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Liu C, Liu H, Li Y, Xiao Z, Wang Y, Guo H, Luo J. Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia. Sci Rep 2024; 14:12589. [PMID: 38824238 PMCID: PMC11144207 DOI: 10.1038/s41598-024-63251-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.
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Affiliation(s)
- Chunmei Liu
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Huizhi Liu
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Yange Li
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Zhiqing Xiao
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Yanqiang Wang
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Han Guo
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Jianmin Luo
- Department of Hematology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China.
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Midroni J, Salunkhe R, Liu Z, Chow R, Boldt G, Palma D, Hoover D, Vinogradskiy Y, Raman S. Incorporation of Functional Lung Imaging Into Radiation Therapy Planning in Patients With Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00481-4. [PMID: 38631538 DOI: 10.1016/j.ijrobp.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
Our purpose was to provide an understanding of current functional lung imaging (FLI) techniques and their potential to improve dosimetry and outcomes for patients with lung cancer receiving radiation therapy (RT). Excerpta Medica dataBASE (EMBASE), PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, or quantification of RT-related outcomes for patients with lung cancer. Studies involving all RT modalities, including stereotactic body RT and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomic and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP). One hundred seventy-eight studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more, 3.52% to the fraction of well-perfused lung receiving 20 Gy or more, 1.3 Gy to the mean dose to the well-ventilated lung, and 2.41 Gy to the mean dose to the well-perfused lung. Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity modulated RT and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which is reduced compared with results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information but was not statistically significant. FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the effect of FL avoidance planning on toxicity reduction and patient outcomes.
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Affiliation(s)
- Julie Midroni
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Rohan Salunkhe
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zhihui Liu
- Biostatistics, Princess Margaret Cancer Center, Toronto, Canada
| | - Ronald Chow
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - David Palma
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada; Ontario Institute for Cancer Research, Toronto, Canada
| | - Douglas Hoover
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, United States of America; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, United States of America
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Ma ZP, Li XL, Gao K, Zhang TL, Wang HD, Zhao YX. Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1333-1340. [PMID: 37840466 DOI: 10.3233/xst-230189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
OBJECTIVE To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC). METHODS 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness. RESULTS LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05). CONCLUSION The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.
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Affiliation(s)
- Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Xiao-Lei Li
- Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang City, Hebei Province, China
| | - Kai Gao
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Tian-Le Zhang
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Heng-Di Wang
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Yong-Xia Zhao
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
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Huber RM, Kauffmann-Guerrero D, Hoffmann H, Flentje M. New developments in locally advanced nonsmall cell lung cancer. Eur Respir Rev 2021; 30:30/160/200227. [PMID: 33952600 DOI: 10.1183/16000617.0227-2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
Locally advanced nonsmall cell lung cancer, due to its varying prognosis, is grouped according to TNM stage IIIA, IIIB and IIIC. Developments over the last 3 years have been focused on the integration of immunotherapy into the combination treatment of a locally definitive therapy (surgery or radiotherapy) and chemotherapy. For concurrent chemoradiotherapy, consolidation therapy with durvalumab was established. Adjuvant targeted therapy has again gained increasing interest. In order to adapt treatment to the specific stage subgroup and its prognosis, fluorodeoxyglucose positron emission tomography/computed tomography and pathological evaluation of the mediastinum are important. Tumours should be investigated for immunological features and driver mutations. Regarding toxicity, evaluation of pulmonary and cardiac function, as well as symptoms and quality of life, is of increasing importance. To improve the management and prognosis of this heterogeneous entity, clinical trials and registries should take these factors into account.
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Affiliation(s)
- Rudolf M Huber
- Division of Respiratory Medicine and Thoracic Oncology, Dept of Medicine, University of Munich - Campus Innenstadt, Comprehensive Pneumology Center Munich (CPC-M) and Thoracic Oncology Centre Munich, Munich, Germany .,Member of the German Centre of Lung Research
| | - Diego Kauffmann-Guerrero
- Division of Respiratory Medicine and Thoracic Oncology, Dept of Medicine, University of Munich - Campus Innenstadt, Comprehensive Pneumology Center Munich (CPC-M) and Thoracic Oncology Centre Munich, Munich, Germany.,Member of the German Centre of Lung Research
| | - Hans Hoffmann
- Division of Thoracic Surgery, Technical University of Munich, Munich, Germany
| | - Michael Flentje
- Dept of Radiation Oncology and Palliative Medicine, University of Würzburg, Würzburg, Germany
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