1
|
Chen Y, Fan Z, Luo Z, Kang X, Wan R, Li F, Lin W, Han Z, Qi B, Lin J, Sun Y, Huang J, Xu Y, Chen S. Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment. Neural Regen Res 2025; 20:1135-1152. [PMID: 38989952 DOI: 10.4103/nrr.nrr-d-23-00875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 12/21/2023] [Indexed: 07/12/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.
Collapse
Affiliation(s)
- Yisheng Chen
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhongcheng Fan
- Department of Orthopedic Surgery, Hainan Province Clinical Medical Center, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, Hainan Province, China
| | - Zhiwen Luo
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xueran Kang
- Department of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Renwen Wan
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangqi Li
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiwei Lin
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Zhihua Han
- Department of Orthopedics, Shanghai General Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai, China
| | - Beijie Qi
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinrong Lin
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yaying Sun
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiebin Huang
- Department of Infectious Diseases, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Yuzhen Xu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Shiyi Chen
- Department of Sport Medicine, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
2
|
Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024; 24:498-512. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
Collapse
Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
3
|
Zhao K, Zhang H, Lin J, Xu S, Liu J, Qian X, Gu Y, Ren G, Lu X, Chen B, Chen D, Yan J, Ma J, Wei W, Wang Y. Radiomic Prediction of CCND1 Expression Levels and Prognosis in Low-grade Glioma Based on Magnetic Resonance Imaging. Acad Radiol 2024:S1076-6332(24)00196-X. [PMID: 38824087 DOI: 10.1016/j.acra.2024.03.031] [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: 10/27/2023] [Revised: 03/24/2024] [Accepted: 03/24/2024] [Indexed: 06/03/2024]
Abstract
OJECTIVES Low-grade glioma (LGG) is associated with increased mortality owing to recrudescence and the tendency for malignant transformation. Therefore, it is imperative to discover novel prognostic biomarkers as existing traditional prognostic biomarkers of glioma, including clinicopathological features and imaging examinations, are unable to meet the clinical demand for precision medicine. Accordingly, we aimed to evaluate the prognostic value of cyclin D1 (CCND1) expression levels and construct radiomic models to predict these levels in patients with LGG MATERIALS AND METHODS: A total of 412 LGG cases from The Cancer Genome Atlas (TCGA) were used for gene-based prognostic analysis. Using magnetic resonance imaging (MRI) images stored in The Cancer Imaging Archive with genomic data from TCGA, 149 cases were selected for radiomics feature extraction and model construction. After feature extraction, the radiomic signature was constructed using logistic regression (LR) and support vector machine (SVM) analyses. RESULTS CCND1 was identified as a prognosis-related gene with differential expression in tumor and normal samples and plays a role in regulating both the cell cycle and immune response. Landmark analysis revealed that high-expression levels of CCND1 were beneficial for survival (P < 0.05) in advanced LGG. Four optimal radiomics features were selected to construct radiomics models. The performance of LR and SVM achieved areas under the curve of 0.703 and 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively. CONCLUSION Elevated levels of CCND1 expression could impact the prognosis of patients with LGG. MRI-based radiomics, especially the AUC values, can serve as a novel tool for predicting CCND1 expression and understanding the correlation between elevated CCND1 expression and prognosis. AVAILABILITY OF DATA AND MATERIALS The datasets analyzed during the current study are available in the TCGA, TCIA, UCSC XENA and GTEx repository, https://portal.gdc.cancer.gov/, https://www.cancerimagingarchive.net/, https://xenabrowser.net/datapages/, https://www.gtexportal.org/home/.
Collapse
Affiliation(s)
- Kun Zhao
- Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.); Department of Cell Biology, Institute of Bioengineering, School of Medicine, Soochow University, Suzhou, Jiangsu, China (K.Z., W.W.); Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.)
| | - Hui Zhang
- Fujian Center for Safety Evaluation of New Drug, Fujian Medical University, Fuzhou, Fujian, China (H.Z.)
| | - Jianyang Lin
- Department of General Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (J.L.)
| | - Shoucheng Xu
- Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.)
| | - Jianzhi Liu
- Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.)
| | - Xianjing Qian
- Medical College, Jiangsu University, Zhenjiang, Jiangsu, China (X.Q.)
| | - Yongbing Gu
- Medical Imaging Department, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (Y.G., G.R.)
| | - Guoqiang Ren
- Medical Imaging Department, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (Y.G., G.R.)
| | - Xinyu Lu
- Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (X.L., B.C.)
| | - Baomin Chen
- Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (X.L., B.C.)
| | - Deng Chen
- Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.)
| | - Jun Yan
- Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.)
| | - Jichun Ma
- Laboratory Center, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (J.M.)
