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Cheng KH, Li W, Lee FKH, Li T, Cai J. Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement in MRI Imaging. Cancers (Basel) 2024; 16:999. [PMID: 38473363 DOI: 10.3390/cancers16050999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667 ± 0.006 for Li's model; 0.0761 ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging.
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Affiliation(s)
- Ka-Hei Cheng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518000, China
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Teng X, Zhang J, Han X, Sun J, Lam SK, Ai QYH, Ma Z, Lee FKH, Au KH, Yip CWY, Chow JCH, Lee VHF, Cai J. Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma. Radiol Med 2023:10.1007/s11547-023-01650-5. [PMID: 37300736 DOI: 10.1007/s11547-023-01650-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE This study aimed to discover intra-tumor heterogeneity signature and validate its predictive value for adjuvant chemotherapy (ACT) following concurrent chemoradiotherapy (CCRT) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). MATERIALS AND METHODS 397 LA-NPC patients were retrospectively enrolled. Pre-treatment contrast-enhanced T1-weighted (CET1-w) MR images, clinical variables, and follow-up were retrospectively collected. We identified single predictive radiomic feature from primary gross tumor volume (GTVnp) and defined predicted subvolume by calculating voxel-wised feature mapping and within GTVnp. We independently validate predictive value of identified feature and associated predicted subvolume. RESULTS Only one radiomic feature, gldm_DependenceVariance in 3 mm-sigma LoG-filtered image, was discovered as a signature. In the high-risk group determined by the signature, patients received CCRT + ACT achieved 3-year disease free survival (DFS) rate of 90% versus 57% (HR, 0.20; 95%CI, 0.05-0.94; P = 0.007) for CCRT alone. The multivariate analysis showed patients receiving CCRT + ACT had a HR of 0.21 (95%CI: 0.06-0.68, P = 0.009) for DFS compared to those receiving CCRT alone. The predictive value can also be generalized to the subvolume with multivariate HR of 0.27 (P = 0.017) for DFS. CONCLUSION The signature with its heterogeneity mapping could be a reliable and explainable ACT decision-making tool in clinical practice.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Jiachen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Sai-Kit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Qi-Yong Hemis Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong SAR, China
| | - Kwok-Hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong SAR, China
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong SAR, China
| | - James Chung Hang Chow
- Department of Clinical Oncology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong SAR, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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