| | - Wenze Qiu
- Department of Radiation Oncology, Guangzhou Medical University Affiliated Cancer Hospital, No. 78 Hengzhigang Road, Guangzhou 510030, Guangdong, China
| | - Haohua Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Information, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Bin Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Information, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Xiang Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Chuanmiao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China
| | - Xianhua Dai
- School of Electronics and Information Technology, Sun Yat-sen University, No. 132 Waihuan East Road, Guangzhou 510006, Guangdong, China
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China.
| | - Chaofeng Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Information, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China.
| | - Liangru Ke
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China.
| |
Collapse
|
13
|
Fujima N, Kamagata K, Ueda D, Fujita S, Fushimi Y, Yanagawa M, Ito R, Tsuboyama T, Kawamura M, Nakaura T, Yamada A, Nozaki T, Fujioka T, Matsui Y, Hirata K, Tatsugami F, Naganawa S. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magn Reson Med Sci 2023; 22:401-414. [PMID: 37532584 PMCID: PMC10552661 DOI: 10.2463/mrms.rev.2023-0047] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/09/2023] [Indexed: 08/04/2023] Open
Abstract
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.
Collapse
Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Kumamoto, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Okayama, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Hiroshima, Hiroshima, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| |
Collapse
|
14
|
Yang X, Wu J, Chen X. Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma. J Clin Med 2023; 12:jcm12093077. [PMID: 37176518 PMCID: PMC10178972 DOI: 10.3390/jcm12093077] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human-computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.
Collapse
Affiliation(s)
- Xinggang Yang
- Division of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Juan Wu
- Out-Patient Department, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| |
Collapse
|
15
|
Li S, Wan X, Deng YQ, Hua HL, Li SL, Chen XX, Zeng ML, Zha Y, Tao ZZ. Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued. Cancer Imaging 2023; 23:14. [PMID: 36759889 PMCID: PMC9912633 DOI: 10.1186/s40644-023-00530-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. METHODS A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. RESULTS The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. CONCLUSIONS The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.
Collapse
Affiliation(s)
- Song Li
- grid.89957.3a0000 0000 9255 8984Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029 China ,grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xia Wan
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yu-Qin Deng
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Hong-Li Hua
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Sheng-Lan Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xi-Xiang Chen
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Man-Li Zeng
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Ji L, Mao R, Wu J, Ge C, Xiao F, Xu X, Xie L, Gu X. Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12102478. [PMID: 36292167 PMCID: PMC9601165 DOI: 10.3390/diagnostics12102478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/03/2022] [Accepted: 10/09/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.
Collapse
Affiliation(s)
- Li Ji
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Rongzhi Mao
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Jian Wu
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Cheng Ge
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Feng Xiao
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| | - Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| | - Xiaofeng Gu
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| |
Collapse
|
18
|
Downregulated miR-150-5p in the Tissue of Nasopharyngeal Carcinoma. Genet Res (Camb) 2022; 2022:2485055. [PMID: 36118276 PMCID: PMC9467814 DOI: 10.1155/2022/2485055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 06/01/2022] [Accepted: 07/06/2022] [Indexed: 01/09/2023] Open
Abstract
The clinical significance and potential targets of miR-150-5p have not been elucidated in nasopharyngeal carcinoma (NPC). The pooled analysis based on 539 NPC samples and 75 non-NPC nasopharyngeal samples demonstrated that the expression of miR-150-5p was down-regulated in NPC, with the area under the curve being 0.89 and the standardized mean difference being -0.66. Subsequently, we further screened the differentially expressed genes (DEGs) of 14 datasets, including 312 NPC samples and 70 non-NPC nasopharyngeal samples. After the DEGs were narrowed down with the predicted targets from the miRWalk database, 1316 prospective target genes of miR-150-5p were identified. The enrichment analysis suggested that "pathways in cancer" was the most significant pathway. Finally, six hub genes of "pathways in cancer", including EGFR, TP53, HRAS, CCND1, CDH1, and FGF2, were screened out through the STRING database. In conclusion, the down-regulation of miR-150-5p modulates the tumorigenesis and progression of NPC.