| | - Wenxiang Wei
- Department of Cell Biology, Institute of Bioengineering, School of Medicine, Soochow University, Suzhou, Jiangsu, China (K.Z., W.W.)
| | - Yuanwei Wang
- Department of Neurology, Shuyang Hospital, Shuyang Hospital Affiliated to Xuzhou Medical University, Shuyang, Jiangsu, China (Y.W.).
| |
Collapse
|
4
|
Duan W, Wang Z, Ma Z, Zheng H, Li Y, Pei D, Wang M, Qiu Y, Duan M, Yan D, Ji Y, Cheng J, Liu X, Zhang Z, Yan J. Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities. Cancer Sci 2024; 115:1261-1272. [PMID: 38279197 PMCID: PMC11007007 DOI: 10.1111/cas.16089] [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: 09/04/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/28/2024] Open
Abstract
Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.
Collapse
Affiliation(s)
- Wenchao Duan
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zilong Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zeyu Ma
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Hongwei Zheng
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yinhua Li
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Dongling Pei
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Minkai Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuning Qiu
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Mengjiao Duan
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Dongming Yan
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuchen Ji
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Jingliang Cheng
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Xianzhi Liu
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zhenyu Zhang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Jing Yan
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| |
Collapse
|
5
|
Chen D, Zhang R, Huang X, Ji C, Xia W, Qi Y, Yang X, Lin L, Wang J, Cheng H, Tang W, Yu J, Hoon DSB, Zhang J, Gao X, Yao Y. MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma. Biomark Res 2024; 12:14. [PMID: 38291499 PMCID: PMC10829320 DOI: 10.1186/s40364-024-00560-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND The tumor immune microenvironment can influence the prognosis and treatment response to immunotherapy. We aimed to develop a non-invasive radiomic signature in high-grade glioma (HGG) to predict the absolute density of tumor-associated macrophages (TAMs), the preponderant immune cells in the microenvironment of HGG. We also aimed to evaluate the association between the signature, and tumor immune phenotype as well as response to immunotherapy. METHODS In this retrospective setting, total of 379 patients with HGG from three independent cohorts were included to construct a radiomic model named Radiomics Immunological Biomarker (RIB) for predicting the absolute density of M2-like TAM using the mRMR feature ranking method and LASSO classifier. Among them, 145 patients from the TCGA microarray cohort were randomly allocated into a training set (N=101) and an internal validation set (N=44), while the immune-phenotype cohort (N=203) and the immunotherapy-treated cohort (N=31, patients from a prospective clinical trial treated with DC vaccine) recruited from Huashan Hospital were used as two external validation sets. The immunotherapy-treated cohort was also used to evaluate the relationship between RIB and immunotherapy response. Radiogenomic analysis was performed to find functional annotations using RNA sequencing data from TAM cells. RESULTS An 11-feature radiomic model for M2-like TAM was developed and validated in four datasets of HGG patients (area under the curve = 0.849, 0.719, 0.674, and 0.671) using MRI images of post contrast enhanced T1-weighted (T1CE). Patients with high RIB scores had a strong inflammatory response. Four hub-genes (SLC7A7, RNASE6, HLA-DRB1 and CD300A) expressed by TAM were identified to be closely related to the RIB, providing important evidence for biological interpretation. Only individuals with a high RIB score were shown to have survival benefits from DC vaccine [DC vaccine vs. Placebo: median progression-free survival (mPFS), 10.0 mos vs. 4.5 mos, HR=0.17, P=0.0056, 95%CI=0.041-0.68; median overall survival (mOS), 15.0 mos vs. 7.0 mos, HR=0.17, P =0.0076, 95%CI=0.04-0.68]. Multivariate analyses also confirmed that treatment by DC vaccine was an independent factor for improved survival in the high RIB score group. However, in the low RIB score group, DC vaccine was not associated with improved survival. Furthermore, a radiomic nomogram based on the RIB score and clinical factors could efficiently predict the 1-, 2-, and 3-year survival rates, as confirmed by ROC curve analysis (AUC for 1-, 2- and 3-year survival: 0.705, 0.729 and 0.684, respectively). CONCLUSIONS The radiomic model could allow for non-invasive assessment of the absolute density of TAM from MRI images in HGG patients. Of note, our RIB model is the first immunological radiomic model confirmed to have the ability to predict survival benefits from DC vaccine in gliomas, thereby providing a novel tool to inform treatment decisions and monitor patient treatment course by radiomics.
