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Basree MM, Li C, Um H, Bui AH, Liu M, Ahmed A, Tiwari P, McMillan AB, Baschnagel AM. Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases. J Neurooncol 2024; 168:307-316. [PMID: 38689115 DOI: 10.1007/s11060-024-04669-4] [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: 02/12/2024] [Accepted: 03/27/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS. METHODS Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed. RESULTS Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence. CONCLUSIONS Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it's utility in patient care.
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Affiliation(s)
- Mustafa M Basree
- Deparment of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Chengnan Li
- Department of Computer Science, University of Wisconsin, Madison, WI, USA
| | - Hyemin Um
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - Anthony H Bui
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Manlu Liu
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Azam Ahmed
- Department of Neurological Surgery, University of Wisconsin, Madison, WI, USA
| | - Pallavi Tiwari
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - Alan B McMillan
- Department of Radiology, University of Wisconsin, Madison, WI, USA.
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin, Madison, WI, USA.
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA.
| | - Andrew M Baschnagel
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
- University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
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Cho SJ, Cho W, Choi D, Sim G, Jeong SY, Baik SH, Bae YJ, Choi BS, Kim JH, Yoo S, Han JH, Kim CY, Choo J, Sunwoo L. Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data. Sci Rep 2024; 14:11085. [PMID: 38750084 PMCID: PMC11096355 DOI: 10.1038/s41598-024-60781-5] [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: 11/24/2023] [Accepted: 04/26/2024] [Indexed: 05/18/2024] Open
Abstract
We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.
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Affiliation(s)
- Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Wonwoo Cho
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, 291 Daehak-Ro, Yuseong-Gu, Daejeon, 34141, Republic of Korea
- Letsur Inc, 180 Yeoksam-Ro, Gangnam-Gu, Seoul, 06248, Republic of Korea
| | - Dongmin Choi
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, 291 Daehak-Ro, Yuseong-Gu, Daejeon, 34141, Republic of Korea
- Letsur Inc, 180 Yeoksam-Ro, Gangnam-Gu, Seoul, 06248, Republic of Korea
| | - Gyuhyeon Sim
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, 291 Daehak-Ro, Yuseong-Gu, Daejeon, 34141, Republic of Korea
- Letsur Inc, 180 Yeoksam-Ro, Gangnam-Gu, Seoul, 06248, Republic of Korea
| | - So Yeong Jeong
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Sung Hyun Baik
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Sooyoung Yoo
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Jung Ho Han
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Chae-Yong Kim
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Jaegul Choo
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, 291 Daehak-Ro, Yuseong-Gu, Daejeon, 34141, Republic of Korea.
- Letsur Inc, 180 Yeoksam-Ro, Gangnam-Gu, Seoul, 06248, Republic of Korea.
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea.
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, 82, Gumi-Ro 173Beon-Gil, Bundang-Gu, Seongnam, Gyeonggi, 13620, Republic of Korea.
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Royal-Preyra B. The Impact of Immune Checkpoint Inhibition on the Risk of Radiation Necrosis Following Stereotactic Radiotherapy for Metastatic Brain Cancer. Cureus 2023; 15:e51381. [PMID: 38161546 PMCID: PMC10757743 DOI: 10.7759/cureus.51381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/30/2023] [Indexed: 01/03/2024] Open
Abstract
Purpose/objective Forty percent of cancer patients develop brain metastases (BM) and are often treated with stereotactic radiation (SRS/SRT). Checkpoint inhibitor (CI) use is suspected of increasing the risk of radiation necrosis (RN). Our aim is to determine whether treatment with CI is associated with an increased risk of RN in BM patients treated with SRS/SRT. Methods We retrospectively identified the medical records of BM patients treated with SRS/SRT between 1/2017 and 12/2021 using an institutional database. RN was defined by MRI imaging read by neuroradiologists and/or surgical pathology. V12GY of patients with and without RN was compared using the Mann-Whitney test. The chi-square test was used to see if RN was associated with CI use, histology, particular CI agent used, > 1 course SRS/SRT, SRS/SRT dose, chemotherapy, whole brain radiotherapy (WBRT), age, or sex. Results Two hundred and fifty-nine patients treated with 455 courses of SRS/SRT were analyzed. The most common primary histologies were lung 56% (N=146), breast 14% (N= 37), melanoma 9% (N=24), and renal cancer 7% (N=18). A total of 53.8% (N = no. of patients) were treated with CI. The overall rate of any RN was 21.8% (N=27) in the CI group compared to 14.8% (N=141) in the non-CI group (p=0.174). Mean V12Gy was 15.525 cc and 9.419 cc in patients with and without RN (p=0.02768). Mean number of SRS/SRT courses was 2 and 1.53 for patients with and without RN, and >1 course of SRS/SRT was a predictor of RN (p <0.01). Other features analyzed were not significant. Conclusion RN was higher in the BM patients treated with SRS/SRT receiving CI compared to non-CI patients (21.8%, N=27, versus 14.6%, N= 16), but failed to reach statistical significance. V12Gy and > 1 course of SRS/SRT was associated with RN. Caution should be taken in treating patients with SRS/SRT and CI there might be an increased risk of RN.
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Affiliation(s)
- Ben Royal-Preyra
- Radiation Oncology, Centre Hospitalier Affilié Universitaire Regional, Trois-Rivières, CAN
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Cao Y, Parekh VS, Lee E, Chen X, Redmond KJ, Pillai JJ, Peng L, Jacobs MA, Kleinberg LR. A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases. Cancers (Basel) 2023; 15:4113. [PMID: 37627141 PMCID: PMC10452423 DOI: 10.3390/cancers15164113] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82-0.94), and AUC-PR of 0.94 (95% CI: 0.87-0.97).