Collapse
|
19
|
Wong LM, Ai QYH, Zhang R, Mo F, King AD. Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI. Cancers (Basel) 2022; 14:cancers14143433. [PMID: 35884494 PMCID: PMC9324280 DOI: 10.3390/cancers14143433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Discriminating early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) on MRI is a challenging but important task for the early detection of NPC in screening programs. Radiomics models have the potential to meet this challenge, but instability in the feature selection step may reduce their reliability. Therefore, in this study, we aim to discriminate between early-stage T1 NPC and BH on MRI using radiomics and propose a method to improve the stability of the feature selection step in the radiomics pipeline. A radiomics model was trained using data from 442 patients (221 early-stage T1 NPC and 221 with BH) scanned at 3T and tested on 213 patients (99 early-stage T1 NPC and 114 BH) scanned at 1.5T. To verify the improvement in feature selection stability, we compared our proposed ensemble technique, which uses a combination of bagging and boosting (BB-RENT), with the well-established elastic net. The proposed radiomics model achieved an area under the curve of 0.85 (95% confidence interval (CI): 0.82−0.89) and 0.80 (95% CI: 0.74−0.86) in discriminating NPC and BH in the 3T training and 1.5T testing cohort, respectively, using 17 features selected from a pool of 422 features by the proposed feature selection technique. BB-RENT showed a better feature selection stability compared to the elastic net (Jaccard index = 0.39 ± 0.14 and 0.24 ± 0.06, respectively; p < 0.001).
Collapse
Affiliation(s)
- Lun M. Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
| | - Qi Yong H. Ai
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Correspondence: (Q.Y.H.A.); (A.D.K.)
| | - Rongli Zhang
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
| | - Frankie Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Sir YK Pao Centre for Cancer, Hong Kong Cancer Institute and Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China;
| | - Ann D. King
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
- Correspondence: (Q.Y.H.A.); (A.D.K.)
| |
Collapse
|
20
|
Feng T, Fang Y, Pei Z, Li Z, Chen H, Hou P, Wei L, Wang R, Wang S. A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans. Front Neurosci 2022; 16:900519. [PMID: 35860294 PMCID: PMC9289618 DOI: 10.3389/fnins.2022.900519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/30/2022] [Indexed: 11/29/2022] Open
Abstract
Objective Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN. Methods A total of 1413 coronal and sagittal magnetic resonance images were collected from 695 patients with PAs. The enrolled images were divided into the invasive group (n = 530) and the non-invasive group (n = 883) according to the surgical observation of SFI. Before model training, 100 images were randomly selected for the external testing set. The remaining 1313 cases were randomly divided into the training and validation sets at a ratio of 80:20 for model training. Finally, the testing set was imported to evaluate the model performance. Results A CNN model with a 10-layer structure (6-layer convolution and 4-layer fully connected neural network) was constructed. After 1000 epoch of training, the model achieved high accuracy in identifying SFI (97.0 and 94.6% in the training and testing sets, respectively). The testing set presented excellent performance, with a model prediction accuracy of 96%, a sensitivity of 0.964, a specificity of 0.958, and an area under the receptor operator curve (AUC-ROC) value of 0.98. Four images in the testing set were misdiagnosed. Three images were misread with SFI (one with conchal type sphenoid sinus), and one image with a relatively intact sellar floor was not identified with SFI. Conclusion This study highlights the potential of the CNN model for the efficient assessment of PA invasion.
Collapse
Affiliation(s)
- Tianshun Feng
- Department of Neurosurgery, Dongfang Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yi Fang
- Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhijie Pei
- Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Ziqi Li
- Department of Neurosurgery, Dongfang Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Hongjie Chen
- Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Pengwei Hou
- Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Liangfeng Wei
- Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Renzhi Wang,
| | - Shousen Wang
- Department of Neurosurgery, Dongfang Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Neurosurgery, Fuzhou 900th Hospital, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Shousen Wang,
| |
Collapse
|
21
|
Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:3790269. [PMID: 35677026 PMCID: PMC9159821 DOI: 10.1155/2022/3790269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022]
Abstract
The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image super-resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U-net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant (Ktrans), rate constant (Kep), volume fraction (Ve), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U-net network model. Meanwhile, the difference was statistically significant (P < 0.05). Ktrans, Kep, and Ve in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side (P < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). The diagnostic sensitivity of DCE-MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE-MRI (P < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE-MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE-MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma.
