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Bi S, Yuan Q, Dai Z, Sun X, Wan Sohaimi WFB, Bin Yusoff AL. Advances in CT-based lung function imaging for thoracic radiotherapy. Front Oncol 2024; 14:1414337. [PMID: 39286020 PMCID: PMC11403405 DOI: 10.3389/fonc.2024.1414337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 08/14/2024] [Indexed: 09/19/2024] Open
Abstract
The objective of this review is to examine the potential benefits and challenges of CT-based lung function imaging in radiotherapy over recent decades. This includes reviewing background information, defining related concepts, classifying and reviewing existing studies, and proposing directions for further investigation. The lung function imaging techniques reviewed herein encompass CT-based methods, specifically utilizing phase-resolved four-dimensional CT (4D-CT) or end-inspiratory and end-expiratory CT scans, to delineate distinct functional regions within the lungs. These methods extract crucial functional parameters, including lung volume and ventilation distribution, pivotal for assessing and characterizing the functional capacity of the lungs. CT-based lung ventilation imaging offers numerous advantages, notably in the realm of thoracic radiotherapy. By utilizing routine CT scans, additional radiation exposure and financial burdens on patients can be avoided. This imaging technique also enables the identification of different functional areas of the lung, which is crucial for minimizing radiation exposure to healthy lung tissue and predicting and detecting lung injury during treatment. In conclusion, CT-based lung function imaging holds significant promise for improving the effectiveness and safety of thoracic radiotherapy. Nevertheless, challenges persist, necessitating further research to address limitations and optimize clinical utilization. Overall, this review highlights the importance of CT-based lung function imaging as a valuable tool in radiotherapy planning and lung injury monitoring.
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Affiliation(s)
- Suyan Bi
- School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia
| | - Qingqing Yuan
- National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zhitao Dai
- National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Xingru Sun
- Huizhou Third People's Hospital, Guangzhou Medical University, Huizhou, Guangdong, China
| | - Wan Fatihah Binti Wan Sohaimi
- Department of Nuclear Medicine Radiotherapy and Oncology, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia
| | - Ahmad Lutfi Bin Yusoff
- Department of Nuclear Medicine Radiotherapy and Oncology, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia
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Hou Z, Kong Y, Wu J, Gu J, Liu J, Gao S, Yin Y, Zhang L, Han Y, Zhu J, Li S. A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning. Jpn J Radiol 2024; 42:765-776. [PMID: 38536558 DOI: 10.1007/s11604-024-01550-2] [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: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 07/03/2024]
Abstract
PURPOSE Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning. MATERIALS AND METHODS Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model. RESULTS CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05). CONCLUSION Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.
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Affiliation(s)
- Zhen Hou
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Youyong Kong
- School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China
- Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China
- Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France
| | - Junxian Wu
- School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China
| | - Jiabing Gu
- School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China
| | - Juan Liu
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Shanbao Gao
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Yicai Yin
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Ling Zhang
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Yongchao Han
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Jian Zhu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China.
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China.
- Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China.
- Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France.
| | - Shuangshuang Li
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China.
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Li Y, Xie Y, Xu Y, Zhang N, Li G, Ju S. A new scheme of global feature management improved the performance and stability of radiomics model: a study based on CT images of acute brainstem infarction. Eur Radiol 2022; 32:5508-5516. [PMID: 35267092 DOI: 10.1007/s00330-022-08659-w] [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: 10/11/2021] [Revised: 01/28/2022] [Accepted: 02/12/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The performance and stability of radiomics model caused by dimension reduction remain being confronted with major challenges. In this study, we aimed to propose a new scheme of global feature management independent of dimension reduction to improve it. METHODS The non-contrast computed tomography (NCCT) images of acute brainstem infarction (ABI) from two medical centers were used as test and validation sets. A new scheme was constructed based on global feature management, and the traditional scheme dependent on dimension reduction was used as control. The radiomic features of NCCT images were extracted in Matlab R2013a. The performance of prediction model was evaluated by the generalized linear model (GLM) and multivariate logistic regression. And, the stability of radiomics model was evaluated with the difference of area under curve (AUC) between the test and validation sets. RESULTS Compared with the traditional scheme, the new scheme presented a similar detection performance (AUC: 0.875 vs. 0.883), yet a better performance in predicting prognosis (AUC: 0.864, OR = 0.917, p = 0.021 vs. AUC:0.806, OR = 0.972, p = 0.007). All these results were well verified in an independent validation set. Moreover, the new scheme showed stronger stability in both the detection model (ΔAUC: 0.013 vs. 0.039) and prediction model (ΔAUC = 0.004 vs. 0.044). CONCLUSION Although there might be several limitations, this study proved that the scheme of global feature management independent of dimension reduction could be a powerful supplement to the radiomics methodology. KEY POINTS • The new scheme (Swavelet) presented similar detection performances for ABI with the traditional scheme. • A better predictive performance for END was found in the new scheme (Swavelet) compared with the traditional scheme. • Stronger model stability was found in both the detection and prediction models based on the new scheme.
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Affiliation(s)
- Yuefeng Li
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China.,Department of Psychiatry, Zhenjiang Mental Health Center, Zhenjiang, China
| | - Yuhang Xie
- Neuroimaging Laboratory, Medical College of Jiangsu University, Zhenjiang, China
| | - Yuhao Xu
- Neuroimaging Laboratory, Medical College of Jiangsu University, Zhenjiang, China
| | - Ningning Zhang
- Neuroimaging Laboratory, Medical College of Jiangsu University, Zhenjiang, China
| | - Guohai Li
- Department of Psychiatry, Zhenjiang Mental Health Center, Zhenjiang, China.
| | - Shenghong Ju
- Department of Radiology, Zhongda Affiliated Hospital of Southeast University, Nanjing, China.
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