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Chen M, Wang K, Dohopolski M, Morgan H, Sher D, Wang J. TransAnaNet: Transformer-based anatomy change prediction network for head and neck cancer radiotherapy. Med Phys 2025; 52:3015-3029. [PMID: 39887473 PMCID: PMC12059511 DOI: 10.1002/mp.17655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 01/11/2025] [Accepted: 01/14/2025] [Indexed: 02/01/2025] Open
Abstract
BACKGROUND Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head-neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, the prediction of anatomical change during radiotherapy for HNC patients is of importance to optimize patient clinical benefit and treatment resources. Current studies focus on developing binary ART eligibility classification models to identify patients who would experience significant anatomical change, but these models lack the ability to present the complex patterns and variations in anatomical changes over time. Vision Transformers (ViTs) represent a recent advancement in neural network architectures, utilizing self-attention mechanisms to process image data. Unlike traditional Convolutional Neural Networks (CNNs), ViTs can capture global contextual information more effectively, making them well-suited for image analysis and image generation tasks that involve complex patterns and structures, such as predicting anatomical changes in medical imaging. PURPOSE The purpose of this study is to assess the feasibility of using a ViT-based neural network to predict radiotherapy-induced anatomic change of HNC patients. METHODS We retrospectively included 121 HNC patients treated with definitive chemoradiotherapy (CRT) or radiation alone. We collected the planning computed tomography image (pCT), planned dose, cone beam computed tomography images (CBCTs) acquired at the initial treatment (CBCT01) and Fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs of each patient for model construction and evaluation. A UNet-style Swin-Transformer-based ViT network was designed to learn the spatial correspondence and contextual information from embedded image patches of CT, dose, CBCT01, GTVp, and GTVn. The deformation vector field between CBCT01 and CBCT21 was estimated by the model as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn, and patient body for volumetric change evaluation. We used data from 101 patients for training and validation, and the remaining 20 patients for testing. Image and volumetric similarity metrics including mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), Dice coefficient, and average surface distance were used to measure the similarity between the target image and predicted CBCT. Anatomy change prediction performance of the proposed model was compared to a CNN-based prediction model and a traditional ViT-based prediction model. RESULTS The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE, PSNR, and SSIM between the normalized predicted CBCT and CBCT21 are 0.009, 20.266, and 0.933, while the average Dice coefficient between body mask, GTVp mask, and GTVn mask is 0.972, 0.792, and 0.821, respectively. CONCLUSIONS The proposed method showed promising performance for predicting radiotherapy-induced anatomic change, which has the potential to assist in the decision-making of HNC ART.
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Affiliation(s)
- Meixu Chen
- Medical Artificial Intelligence and Automation (MAIA) LabDepartment of Radiation OncologyUT Southwestern Medical CenterDallasTexasUSA
| | - Kai Wang
- Medical Artificial Intelligence and Automation (MAIA) LabDepartment of Radiation OncologyUT Southwestern Medical CenterDallasTexasUSA
- Department of Radiation OncologyUniversity of Maryland Medical CenterBaltimoreMarylandUSA
| | - Michael Dohopolski
- Medical Artificial Intelligence and Automation (MAIA) LabDepartment of Radiation OncologyUT Southwestern Medical CenterDallasTexasUSA
| | - Howard Morgan
- Medical Artificial Intelligence and Automation (MAIA) LabDepartment of Radiation OncologyUT Southwestern Medical CenterDallasTexasUSA
- Department of Radiation OncologyCentral Arkansas Radiation Therapy InstituteLittle RockArkansasUSA
| | - David Sher
- Medical Artificial Intelligence and Automation (MAIA) LabDepartment of Radiation OncologyUT Southwestern Medical CenterDallasTexasUSA
| | - Jing Wang
- Medical Artificial Intelligence and Automation (MAIA) LabDepartment of Radiation OncologyUT Southwestern Medical CenterDallasTexasUSA
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Sun J, Lam SKE, Teng X, Zhang J, Lee FKH, Yip CWY, Chow JCH, Lee VHF, Sun Y, Cai J. Predicting disease progression from the rate of bodyweight change in nasopharyngeal carcinoma patient during radiotherapy. Sci Rep 2025; 15:7490. [PMID: 40032903 PMCID: PMC11876310 DOI: 10.1038/s41598-025-88810-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Accepted: 01/30/2025] [Indexed: 03/05/2025] Open
Abstract
Bodyweight loss is a common occurrence in Nasopharyngeal Carcinoma (NPC) patients during Radiotherapy (RT). Previous studies found that the prognostic value of percentage weight loss (pWL) during RT is not credible. We aimed to develop a novel progression predictor surrogated to pWL by modelling all bodyweight records measured during the treatment interval. This retrospective study included two independent hospitals of 624 patients. The Predicted Progression Probability (PPP) was obtained from deep learning-guided differential equation solution, model by the patient's age, sex, body height, and the weekly measured bodyweight records. The performance of PPP in predicting disease progression was assessed, its association with prognosis and adjuvant chemotherapy response was evaluated. The PPP was learnt from the training cohort (N = 257) with 7 weeks of bodyweight records. The prediction performance was validated with 367 patients of the testing cohort sub-divided according to the number of bodyweight records found. The area under of curve for patients with 7 weeks (N = 155), 6 weeks (N = 176), and 5 weeks bodyweight records (N = 32) were 0.76, 0.73, and 0.95 respectively. PPP was significantly associated with progression-free and remained an independent prognostic factor adjusting for clinicopathologic variables in multivariate analysis in all study cohort (adjusted hazard ratio [HR] range: 2.50-7.04, all p < 0.001). Patients with high-PPP derived progression benefit from adjuvant chemotherapy (HR: 0.41-0.54, all p < 0.03), whereas those with low-PPP did not for both cohorts. The trajectory of bodyweight change during RT is more robust than the pWL to give a progression prediction after RT. The PPP is a reliable predictor for estimating the risk of residual diseases after RT course, which also helps to predict adjuvant chemotherapy response in locally advanced NPC patients.