Collapse
|
22
|
Li S, Hua HL, Li F, Kong YG, Zhu ZL, Li SL, Chen XX, Deng YQ, Tao ZZ. Anatomical Partition-Based Deep Learning: An Automatic Nasopharyngeal MRI Recognition Scheme. J Magn Reson Imaging 2022; 56:1220-1229. [PMID: 35157782 PMCID: PMC9541866 DOI: 10.1002/jmri.28112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 11/24/2022] Open
Abstract
Background Training deep learning (DL) models to automatically recognize diseases in nasopharyngeal MRI is a challenging task, and optimizing the performance of DL models is difficult. Purpose To develop a method of training anatomical partition‐based DL model which integrates knowledge of clinical anatomical regions in otorhinolaryngology to automatically recognize diseases in nasopharyngeal MRI. Study Type Single‐center retrospective study. Population A total of 2485 patients with nasopharyngeal diseases (age range 14–82 years, female, 779[31.3%]) and 600 people with normal nasopharynx (age range 18–78 years, female, 281[46.8%]) were included. Sequence 3.0 T; T2WI fast spin‐echo sequence. Assessment Full images (512 × 512) of 3085 patients constituted 100% of the dataset, 50% and 25% of which were randomly retained as two new datasets. Two new series of images (seg112 image [112 × 112] and seg224 image [224 × 224]) were automatically generated by a segmentation model. Four pretrained neural networks for nasopharyngeal diseases classification were trained under the nine datasets (full image, seg112 image, and seg224 image, each with 100% dataset, 50% dataset, and 25% dataset). Statistical Tests The receiver operating characteristic curve was used to evaluate the performance of the models. Analysis of variance was used to compare the performance of the models built with different datasets. Statistical significance was set at P < 0.05. Results When the 100% dataset was used for training, the performances of the models trained with the seg112 images (average area under the curve [aAUC] 0.949 ± 0.052), seg224 images (aAUC 0.948 ± 0.053), and full images (aAUC 0.935 ± 0.053) were similar (P = 0.611). When the 25% dataset was used for training, the mean aAUC of the models that were trained with seg112 images (0.823 ± 0.116) and seg224 images (0.765 ± 0.155) was significantly higher than the models that were trained with full images (0.640 ± 0.154). Data Conclusion The proposed method can potentially improve the performance of the DL model for automatic recognition of diseases in nasopharyngeal MRI. Level of Evidence 4 Technical Efficacy Stage 1
Collapse
Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fen Li
- Department of Otolaryngology-Head and Neck Surgery, Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yong-Gang Kong
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.,Department of Otolaryngology-Head and Neck Surgery, Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-Lan Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xi-Xiang Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.,Department of Otolaryngology-Head and Neck Surgery, Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
23
|
Ng WT, But B, Choi HCW, de Bree R, Lee AWM, Lee VHF, López F, Mäkitie AA, Rodrigo JP, Saba NF, Tsang RKY, Ferlito A. Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management - A Systematic Review. Cancer Manag Res 2022; 14:339-366. [PMID: 35115832 PMCID: PMC8801370 DOI: 10.2147/cmar.s341583] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/25/2021] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Nasopharyngeal carcinoma (NPC) is endemic to Eastern and South-Eastern Asia, and, in 2020, 77% of global cases were diagnosed in these regions. Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. METHODS The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles. RESULTS A total of 78 articles were reviewed after removing duplicates and papers that did not meet the inclusion and exclusion criteria. After quality assessment, 60 papers were included in the current study. There were four main types of applications, which were auto-contouring, diagnosis, prognosis, and miscellaneous applications (especially on radiotherapy planning). The different forms of convolutional neural networks (CNNs) accounted for the majority of DL algorithms used, while the artificial neural network (ANN) was the most frequent ML model implemented. CONCLUSION There is an overall positive impact identified from AI implementation in the management of NPC. With improving AI algorithms, we envisage AI will be available as a routine application in a clinical setting soon.