Collapse
Affiliation(s)
- Di Chen
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Immunology Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Rui Zhang
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xiaoming Huang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Immunology Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Chunxia Ji
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Immunology Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Wei Xia
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Ying Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Immunology Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Xinyu Yang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Immunology Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Lishuang Lin
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haixia Cheng
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Weijun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Dave S B Hoon
- Department of Translational Molecular Medicine, Saint Johns Cancer Institute, Providence Health Systems, Santa Monica, CA, USA
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China.
| | - Xin Gao
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China.
| | - Yu Yao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China.
- Immunology Laboratory, Neurosurgical Institute of Fudan University, Shanghai, China.
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China.
| |
Collapse
|
6
|
Herr J, Stoyanova R, Mellon EA. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Crit Rev Oncog 2024; 29:33-65. [PMID: 38683153 DOI: 10.1615/critrevoncog.2023050852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
Collapse
Affiliation(s)
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| | - Eric Albert Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| |
Collapse
|
7
|
Picca A, Bruno F, Nichelli L, Sanson M, Rudà R. Advances in molecular and imaging biomarkers in lower-grade gliomas. Expert Rev Neurother 2023; 23:1217-1231. [PMID: 37982735 DOI: 10.1080/14737175.2023.2285472] [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: 08/07/2023] [Accepted: 11/15/2023] [Indexed: 11/21/2023]
Abstract
INTRODUCTION Lower-grade (grade 2-3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up. AREAS COVERED This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up. EXPERT OPINION The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs.
Collapse
Affiliation(s)
- Alberto Picca
- Service de Neurologie 2 Mazarin, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau-Paris Brain Institute-ICM, AP-HP, Paris, France
| | - Francesco Bruno
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University and City of Health and Science University Hospital, Turin, Italy
| | - Lucia Nichelli
- Service de Neuroradiologie, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
| | - Marc Sanson
- Service de Neurologie 2 Mazarin, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau-Paris Brain Institute-ICM, AP-HP, Paris, France
| | - Roberta Rudà
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University and City of Health and Science University Hospital, Turin, Italy
| |
Collapse
|
8
|
Lee M. Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature. BIOLOGY 2023; 12:893. [PMID: 37508326 PMCID: PMC10376033 DOI: 10.3390/biology12070893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.
Collapse
Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| |
Collapse
|
9
|
Khalili N, Kazerooni AF, Familiar A, Haldar D, Kraya A, Foster J, Koptyra M, Storm PB, Resnick AC, Nabavizadeh A. Radiomics for characterization of the glioma immune microenvironment. NPJ Precis Oncol 2023; 7:59. [PMID: 37337080 DOI: 10.1038/s41698-023-00413-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/02/2023] [Indexed: 06/21/2023] Open
Abstract
Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.
Collapse
Affiliation(s)
- Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Debanjan Haldar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Kraya
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jessica Foster
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mateusz Koptyra
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Phillip B Storm
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Salome P, Sforazzini F, Grugnara G, Kudak A, Dostal M, Herold-Mende C, Heiland S, Debus J, Abdollahi A, Knoll M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers (Basel) 2023; 15:cancers15030965. [PMID: 36765922 PMCID: PMC9913466 DOI: 10.3390/cancers15030965] [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: 01/07/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
Collapse
Affiliation(s)
- Patrick Salome
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
| | - Francesco Sforazzini
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
| | - Gianluca Grugnara
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Andreas Kudak
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Matthias Dostal
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Brain Tumour Group, European Organization for Research and Treatment of Cancer, 1200 Brussels, Belgium
- Division of Neurosurgical Research, Department of Neurosurgery, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jürgen Debus
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Amir Abdollahi
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
| |
Collapse
|
12
|
Alleman K, Knecht E, Huang J, Zhang L, Lam S, DeCuypere M. Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review. Cancers (Basel) 2023; 15:cancers15020545. [PMID: 36672494 PMCID: PMC9856816 DOI: 10.3390/cancers15020545] [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: 09/30/2022] [Revised: 01/05/2023] [Accepted: 01/08/2023] [Indexed: 01/18/2023] Open
Abstract
Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. A systematic review of the DL-based prognostication of gliomas was performed using the Embase (Elsevier), PubMed MEDLINE (National library of Medicine), and Scopus (Elsevier) databases, in accordance with PRISMA guidelines. All included studies focused on the prognostication of gliomas, and predicted overall survival (13 studies, 81%), overall survival as well as genotype (2 studies, 12.5%), and response to immunotherapy (1 study, 6.2%). Multimodal analyses were varied, with 6 studies (37.5%) combining MRI with clinical data; 6 studies (37.5%) integrating MRI with histologic, clinical, and biomarker data; 3 studies (18.8%) combining MRI with genomic data; and 1 study (6.2%) combining histologic imaging with clinical data. Studies that compared multimodal models to unimodal-only models demonstrated improved predictive performance. The risk of bias was mixed, most commonly due to inconsistent methodological reporting. Overall, the use of multimodal data in DL assessments of gliomas leads to a more accurate overall survival prediction. However, due to data limitations and a lack of transparency in model and code reporting, the full extent of multimodal DL as a resource for brain tumor patients has not yet been realized.
