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Sun Y, Liang F, Yang J, Liu Y, Shen Z, Zhou C, Xia Y. Pilot study: radiomic analysis for predicting treatment response to whole-brain radiotherapy combined temozolomide in lung cancer brain metastases. Front Oncol 2024; 14:1395313. [PMID: 39193384 PMCID: PMC11347322 DOI: 10.3389/fonc.2024.1395313] [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: 03/03/2024] [Accepted: 07/23/2024] [Indexed: 08/29/2024] Open
Abstract
Objective The objective of this study is to assess the viability of utilizing radiomics for predicting the treatment response of lung cancer brain metastases (LCBM) to whole-brain radiotherapy (WBRT) combined with temozolomide (TMZ). Methods Fifty-three patients diagnosed with LCBM and undergoing WBRT combined with TMZ were enrolled. Patients were divided into responsive and non-responsive groups based on the RANO-BM criteria. Radiomic features were extracted from contrast-enhanced the whole brain tissue CT images. Feature selection was performed using t-tests, Pearson correlation coefficients, and Least Absolute Shrinkage And Selection (LASSO) regression. Logistic regression was employed to construct the radiomics model, which was then integrated with clinical data to develop the nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves, and clinical utility was assessed using decision curve analysis (DCA). Results A total of 1834 radiomic features were extracted from each patient's images, and 3 features with predictive value were selected. Both the radiomics and nomogram models exhibited satisfactory predictive performance and clinical utility, with the nomogram model demonstrating superior predictive value. The ROC analysis revealed that the AUC of the radiomics model in the training and testing sets were 0.776 and 0.767, respectively, while the AUC of the nomogram model were 0.799 and 0.833, respectively. DCA curves demonstrated that both models provided benefits to patients across various thresholds. Conclusion Radiomic-defined image biomarkers can effectively predict the treatment response of WBRT combined with TMZ in patients with LCBM, offering potential to optimize treatment decisions for this condition.
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Affiliation(s)
- Yichu Sun
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Fei Liang
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Jing Yang
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Yong Liu
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Ziqiang Shen
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Chong Zhou
- Department of Radiation Oncology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China
| | - Youyou Xia
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
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Ghaderi S, Mohammadi S, Mohammadi M, Pashaki ZNA, Heidari M, Khatyal R, Zafari R. A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects. J Med Radiat Sci 2024; 71:269-289. [PMID: 38234262 PMCID: PMC11177032 DOI: 10.1002/jmrs.756] [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: 08/15/2023] [Accepted: 01/06/2024] [Indexed: 01/19/2024] Open
Abstract
INTRODUCTION Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. METHODS This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. RESULTS Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. CONCLUSION Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
| | - Sana Mohammadi
- Department of Medical Sciences, School of MedicineIran University of Medical SciencesTehranIran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran
| | | | - Mehrsa Heidari
- Department of Medical Science, School of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Rahim Khatyal
- Department of Radiology, Faculty of Allied Medical SciencesTabriz University of Medical SciencesTabrizIran
| | - Rasa Zafari
- School of MedicineTehran University of Medical SciencesTehranIran
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Kong C, Yin X, Zou J, Ma C, Liu K. The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung. BMC Cancer 2024; 24:454. [PMID: 38605303 PMCID: PMC11010275 DOI: 10.1186/s12885-024-12158-0] [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: 01/11/2024] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
OBJECTIVE To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma. METHODS Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness. RESULTS In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group). CONCLUSIONS Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.
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Affiliation(s)
- Chao Kong
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Xiaoyan Yin
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Jingmin Zou
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Changsheng Ma
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China.
| | - Kai Liu
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, 830000, Urumqi, China.
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Wen J, Yu JZ, Liu C, Ould Ismail AAO, Ma W. Exploring the Molecular Tumor Microenvironment and Translational Biomarkers in Brain Metastases of Non-Small-Cell Lung Cancer. Int J Mol Sci 2024; 25:2044. [PMID: 38396722 PMCID: PMC10889194 DOI: 10.3390/ijms25042044] [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: 12/05/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Brain metastases represent a significant clinical challenge in the treatment of non-small-cell lung cancer (NSCLC), often leading to a severe decline in patient prognosis and survival. Recent advances in imaging and systemic treatments have increased the detection rates of brain metastases, yet clinical outcomes remain dismal due to the complexity of the metastatic tumor microenvironment (TME) and the lack of specific biomarkers for early detection and targeted therapy. The intricate interplay between NSCLC tumor cells and the surrounding TME in brain metastases is pivotal, influencing tumor progression, immune evasion, and response to therapy. This underscores the necessity for a deeper understanding of the molecular underpinnings of brain metastases, tumor microenvironment, and the identification of actionable biomarkers that can inform multimodal treatment approaches. The goal of this review is to synthesize current insights into the TME and elucidate molecular mechanisms in NSCLC brain metastases. Furthermore, we will explore the promising horizon of emerging biomarkers, both tissue- and liquid-based, that hold the potential to radically transform the treatment strategies and the enhancement of patient outcomes.
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Affiliation(s)
- Jiexi Wen
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Jie-Zeng Yu
- Division of Hematology/Oncology, Department of Medicine, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Catherine Liu
- School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - A. Aziz O. Ould Ismail
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Weijie Ma
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
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Gong J, Wang T, Wang Z, Chu X, Hu T, Li M, Peng W, Feng F, Tong T, Gu Y. Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model. Cancer Imaging 2024; 24:1. [PMID: 38167564 PMCID: PMC10759676 DOI: 10.1186/s40644-023-00623-1] [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: 08/01/2023] [Accepted: 10/16/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.
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Affiliation(s)
- Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Zezhou Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Shanghai Municipal Hospital Oncological Specialist Alliance, Shanghai, 200032, China
| | - Xiao Chu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Tingdan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Feng Feng
- Department of Medical Imaging, Nantong Tumor Hospital, Nantong University, Nantong, 226361, China.
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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Pan F, Feng L, Liu B, Hu Y, Wang Q. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol 2023; 14:1295511. [PMID: 38027000 PMCID: PMC10646419 DOI: 10.3389/fphar.2023.1295511] [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: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.
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Affiliation(s)
- Feng Pan
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
- Department of CT, Jilin Province FAW General Hospital, Changchun, China
| | - Li Feng
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baocai Liu
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Hu
- Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
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