Collapse
Affiliation(s)
- Wai Tong Ng
- Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Barton But
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Horace C W Choi
- Department of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anne W M Lee
- Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Victor H F Lee
- Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Fernando López
- Department of Otolaryngology, Hospital Universitario Central de Asturias (HUCA), Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo, Oviedo, 33011, Spain
- Spanish Biomedical Research Network Centre in Oncology, CIBERONC, Madrid, 28029, Spain
| | - Antti A Mäkitie
- Department of Otorhinolaryngology - Head and Neck Surgery, HUS Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Juan P Rodrigo
- Department of Otolaryngology, Hospital Universitario Central de Asturias (HUCA), Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo, Oviedo, 33011, Spain
- Spanish Biomedical Research Network Centre in Oncology, CIBERONC, Madrid, 28029, Spain
| | - Nabil F Saba
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, USA
| | - Raymond K Y Tsang
- Division of Otorhinolaryngology, Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, Padua, Italy
| |
Collapse
|
24
|
Abstract
Nasopharyngeal carcinoma is endemic in parts of the world such as southern China and Southeast Asia. It is predominantly an undifferentiated carcinoma with a strong genetic basis and a close association with the Epstein-Barr virus. The ability of MR imaging to depict the boundaries of the primary tumor and its relationship with the complex structures of the skull base makes it the technique of choice for imaging of this disease in the head and neck. This article describes the MR imaging findings pertinent to staging and management and a new role of MR imaging in early cancer detection, in addition to a brief discussion of differential diagnoses.
Collapse
Affiliation(s)
- Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong SAR, China.
| |
Collapse
|
25
|
Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
Collapse
Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| |
Collapse
|
26
|
Are asymptomatic gastrointestinal findings on imaging more common in COVID-19 infection? Study to determine frequency of abdominal findings of COVID-19 infection in patients with and without abdominal symptoms and in patients with chest-only CT scans. Abdom Radiol (NY) 2021; 46:2407-2414. [PMID: 33394096 PMCID: PMC7780216 DOI: 10.1007/s00261-020-02920-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To identify incidence of abdominal findings in COVID-19 patients with and without abdominal symptoms on various imaging modalities including chest-only CT scans and to correlate them with clinical, laboratory and chest CT findings. MATERIALS AND METHODS In this retrospective study, we searched our clinical database between March 1st, 2020 and May 22nd, 2020 to identify patients who had positive real-time reverse transcriptase polymerase chain reaction (RT-PCR) on throat swabs for COVID-19, had availability of clinical, laboratory information and had availability of CT scan of chest or abdominal radiograph, abdominal ultrasound or CT scan within 2 weeks of the diagnosis. Abdominal imaging findings on all imaging modalities were documented. Chest CT severity score (CT-SS) was assessed in all patients. Clinical and laboratory findings were recorded from the electronic medical record. Statistical analysis was performed to determine correlation of abdominal findings with CT-SS, clinical and laboratory findings. RESULTS Out of 264 patients with positive RT-PCR, 73 patients (38 males and 35 females; 35 African American) with mean age of 62.2 (range 21-94) years were included. The median CTSS was 13.5 (IQR 75-25 18-8). Most common finding in the abdomen on CT scans (n = 72) were in the gastrointestinal system in 13/72 patients (18.1%) with fluid-filled colon without wall thickening or pericolonic stranding (n = 12) being the most common finding. Chest-only CT (n = 49) found bowel findings in 3 patients. CTSS did not differ in terms of age, sex, race or number of comorbidities but was associated with longer duration of hospitalization (p = 0.0.0256), longer intensive care unit stay (p = 0.0263), more frequent serum lactate dehydrogenase elevation (p = 0.0120) and serum C-reactive protein elevation (p = 0.0402). No statistically significant correlation of occurrence of bowel abnormalities with CTSS, clinical or laboratory features. Deep venous thrombosis was seen in 7/72 patients (9.8%) with three patients developing pulmonary embolism CONCLUSION: Abnormal bowel is the most common finding in the abdomen in patients with COVID-19 infection, is often without abdominal symptoms and occurs independent of severity of pulmonary involvement, other clinical and laboratory features.
Collapse
|