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Affiliation(s)
- Jiachen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Sai Kit Edmond Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong
| | | | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnostics and Therapy, Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
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Li G, Zhang Y, Tang J, Chen S, Liu Q, Zhang J, Shi S. Diffusion-Weighted Imaging-Based Radiomics Features and Machine Learning Method to Predict the 90-Day Prognosis in Patients With Acute Ischemic Stroke. Neurologist 2025; 30:93-101. [PMID: 40035203 PMCID: PMC11864048 DOI: 10.1097/nrl.0000000000000599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
OBJECTIVES The evaluation of the prognosis of patients with acute ischemic stroke (AIS) is of great significance in clinical practice. We aim to evaluate the feasibility and effectiveness of diffusion-weighted imaging (DWI) image-based radiomics features and machine learning methods in predicting 90-day prognosis among patients with AIS. PATIENTS AND METHODS We enrolled a total of 171 patients with AIS in this study, including 134 patients with a good prognosis and 37 patients with a poor prognosis, and collected the patients' clinical and DWI image data. Radiomics features from manually sketched ischemic lesions were extracted using the Pyradiomics package of Python, and the best radiomics features were selected by a t test and the least absolute shrinkage and selection operator. The radiomics model and clinical model were constructed using support vector machine and logistic regression, respectively, and the predictive performance of each model was evaluated. RESULTS We selected 9 features from a total of 851 radiomics features to build the final radiomics model. For predicting the poor prognosis of patients with AIS, the area under the curves, accuracy, sensitivity and specificity of the clinical model, radiomics model in the training set and radiomics model in the testing set were 0.865, 0.930 and 0.906, 81.3%, 92.0% and 90.0%, 81.1%, 76.0% and 75.0%, and 81.3%, 97.0% and 95.0%, respectively. CONCLUSIONS DWI image-based radiomics features and machine learning methods can accurately predict the 90-day prognosis of patients with AIS, and the radiomics model is superior to the clinical model in predicting prognosis.
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Affiliation(s)
- Guirui Li
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning
| | - Yueling Zhang
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning
| | - Jian Tang
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning
| | - Shijian Chen
- Department of Neurology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Qianqian Liu
- Department of Neurology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Jian Zhang
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning
| | - Shengliang Shi
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning
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Nicol AJ, Lam SK, Ching JCF, Tam VCW, Teng X, Zhang J, Lee FKH, Wong KCW, Cai J, Lee SWY. A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma. LA RADIOLOGIA MEDICA 2025; 130:161-178. [PMID: 39570458 PMCID: PMC11870888 DOI: 10.1007/s11547-024-01901-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 10/05/2024] [Indexed: 11/22/2024]
Abstract
PURPOSE Oral mucositis (OM) is one of the most prevalent and crippling treatment-related toxicities experienced by nasopharyngeal carcinoma (NPC) patients receiving radiotherapy (RT), posing a tremendous adverse impact on quality of life. This multi-center study aimed to develop and externally validate a multi-omic prediction model for severe OM. METHODS Four hundred and sixty-four histologically confirmed NPC patients were retrospectively recruited from two public hospitals in Hong Kong. Model development was conducted on one institution (n = 363), and the other was reserved for external validation (n = 101). Severe OM was defined as the occurrence of CTCAE grade 3 or higher OM during RT. Two predictive models were constructed: 1) conventional clinical and DVH features and 2) a multi-omic approach including clinical, radiomic and dosiomic features. RESULTS The multi-omic model, consisting of chemotherapy status and radiomic and dosiomic features, outperformed the conventional model in internal and external validation, achieving AUC scores of 0.67 [95% CI: (0.61, 0.73)] and 0.65 [95% CI: (0.53, 0.77)], respectively, compared to the conventional model with 0.63 [95% CI: (0.56, 0.69)] and 0.56 [95% CI: (0.44, 0.67)], respectively. In multivariate analysis, only the multi-omic model signature was significantly correlated with severe OM in external validation (p = 0.017), demonstrating the independent predictive value of the multi-omic approach. CONCLUSION A multi-omic model with combined clinical, radiomic and dosiomic features achieved superior pre-treatment prediction of severe OM. Further exploration is warranted to facilitate improved clinical decision-making and enable more effective and personalized care for the prevention and management of OM in NPC patients.
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Affiliation(s)
- Alexander James Nicol
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China
| | - Sai-Kit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Jerry Chi Fung Ching
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China
| | - Victor Chi Wing Tam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China
| | - Francis Kar Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong, China
| | - Kenneth C W Wong
- Department of Clinical Oncology, Prince of Wales Hospital, Sha Tin, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, China
| | - Shara Wee Yee Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China.
