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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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Wu J, Zhou Y, Xu C, Yang C, Liu B, Zhao L, Song J, Wang W, Yang Y, Liu N. Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer. BMC Cancer 2024; 24:170. [PMID: 38310283 PMCID: PMC10838455 DOI: 10.1186/s12885-024-11862-1] [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: 10/23/2023] [Accepted: 01/09/2024] [Indexed: 02/05/2024] Open
Abstract
BACKGROUND The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). METHODS Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis. RESULTS A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). CONCLUSION Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.
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Affiliation(s)
- Jiehan Wu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
- Langfang Health Vocational College, Siguang Road, Guangyang District, Langfang, 065000, Hebei, China
| | - Yuntao Zhou
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Chang Xu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Chengwen Yang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Bingxin Liu
- College of Arts and Sciences, Lehigh University, 27 Memorial Drive West, Bethlehem, PA, 18015, USA
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Jiawei Song
- Department of Oncology, the People's Hospital of Ganyu District, Lianyungang, 222100, China
| | - Wei Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Yining Yang
- The Department of Radiotherapy, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Ningbo Liu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
- Hetian District People's Hospital, Hetian, 848000, Xinjiang, China.
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Zeng H, Tohidinezhad F, De Ruysscher DKM, Willems YCP, Degens JHRJ, van Kampen-van den Boogaart VEM, Pitz C, Cortiula F, Brandts L, Hendriks LEL, Traverso A. The Association of Gross Tumor Volume and Its Radiomics Features with Brain Metastases Development in Patients with Radically Treated Stage III Non-Small Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15113010. [PMID: 37296973 DOI: 10.3390/cancers15113010] [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: 05/10/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE To identify clinical risk factors, including gross tumor volume (GTV) and radiomics features, for developing brain metastases (BM) in patients with radically treated stage III non-small cell lung cancer (NSCLC). METHODS Clinical data and planning CT scans for thoracic radiotherapy were retrieved from patients with radically treated stage III NSCLC. Radiomics features were extracted from the GTV, primary lung tumor (GTVp), and involved lymph nodes (GTVn), separately. Competing risk analysis was used to develop models (clinical, radiomics, and combined model). LASSO regression was performed to select radiomics features and train models. Area under the receiver operating characteristic curves (AUC-ROC) and calibration were performed to assess the models' performance. RESULTS Three-hundred-ten patients were eligible and 52 (16.8%) developed BM. Three clinical variables (age, NSCLC subtype, and GTVn) and five radiomics features from each radiomics model were significantly associated with BM. Radiomic features measuring tumor heterogeneity were the most relevant. The AUCs and calibration curves of the models showed that the GTVn radiomics model had the best performance (AUC: 0.74; 95% CI: 0.71-0.86; sensitivity: 84%; specificity: 61%; positive predictive value [PPV]: 29%; negative predictive value [NPV]: 95%; accuracy: 65%). CONCLUSION Age, NSCLC subtype, and GTVn were significant risk factors for BM. GTVn radiomics features provided higher predictive value than GTVp and GTV for BM development. GTVp and GTVn should be separated in clinical and research practice.
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Affiliation(s)
- Haiyan Zeng
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Dirk K M De Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Yves C P Willems
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Juliette H R J Degens
- Department of Respiratory Medicine, Zuyderland Medical Center, 6419 PC Heerlen, The Netherlands
| | | | - Cordula Pitz
- Department of Pulmonary Diseases, Laurentius Hospital, 6043 CV Roermond, The Netherlands
| | - Francesco Cortiula
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- Department of Medical Oncology, University Hospital of Udine, 33100 Udine, Italy
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, Maastricht, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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