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Affiliation(s)
- Yilin Cao
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Radiation Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA 02115, USA
| | - Vishwa S. Parekh
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 20201, USA
| | - Emerson Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Xuguang Chen
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Kristin J. Redmond
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Jay J. Pillai
- Division of Neuroradiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Luke Peng
- Department of Radiation Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA 02115, USA
| | - Michael A. Jacobs
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Diagnostics and Interventional Imaging, McGovern Medical School, Houston, TX 77030, USA
| | - Lawrence R. Kleinberg
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
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Capobianco E, Dominietto M. Assessment of brain cancer atlas maps with multimodal imaging features. J Transl Med 2023; 21:385. [PMID: 37308956 DOI: 10.1186/s12967-023-04222-3] [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: 04/18/2023] [Accepted: 05/22/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. MAIN TEXT Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. CONCLUSIONS The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.
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Affiliation(s)
- Enrico Capobianco
- The Jackson Laboratory, 10 Discovery Drive, Farmington, CT, 06032, USA.
| | - Marco Dominietto
- Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232, Villigen, Switzerland
- Gate To Brain SA, Via Livio 7, 6830, Chiasso, Switzerland
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Vaios EJ, Winter SF, Shih HA, Dietrich J, Peters KB, Floyd SR, Kirkpatrick JP, Reitman ZJ. Novel Mechanisms and Future Opportunities for the Management of Radiation Necrosis in Patients Treated for Brain Metastases in the Era of Immunotherapy. Cancers (Basel) 2023; 15:2432. [PMID: 37173897 PMCID: PMC10177360 DOI: 10.3390/cancers15092432] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Radiation necrosis, also known as treatment-induced necrosis, has emerged as an important adverse effect following stereotactic radiotherapy (SRS) for brain metastases. The improved survival of patients with brain metastases and increased use of combined systemic therapy and SRS have contributed to a growing incidence of necrosis. The cyclic GMP-AMP (cGAMP) synthase (cGAS) and stimulator of interferon genes (STING) pathway (cGAS-STING) represents a key biological mechanism linking radiation-induced DNA damage to pro-inflammatory effects and innate immunity. By recognizing cytosolic double-stranded DNA, cGAS induces a signaling cascade that results in the upregulation of type 1 interferons and dendritic cell activation. This pathway could play a key role in the pathogenesis of necrosis and provides attractive targets for therapeutic development. Immunotherapy and other novel systemic agents may potentiate activation of cGAS-STING signaling following radiotherapy and increase necrosis risk. Advancements in dosimetric strategies, novel imaging modalities, artificial intelligence, and circulating biomarkers could improve the management of necrosis. This review provides new insights into the pathophysiology of necrosis and synthesizes our current understanding regarding the diagnosis, risk factors, and management options of necrosis while highlighting novel avenues for discovery.
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Affiliation(s)
- Eugene J. Vaios
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
| | - Sebastian F. Winter
- Division of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Helen A. Shih
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jorg Dietrich
- Division of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Katherine B. Peters
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
| | - Scott R. Floyd
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
| | - John P. Kirkpatrick
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
| | - Zachary J. Reitman
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
- Department of Pathology, Duke University Medical Center, Durham, NC 27710, USA
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Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction. J Neurooncol 2023; 161:441-450. [PMID: 36635582 DOI: 10.1007/s11060-022-04234-x] [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: 12/09/2022] [Accepted: 12/30/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery. METHODS Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed. RESULTS AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application. CONCLUSIONS Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.
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Wang S, Feng Y, Chen L, Yu J, Van Ongeval C, Bormans G, Li Y, Ni Y. Towards updated understanding of brain metastasis. Am J Cancer Res 2022; 12:4290-4311. [PMID: 36225632 PMCID: PMC9548021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/06/2022] [Indexed: 06/16/2023] Open
Abstract
Brain metastasis (BM) is a common complication in cancer patients with advanced disease and attributes to treatment failure and final mortality. Currently there are several therapeutic options available; however these are only suitable for limited subpopulation: surgical resection or radiosurgery for cases with a limited number of lesions, targeted therapies for approximately 18% of patients, and immune checkpoint inhibitors with a response rate of 20-30%. Thus, there is a pressing need for development of novel diagnostic and therapeutic options. This overview article aims to provide research advances in disease model, targeted therapy, blood brain barrier (BBB) opening strategies, imaging and its incorporation with artificial intelligence, external radiotherapy, and internal targeted radionuclide theragnostics. Finally, a distinct type of BM, leptomeningeal metastasis is also covered.
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Affiliation(s)
- Shuncong Wang
- KU Leuven, Biomedical Group, Campus GasthuisbergLeuven 3000, Belgium
| | - Yuanbo Feng
- KU Leuven, Biomedical Group, Campus GasthuisbergLeuven 3000, Belgium
| | - Lei Chen
- KU Leuven, Biomedical Group, Campus GasthuisbergLeuven 3000, Belgium
| | - Jie Yu
- KU Leuven, Biomedical Group, Campus GasthuisbergLeuven 3000, Belgium
| | - Chantal Van Ongeval
- Department of Radiology, University Hospitals Leuven, KU LeuvenHerestraat 49, Leuven 3000, Belgium
| | - Guy Bormans
- KU Leuven, Biomedical Group, Campus GasthuisbergLeuven 3000, Belgium
| | - Yue Li
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health SciencesShanghai 201318, China
| | - Yicheng Ni
- KU Leuven, Biomedical Group, Campus GasthuisbergLeuven 3000, Belgium
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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