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Yan M, Zhang Z, Tian J, Yu J, Dekker A, Ruysscher DD, Wee L, Zhao L. Whole lung radiomic features are associated with overall survival in patients with locally advanced non-small cell lung cancer treated with definitive radiotherapy. Radiat Oncol 2025; 20:9. [PMID: 39825409 PMCID: PMC11742218 DOI: 10.1186/s13014-025-02583-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 01/03/2025] [Indexed: 01/20/2025] Open
Abstract
BACKGROUND Several studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy. METHODS A total of 661 patients with LA-NSCLC treated with definitive radiotherapy in combination with chemotherapy were enrolled in this study, with 292 patients in the training set, 57 patients from the same hospital from January to December 2017 as an independent test set (test-set-1), 83 patients from a multi-institutional prospective clinical trial data set (RTOG0617) as test-set-2, and 229 patients from a Dutch radiotherapy center as test-set-3. Tumor-based radiomic features and whole lung-based radiomic features were extracted from primary tumor and whole lungs (excluding the primary tumor) delineations in planning CT images. Feature selection of radiomic features was done by the least absolute shrinkage (LASSO) method embedded with a Cox proportional hazards (CPH) model with 5-fold cross-internal validation, with 1000 bootstrap samples. Radiomics prognostic scores (RS) were calculated by CPH regression based on selected features. Three models based on a tumor RS, and a lung RS separately and their combinations were constructed. The Harrell concordance index (C-index) and calibration curves were used to evaluate the discrimination and calibration performance. Patients were stratified into high and low risk groups based on median RS, and a log-rank test was performed. RESULTS The discrimination ability of lung- and tumor-based radiomics model was similar in terms of C-index, 0.69 vs. 0.68 in training set, 0.68 vs. 0.66 in test-set-1, 0.61 vs. 0.62 in test-set-2, 0.65 vs. 0.64 in test-set-3. The combination of tumor- and lung-based radiomics model performed best, with C-index of 0.71 in training set, 0.70 in test-set-1, 0.69 in test-set-2, and 0.68 in test-set-3. The calibration curve showed good agreement between predicted values and actual values. Patients were well stratified in training set, test-set-1 and test-set-3. In test-set-2, it was only whole lung-based RS that could stratify patients well and tumor-based RS performed bad. CONCLUSION Lung- and tumor-based radiomic features have the power to predict OS in LA-NSCLC. The combination of tumor- and lung-based radiomic features can achieve optimal performance.
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Affiliation(s)
- Meng Yan
- Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Zhen Zhang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Jia Tian
- Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Jiaqi Yu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lujun Zhao
- Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
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Peng YJ, Liu X, Liu Y, Tang X, Zhao QP, Du Y. Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis. World J Gastroenterol 2024; 30:4044-4056. [PMID: 39351251 PMCID: PMC11439117 DOI: 10.3748/wjg.v30.i36.4044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/28/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications. However, most current studies predict the risk of esophageal variceal bleeding (EVB) based on image features at a single level, which results in incomplete data. Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis. AIM To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis. METHODS In this study, 208 patients with cirrhosis were retrospectively evaluated and randomly split into training (n = 145) and validation (n = 63) cohorts. Three areas were chosen as regions of interest for extraction of multi-organ radiomic features: The whole liver, whole spleen, and lower esophagus-gastric fundus region. In the training cohort, radiomic score (Rad-score) was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method. Independent clinical risk factors were selected using multivariate logistic regression analyses. The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model (RC model). The established models were validated using the validation cohort. RESULTS The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis. Ascites, portal vein thrombosis, and plasma prothrombin time were identified as independent clinical risk factors. The area under the receiver operating characteristic curve (AUC) values for the RC model, Rad-score (liver + spleen + esophagus), Rad-score (liver), Rad-score (spleen), Rad-score (esophagus), and clinical model in the training cohort were 0.951, 0.930, 0.801, 0.831, 0.864, and 0.727, respectively. The corresponding AUC values in the validation cohort were 0.930, 0.886, 0.763, 0.792, 0.857, and 0.692. CONCLUSION In patients with cirrhosis, combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.
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Affiliation(s)
- Yu-Jie Peng
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, The People’s Hospital of Chongqing Liang Jiang New Area, Chongqing 401121, China
| | - Xin Liu
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, The People’s Hospital of Chongqing Liang Jiang New Area, Chongqing 401121, China
| | - Ying Liu
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xue Tang
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Qi-Peng Zhao
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Yong Du
- Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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Yang YX, Li L, Wang G, Jiang X, Li H, Jia LC, Zhou G, Sun Y. Initial Experience of CT-Based Online Adaptive Radiotherapy for Nasopharyngeal Carcinoma With a Novel Integrated Platform: A Case Report. Cureus 2024; 16:e69262. [PMID: 39398669 PMCID: PMC11470842 DOI: 10.7759/cureus.69262] [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] [Accepted: 09/11/2024] [Indexed: 10/15/2024] Open
Abstract
Intensity-modulated radiation therapy (IMRT) improves tumor control and reduces long-term radiation-induced complications of patients with nasopharyngeal carcinoma (NPC), contingent upon accurate contouring and precise delivery of treatment plans. Online adaptive radiotherapy (ART) involves real-time treatment plan modification based on the variations in targets and organs at risk (OARs) to uphold treatment planning accuracy. This study describes the first reported case of fan beam computed tomography (FBCT)-guided online ART for NPC using a novel integrated platform. Online ART was performed at the 25th fraction in this case, as tumors and the patient's anatomy were observed to regress inter-fractionally, necessitating adjustments to the contours based on the anatomy of the day. Online ART plan optimized target volume coverage while reducing doses to OARs. Notably, online ART significantly improved radiotherapy efficiency. This patient achieved a clinical complete response 12 weeks post-treatment, with Epstein-Barr virus DNA levels reduced to 0 copies/ml. Currently, the patient is alive without evidence of high-grade toxicity or local recurrence at approximately 10 months post-treatment. This case confirms the feasibility and dosimetric benefit of online ART for NPC using a novel integrated platform. Further research is needed to confirm its clinical benefits.
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Affiliation(s)
- Yu-Xian Yang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, CHN
| | - Lin Li
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, CHN
| | - Gangyu Wang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, CHN
| | - Xiaobo Jiang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, CHN
| | - Hua Li
- Real-time Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, CHN
| | - Le-Cheng Jia
- Real-time Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, CHN
| | - Guanqun Zhou
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, CHN
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, CHN
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Kang H, Wang X, Sun Y, Li S, Sun X, Li F, Hou C, Lam SK, Zhang W, Zheng YP. Automatic Transcranial Sonography-Based Classification of Parkinson's Disease Using a Novel Dual-Channel CNXV2-DANet. Bioengineering (Basel) 2024; 11:889. [PMID: 39329631 PMCID: PMC11429106 DOI: 10.3390/bioengineering11090889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 08/29/2024] [Accepted: 08/29/2024] [Indexed: 09/28/2024] Open
Abstract
Transcranial sonography (TCS) has been introduced to assess hyper-echogenicity in the substantia nigra of the midbrain for Parkinson's disease (PD); however, its subjective and resource-demanding nature has impeded its widespread application. An AI-empowered TCS-based PD classification tool is greatly demanding, yet relevant research is severely scarce. Therefore, we proposed a novel dual-channel CNXV2-DANet for TCS-based PD classification using a large cohort. A total of 1176 TCS images from 588 subjects were retrospectively enrolled from Beijing Tiantan Hospital, encompassing both the left and right side of the midbrain for each subject. The entire dataset was divided into a training/validation/testing set at a ratio of 70%/15%/15%. Development of the proposed CNXV2-DANet was performed on the training set with comparisons between the single-channel and dual-channel input settings; model evaluation was conducted on the independent testing set. The proposed dual-channel CNXV2-DANet was compared against three state-of-the-art networks (ConvNeXtV2, ConvNeXt, Swin Transformer). The results demonstrated that both CNXV2-DANet and ConvNeXt V2 performed more superiorly under dual-channel inputs than the single-channel input. The dual-channel CNXV2-DANet outperformed the single-channel, achieving superior average metrics for accuracy (0.839 ± 0.028), precision (0.849 ± 0.014), recall (0.845 ± 0.043), F1-score (0.820 ± 0.038), and AUC (0.906 ± 0.013) compared with the single channel metrics for accuracy (0.784 ± 0.037), precision (0.817 ± 0.090), recall (0.748 ± 0.093), F1-score (0.773 ± 0.037), and AUC (0.861 ± 0.047). Furthermore, the dual-channel CNXV2-DANet outperformed all other networks (all p-values < 0.001). These findings suggest that the proposed dual-channel CNXV2-DANet may provide the community with an AI-empowered TCS-based tool for PD assessment.
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Affiliation(s)
- Hongyu Kang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinyi Wang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yu Sun
- Research Institute of Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shuai Li
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xin Sun
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Fangxian Li
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Chao Hou
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Sai-Kit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Research Institute of Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wei Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yong-Ping Zheng
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Research Institute of Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects. Int J Med Inform 2024; 188:105464. [PMID: 38728812 DOI: 10.1016/j.ijmedinf.2024.105464] [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: 10/15/2023] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Chen M, Wang K, Dohopolski M, Morgan H, Sher D, Wang J. TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy. ARXIV 2024:arXiv:2405.05674v2. [PMID: 38764596 PMCID: PMC11100917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
Background Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is of importance to optimize patient clinical benefit and treatment resources. Purpose The purpose of this study is to assess the feasibility of using a vision-transformer (ViT) based neural network to predict radiotherapy induced anatomic change of HNC patients. Methods We retrospectively included 121 HNC patients treated with definitive RT/CRT. We collected the planning CT (pCT), planned dose, CBCTs acquired at the initial treatment (CBCT01) and fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs for model construction and evaluation. A UNet-style ViT network was designed to learn the spatial correspondence and contextual information from embedded image patches of CT, dose, CBCT01, GTVp, and GTVn. The deformation vector field between CBCT01 and CBCT21 was estimated by the model as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn and patient body for volumetric change evaluation. We used data from 100 patients for training and validation, and the remaining 21 patients for testing. Image and volumetric similarity metrics including mean square error (MSE), structural similarity index (SSIM), dice coefficient, and average surface distance were used to measure the similarity between the target image and predicted CBCT. Results The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE and SSIM between the normalized predicted CBCT to CBCT21 are 0.009 and 0.933, while the average dice coefficient between body mask, GTVp mask, and GTVn mask are 0.972, 0.792, and 0.821 respectively. Conclusions The proposed method showed promising performance for predicting radiotherapy induced anatomic change, which has the potential to assist in the decision making of HNC Adaptive RT.
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Affiliation(s)
- Meixu Chen
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA
| | - Kai Wang
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA
- Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, MD, 21201, USA
| | - Michael Dohopolski
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA
| | - Howard Morgan
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA
- Department of Radiation Oncology, Central Arkansas Radiation Therapy Institute, Little Rock, AR, 72205, USA
| | - David Sher
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA
| | - Jing Wang
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, USA
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Wang CK, Wang TW, Lu CF, Wu YT, Hua MW. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. Diagnostics (Basel) 2024; 14:924. [PMID: 38732337 PMCID: PMC11082984 DOI: 10.3390/diagnostics14090924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.
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Affiliation(s)
- Chih-Keng Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ting-Wei Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Chia-Fung Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan;
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Man-Wei Hua
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
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Guo W, Li B, Xu W, Cheng C, Qiu C, Sam SK, Zhang J, Teng X, Meng L, Zheng X, Wang Y, Lou Z, Mao R, Lei H, Zhang Y, Zhou T, Li A, Cai J, Ge H. Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy. J Cancer Res Clin Oncol 2024; 150:39. [PMID: 38280037 PMCID: PMC10821966 DOI: 10.1007/s00432-023-05520-5] [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: 08/10/2023] [Accepted: 11/20/2023] [Indexed: 01/29/2024]
Abstract
OBJECTIVE This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). METHODS We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. RESULTS For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). CONCLUSION Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.
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Affiliation(s)
- Wei Guo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Wencai Xu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Chen Cheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Chengyu Qiu
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Sai-Kit Sam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lingguang Meng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Xiaoli Zheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Yuan Wang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Zhaoyang Lou
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Ronghu Mao
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Hongchang Lei
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Ta Zhou
- School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Aijia Li
- Zhengzhou University School of Medicine, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
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Murakami Y, Kawahara D, Soyano T, Kozuka T, Takahashi Y, Miyake K, Kashihara K, Kashihara T, Kamima T, Oguchi M, Murakami Y, Yoshioka Y, Nagata Y. Dosiomics for intensity-modulated radiotherapy in patients with prostate cancer: survival analysis stratified by baseline prostate-specific antigen and Gleason grade group in a 2-institutional retrospective study. Br J Radiol 2024; 97:142-149. [PMID: 38263831 PMCID: PMC11008500 DOI: 10.1093/bjr/tqad004] [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: 11/25/2022] [Revised: 05/25/2023] [Accepted: 10/12/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE This study evaluated the prognostic impact of the quality of dose distribution using dosiomics in patients with prostate cancer, stratified by pretreatment prostate-specific antigen (PSA) levels and Gleason grade (GG) group. METHODS A total of 721 patients (Japanese Foundation for Cancer Research [JFCR] cohort: N = 489 and Tokyo Radiation Oncology Clinic [TROC] cohort: N = 232) with localized prostate cancer treated by intensity-modulated radiation therapy were enrolled. Two predictive dosiomic features for biochemical recurrence (BCR) were selected and patients were divided into certain groups stratified by pretreatment PSA levels and GG. Freedom from biochemical failure (FFBF) was estimated using the Kaplan-Meier method based on each dosiomic feature and univariate discrimination was evaluated using the log-rank test. As an exploratory analysis, a dosiomics hazard (DH) score was developed and its prognostic power for BCR was examined. RESULTS The dosiomic feature extracted from planning target volume (PTV) significantly distinguished the high- and low-risk groups in patients with PSA levels >10 ng/mL (7-year FFBF: 86.7% vs 76.1%, P < .01), GG 4 (92.2% vs 76.9%, P < .01), and GG 5 (83.1% vs 77.8%, P = .04). The DH score showed significant association with BCR (hazard score: 2.04; 95% confidence interval: 1.38-3.01; P < .001). CONCLUSION The quality of planned dose distribution on PTV may affect the prognosis of patients with poor prognostic factors, such as PSA levels >10 ng/mL and higher GGs. ADVANCES IN KNOWLEDGE The effects of planned dose distribution on prognosis differ depending on the patient's clinical background.
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Affiliation(s)
- Yu Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
- Department of Physics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
| | - Takashi Soyano
- Department of Radiology, Japan Self-Defense Forces Central Hospital, 1-2-24 Ikejiri, Setagaya-ku, Tokyo 154-8532, Japan
| | - Takuyo Kozuka
- Department of Radiology, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yuka Takahashi
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Konatsu Miyake
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Kenichi Kashihara
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Tairo Kashihara
- Department of Radiation Oncology, National Cancer Center Hospital, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Tatsuya Kamima
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Masahiko Oguchi
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
| | - Yasuo Yoshioka
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
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Wang Z, Fang M, Zhang J, Tang L, Zhong L, Li H, Cao R, Zhao X, Liu S, Zhang R, Xie X, Mai H, Qiu S, Tian J, Dong D. Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review. IEEE Rev Biomed Eng 2024; 17:118-135. [PMID: 37097799 DOI: 10.1109/rbme.2023.3269776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.
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Yang KB, Lee J, Yang J. Multi-class semantic segmentation of breast tissues from MRI images using U-Net based on Haar wavelet pooling. Sci Rep 2023; 13:11704. [PMID: 37474633 PMCID: PMC10359288 DOI: 10.1038/s41598-023-38557-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/11/2023] [Indexed: 07/22/2023] Open
Abstract
MRI images used in breast cancer diagnosis are taken in a lying position and therefore are inappropriate for reconstructing the natural breast shape in a standing position. Some studies have proposed methods to present the breast shape in a standing position using an ordinary differential equation of the finite element method. However, it is difficult to obtain meaningful results because breast tissues have different elastic moduli. This study proposed a multi-class semantic segmentation method for breast tissues to reconstruct breast shapes using U-Net based on Haar wavelet pooling. First, a dataset was constructed by labeling the skin, fat, and fibro-glandular tissues and the background from MRI images taken in a lying position. Next, multi-class semantic segmentation was performed using U-Net based on Haar wavelet pooling to improve the segmentation accuracy for breast tissues. The U-Net effectively extracted breast tissue features while reducing image information loss in a subsampling stage using multiple sub-bands. In addition, the proposed network is robust to overfitting. The proposed network showed a mIOU of 87.48 for segmenting breast tissues. The proposed networks demonstrated high-accuracy segmentation for breast tissue with different elastic moduli to reconstruct the natural breast shape.
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Affiliation(s)
- Kwang Bin Yang
- Devision of Memory - Memory FAB Team 1, Samsung Electronics, 1 Samsungjeonja-ro, Hwaseong, Gyeonggi, 18448, Republic of Korea
| | - Jinwon Lee
- Department of Industrial and Management Engineering, Gangneung-Wonju National University, 150 Namwon-ro, Wonju, Gangwon, 26403, Republic of Korea
| | - Jeongsam Yang
- Department of Industrial Engineering, Ajou University, 206 Worldcup-ro, Suwon, Gyeonggi, 16499, Republic of Korea.
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Ger RB, Wei L, Naqa IE, Wang J. The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation. Semin Radiat Oncol 2023; 33:252-261. [PMID: 37331780 PMCID: PMC11214660 DOI: 10.1016/j.semradonc.2023.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.
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Affiliation(s)
- Rachel B Ger
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX..
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Teng X, Zhang J, Zhang X, Fan X, Zhou T, Huang YH, Wang L, Lee EYP, Yang R, Cai J. Noninvasive imaging signatures of HER2 and HR using ADC in invasive breast cancer: repeatability, reproducibility, and association with pathological complete response to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:77. [PMID: 37381020 DOI: 10.1186/s13058-023-01674-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND The immunohistochemical test (IHC) of HER2 and HR can provide prognostic information and treatment guidance for invasive breast cancer patients. We aimed to develop noninvasive image signatures ISHER2 and ISHR of HER2 and HR, respectively. We independently evaluate their repeatability, reproducibility, and association with pathological complete response (pCR) to neoadjuvant chemotherapy. METHODS Pre-treatment DWI, IHC receptor status HER2/HR, and pCR to neoadjuvant chemotherapy of 222 patients from the multi-institutional ACRIN 6698 trial were retrospectively collected. They were pre-separated for development, independent validation, and test-retest. 1316 image features were extracted from DWI-derived ADC maps within manual tumor segmentations. ISHER2 and ISHR were developed by RIDGE logistic regression using non-redundant and test-retest reproducible features relevant to IHC receptor status. We evaluated their association with pCR using area under receiver operating curve (AUC) and odds ratio (OR) after binarization. Their reproducibility was further evaluated using the test-retest set with intra-class coefficient of correlation (ICC). RESULTS A 5-feature ISHER2 targeting HER2 was developed (AUC = 0.70, 95% CI 0.59 to 0.82) and validated (AUC = 0.72, 95% CI 0.58 to 0.86) with high perturbation repeatability (ICC = 0.92) and test-retest reproducibility (ICC = 0.83). ISHR was developed using 5 features with higher association with HR during development (AUC = 0.75, 95% CI 0.66 to 0.84) and validation (AUC = 0.74, 95% CI 0.61 to 0.86) and similar repeatability (ICC = 0.91) and reproducibility (ICC = 0.82). Both image signatures showed significant associations with pCR with AUC of 0.65 (95% CI 0.50 to 0.80) for ISHER2 and 0.64 (95% CI 0.50 to 0.78) for ISHER2 in the validation cohort. Patients with high ISHER2 were more likely to achieve pCR to neoadjuvant chemotherapy with validation OR of 4.73 (95% CI 1.64 to 13.65, P value = 0.006). Low ISHR patients had higher pCR with OR = 0.29 (95% CI 0.10 to 0.81, P value = 0.021). Molecular subtypes derived from the image signatures showed comparable pCR prediction values to IHC-based molecular subtypes (P value > 0.05). CONCLUSION Robust ADC-based image signatures were developed and validated for noninvasive evaluation of IHC receptors HER2 and HR. We also confirmed their value in predicting treatment response to neoadjuvant chemotherapy. Further evaluations in treatment guidance are warranted to fully validate their potential as IHC surrogates.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Fan
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Lu Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Elaine Yuen Phin Lee
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Y920, Lee Shau Kee Building, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Hong Kong, China.
- Research Institute for Smart Aging, The Hong Kong Polytechnic University, Hong Kong, China.
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Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
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Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
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Wu P, Weng H, Luo W, Zhan Y, Xiong L, Zhang H, Yan H. An improved Yolov5s based on transformer backbone network for detection and classification of bronchoalveolar lavage cells. Comput Struct Biotechnol J 2023; 21:2985-3001. [PMID: 37249972 PMCID: PMC10209489 DOI: 10.1016/j.csbj.2023.05.008] [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: 10/28/2022] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 05/31/2023] Open
Abstract
Biological tissue information of the lung, such as cells and proteins, can be obtained from bronchoalveolar lavage fluid (BALF), through which it can be used as a complement to lung biopsy pathology. BALF cells can be confused with each other due to the similarity of their characteristics and differences in the way sections are handled or viewed. This poses a great challenge for cell detection. In this paper, An Improved Yolov5s Based on Transformer Backbone Network for Detection and Classification of BALF Cells is proposed, focusing on the detection of four types of cells in BALF: macrophages, lymphocytes, neutrophils and eosinophils. The network is mainly based on the Yolov5s network and uses Swin Transformer V2 technology in the backbone network to improve cell detection accuracy by obtaining global information; the C3Ghost module (a variant of the Convolutional Neural Network architecture) is used in the neck network to reduce the number of parameters during feature channel fusion and to improve feature expression performance. In addition, embedding intersection over union Loss (EIoU_Loss) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher accuracy of the algorithm. The experiments showed that our model could achieve mAP of 81.29% and Recall of 80.47%. Compared to the original Yolov5s, the mAP has improved by 3.3% and Recall by 3.67%. We also compared it with Yolov7 and the newly launched Yolov8s. mAP improved by 0.02% and 2.36% over Yolov7 and Yolov8s respectively, while the FPS of our model was higher than both of them, achieving a balance of efficiency and accuracy, further demonstrating the superiority of our model.
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Affiliation(s)
- Puzhen Wu
- The Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
- Beijing-Dublin International College, Beijing University of Technology, Beijing 100124, China
| | - Han Weng
- Beijing-Dublin International College, Beijing University of Technology, Beijing 100124, China
| | - Wenting Luo
- Department of Pathophysiology, Medical College, Nanchang University, 461 Bayi Road, Nanchang 330006, China
| | - Yi Zhan
- Beijing-Dublin International College, Beijing University of Technology, Beijing 100124, China
| | - Lixia Xiong
- Department of Pathophysiology, Medical College, Nanchang University, 461 Bayi Road, Nanchang 330006, China
| | - Hongyan Zhang
- Department of Burn, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Road, Nanschang 330066, China
| | - Hai Yan
- The Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
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20
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Dong Y, Zhang J, Lam S, Zhang X, Liu A, Teng X, Han X, Cao J, Li H, Lee FK, Yip CW, Au K, Zhang Y, Cai J. Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy. Cancers (Basel) 2023; 15:cancers15072032. [PMID: 37046693 PMCID: PMC10093711 DOI: 10.3390/cancers15072032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/21/2023] [Accepted: 03/26/2023] [Indexed: 04/14/2023] Open
Abstract
(1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction.
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Affiliation(s)
- Yanjing Dong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Saikt Lam
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinyu Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Anran Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jin Cao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hongxiang Li
- Department of Radiology, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou 350000, China
| | - Francis Karho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Celia Waiyi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Kwokhung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong 226000, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518000, China
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Yan D, Yin X, Wang L, Huang L, Tang Q, Cheng K, Yan S. Induction chemotherapy reduces target volume drift in patients with locoregionally advanced nasopharyngeal carcinoma undergoing adaptive intensity-modulated radiotherapy: a retrospective cohort study. Quant Imaging Med Surg 2023; 13:1779-1791. [PMID: 36915305 PMCID: PMC10006138 DOI: 10.21037/qims-22-776] [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: 08/09/2022] [Accepted: 01/19/2023] [Indexed: 02/23/2023]
Abstract
Background Adaptive radiotherapy (ART) provides real-time correction of the target and dose of radiation based on repeat computed tomography (CT) imaging and replanning during intensity-modulated radiation therapy (IMRT) and is important for locoregionally advanced nasopharyngeal carcinoma (NPC). However, repeat CT imaging and replanning are time-consuming and hinder the broader application of ART. The optimum dose and frequency of replanning time have been published in previous reports. The purpose of this study was to determine whether induction chemotherapy (IC) reduces target volume drift during IMRT, potentially reducing the replanning workload. Methods From January 2012 to December 2017, 40 patients with locoregionally advanced, nonmetastatic stage III-IVa NPC treated in the Department of Radiation Oncology in the First Affiliated Hospital, College of Medicine, Zhejiang University, were enrolled into this study. Of the 40 patients, 20 received 2-3 cycles of IC before concurrent chemoradiotherapy (IC + CCRT), and the other 20 patients were treated with CCRT plus adjuvant chemotherapy (CCRT + AC). During CCRT, all patients underwent weekly simulated CT for 6 weeks. The gross tumor volume (GTV), clinical target volume (CTV), and body weight were measured weekly and compared between the 2 groups. Results Compared with the baseline, the mean weight loss after 25 fractions was 7.0 kg (13.6%; range, 3.9-25.5%) in the CCRT + AC group and 5.7 kg (8.3%; range, 3.6-20%) in the IC + CCRT group. The mean GTV and CTV decreased by 16.55 mL (15.7%; range, 6.1-33.7%) and 61.25 mL (9.33%; range, 4.4-17.0%), respectively, in the IC + CCRT group, and by 39.86 mL (38.79%; range, 25.3-50.7%) and 87.72 mL (12.7%; range, 6.7-22.9%), respectively, in the CCRT + AC group. The degree of weekly reduction in the GTV of the IC + CCRT group was not significantly higher than that of the CCRT + AC group, with the following P values of each percentage reduction in comparison with the previous week over 5 weeks, respectively: P<0.001, P=0.015, P=0.01, P=0.01, and P<0.001. The weekly CTV reduction only significantly correlated with weight loss (P=0.005) in the IC + CCRT group. Conclusions IC significantly decreased the degree of weight loss, GTV shrinkage, and CTV reduction during CCRT, consequently decreasing the anatomical and target dose drift during the adaptive replanning of IMRT. This may lead to a reduction in the recurrence of locoregionally advanced NPC, especially among patients with large metastatic cervical lymph nodes, potentially improving survival. This result provides favorable evidence that IC improves locoregional recurrence-free survival (LRFS) and overall survival (OS) in patients with locoregionally advanced NPC.
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Affiliation(s)
- Danfang Yan
- Department of Radiation Oncology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xin Yin
- Department of Radiation Oncology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Lihong Wang
- Department of Radiation Oncology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Liming Huang
- Department of Chemotherapy, People's Hospital of Fujian Province, Fuzhou, China
| | - Qiuying Tang
- Department of Radiation Oncology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kejia Cheng
- Department of Otolaryngology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Senxiang Yan
- Department of Radiation Oncology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Zhang J, Lam SK, Teng X, Ma Z, Han X, Zhang Y, Cheung ALY, Chau TC, Ng SCY, Lee FKH, Au KH, Yip CWY, Lee VHF, Han Y, Cai J. Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients. Radiother Oncol 2023; 183:109578. [PMID: 36822357 DOI: 10.1016/j.radonc.2023.109578] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 02/09/2023] [Accepted: 02/17/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND AND PURPOSE To investigate the radiomic feature (RF) repeatability via perturbation and its impact on cross-institutional prognostic model generalizability in Nasopharyngeal Carcinoma (NPC) patients. MATERIALS AND METHODS 286 and 183 NPC patients from two institutions were included for model training and validation. Perturbations with random translations and rotations were applied to contrast-enhanced T1-weighted (CET1-w) MR images. RFs were extracted from primary tumor volume under a wide range of image filtering and discretization settings. RF repeatability was assessed by intraclass correlation coefficient (ICC), which was used to equally separate the RFs into low- and high-repeatable groups by the median value. After feature selection, multivariate Cox regression and Kaplan-Meier analysis were independently employed to develop and analyze prognostic models. Concordance index (C-index) and P-value from log-rank test were used to assess model performance. RESULTS Most textural RFs from high-pass wavelet-filtered images were susceptible to image perturbations. It was more prominent when a smaller discretization bin number was used (e.g., 8, mean ICC = 0.69). Using high-repeatable RFs for model development yielded a significantly higher C-index (0.63) in the validation cohort than when only low-repeatable RFs were used (0.57, P = 0.024), suggesting higher model generalizability. Besides, significant risk stratification in the validation cohort was observed only when high-repeatable RFs were used (P < 0.001). CONCLUSION Repeatability of RFs from high-pass wavelet-filtered CET1-w MR images of primary NPC tumor was poor, particularly when a smaller bin number was used. Exclusive use of high-repeatable RFs is suggested to safeguard model generalizability for wide-spreading clinical utilization.
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Affiliation(s)
- Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Sai-Kit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China
| | - Andy Lai-Yin Cheung
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Tin-Ching Chau
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Sherry Chor-Yi Ng
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
| | - Kwok-Hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Ying Han
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China.
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Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy. Cancers (Basel) 2023; 15:cancers15041105. [PMID: 36831445 PMCID: PMC9954441 DOI: 10.3390/cancers15041105] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.
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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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Li B, Ren G, Guo W, Zhang J, Lam SK, Zheng X, Teng X, Wang Y, Yang Y, Dan Q, Meng L, Ma Z, Cheng C, Tao H, Lei H, Cai J, Ge H. Function-Wise Dual-Omics analysis for radiation pneumonitis prediction in lung cancer patients. Front Pharmacol 2022; 13:971849. [PMID: 36199694 PMCID: PMC9528994 DOI: 10.3389/fphar.2022.971849] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/04/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose: This study investigates the impact of lung function on radiation pneumonitis prediction using a dual-omics analysis method. Methods: We retrospectively collected data of 126 stage III lung cancer patients treated with chemo-radiotherapy using intensity-modulated radiotherapy, including pre-treatment planning CT images, radiotherapy dose distribution, and contours of organs and structures. Lung perfusion functional images were generated using a previously developed deep learning method. The whole lung (WL) volume was divided into function-wise lung (FWL) regions based on the lung perfusion functional images. A total of 5,474 radiomics features and 213 dose features (including dosiomics features and dose-volume histogram factors) were extracted from the FWL and WL regions, respectively. The radiomics features (R), dose features (D), and combined dual-omics features (RD) were used for the analysis in each lung region of WL and FWL, labeled as WL-R, WL-D, WL-RD, FWL-R, FWL-D, and FWL-RD. The feature selection was carried out using ANOVA, followed by a statistical F-test and Pearson correlation test. Thirty times train-test splits were used to evaluate the predictability of each group. The overall average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and f1-score were calculated to assess the performance of each group. Results: The FWL-RD achieved a significantly higher average AUC than the WL-RD group in the training (FWL-RD: 0.927 ± 0.031, WL-RD: 0.849 ± 0.064) and testing cohorts (FWL-RD: 0.885 ± 0.028, WL-RD: 0.762 ± 0.053, p < 0.001). When using radiomics features only, the FWL-R group yielded a better classification result than the model trained with WL-R features in the training (FWL-R: 0.919 ± 0.036, WL-R: 0.820 ± 0.052) and testing cohorts (FWL-R: 0.862 ± 0.028, WL-R: 0.750 ± 0.057, p < 0.001). The FWL-D group obtained an average AUC of 0.782 ± 0.032, obtaining a better classification performance than the WL-D feature-based model of 0.740 ± 0.028 in the training cohort, while no significant difference was observed in the testing cohort (FWL-D: 0.725 ± 0.064, WL-D: 0.710 ± 0.068, p = 0.54). Conclusion: The dual-omics features from different lung functional regions can improve the prediction of radiation pneumonitis for lung cancer patients under IMRT treatment. This function-wise dual-omics analysis method holds great promise to improve the prediction of radiation pneumonitis for lung cancer patients.
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Affiliation(s)
- Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wei Guo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xiaoli Zheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yunhan Wang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Yang Yang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Qinfu Dan
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Lingguang Meng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chen Cheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Hongyan Tao
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Hongchang Lei
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- *Correspondence: Hong Ge, ; Jing Cai,
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
- *Correspondence: Hong Ge, ; Jing Cai,
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26
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Lee W, Lam SK, Zhang Y, Yang R, Cai J. Review of methodological workflow, interpretation and limitations of nomogram application in cancer study. RADIATION MEDICINE AND PROTECTION 2022. [DOI: 10.1016/j.radmp.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022] Open
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Wong KL, Cheng KH, Lam SK, Liu C, Cai J. Review of functional magnetic resonance imaging in the assessment of nasopharyngeal carcinoma treatment response. PRECISION RADIATION ONCOLOGY 2022. [DOI: 10.1002/pro6.1161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Kwun Lam Wong
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
- Department of Radiotherapy Hong Kong Sanatorium & Hospital HKSH Medical Group Hong Kong SAR People's Republic of China
| | - Ka Hei Cheng
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
| | - Sai Kit Lam
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
| | - Chenyang Liu
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
| | - Jing Cai
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR People's Republic of China
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