Collapse
Affiliation(s)
- Kaitlyn Alleman
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA
| | - Erik Knecht
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA
| | - Jonathan Huang
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Lu Zhang
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Sandi Lam
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Michael DeCuypere
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Correspondence:
| |
Collapse
|
13
|
Liu H, Tao T. Prognosis and immune features of pyroptosis-related RNA patterns in low-grade glioma. Front Oncol 2022; 12:1015850. [PMID: 36605437 PMCID: PMC9808047 DOI: 10.3389/fonc.2022.1015850] [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: 08/10/2022] [Accepted: 11/08/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose Low-grade gliomas (LGG), which are malignant primary brain tumors, are more prevalent in young adults. Pyroptosis, an inflammatory form of programmed cell death, has been shown in recent years to be directly associated with tumor growth and tumor microenvironment (TME). However, the correlation between LGG and pyroptosis remained to be explored. In this research, we explored pyroptosis-related gene expression patterns and their prognostic significance based on transcriptome profiles and clinical data in LGG. Methods We identified 31 pyroptosis-related genes differentially expressed at the mRNA level between the data of LGG patients from TCGA and the data of normal brain tissues from GTEx. Univariate Cox regression analysis was used to screen 16 differentially expressed genes (DEGs) based on survival data. Next, the prognostic model was established using LASSO Cox regression, which divided LGG patients into high- and low- risk subgroups and showed an independent prognostic value for overall survival (OS) combined with clinical factors in the CGGA test cohort. Pyroptosis and immune cells were correlated through the CIBERSORT R package and the TIMER database. Results Based on the analyses of 523 LGG and 1152 normal tissues, nine significant differential genes were identified. The AUC remained at about 0.74 when combined with the risk score and clinical factors. Enrichment analyses revealed that DEGs were mainly enriched in cytokine-cytokine receptor interactions, immune response and chemokine signaling pathways. Immune cell enrichment analysis demonstrated that scores for most immune cell types differed significantly between the high-and low-risk groups, and further infiltrating analysis showed obvious differences between these two risk subgroups. Conclusion Pyroptosis-related genes play a pivotal role in LGG and are associated with tumor immunity, which may be beneficial to the prognosis and immunotherapy of LGG.
Collapse
Affiliation(s)
- Hanzhang Liu
- Morphology Laboratory, Medical College of Nantong University, Nantong, Jiangsu, China,*Correspondence: Tao Tao, ; Hanzhang Liu,
| | - Tao Tao
- Department of Clinical Medicine, Ningbo College of Health Science, Ningbo, Zhejiang, China,*Correspondence: Tao Tao, ; Hanzhang Liu,
| |
Collapse
|
14
|
Xiao ML, Wei Y, Zhang J, Jian JM, Song Y, Lin ZJ, Qian L, Zhang GF, Qiang JW. MRI Texture Analysis for Preoperative Prediction of Lymph Node Metastasis in Patients with Nonsquamous Cell Cervical Carcinoma. Acad Radiol 2022; 29:1661-1671. [PMID: 35151550 DOI: 10.1016/j.acra.2022.01.005] [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: 11/16/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES To preoperatively predict lymph node metastasis (LNM) in patients with cervical nonsquamous cell carcinoma (non-SCC) based on magnetic resonance imaging (MRI) texture analysis. MATERIALS AND METHODS This retrospective study included 104 consecutive patients (mean age of 47.2 ± 11.3 years) with stage IB-IIA cervical non-SCC. According to the ratio of 7:3, 72, and 32 patients were randomly divided into the training and testing cohorts. A total of 272 original features were extracted. In the process of feature selection, features with intraclass correlation coefficients (ICCs) less than 0.8 were eliminated. The Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were applied to reduce redundancy, overfitting, and selection biases. Further, a support vector machine (SVM) with linear kernel function was applied to select the optimal feature set with a high discrimination power. RESULTS The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI (LN status on MRI)-based SVM models yielded an AUC and accuracy of 0.78 and 0.79; 0.79 and 0.69; 0.79 and 0.81 for predicting LNM in the training cohort, and 0.82 and 0.78; 0.82 and 0.69; 0.79 and 0.72 in the testing cohort. The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI-based SVM models performed better than morphologic criteria of LNS-MRI and yield similar discrimination abilities in predicting LNM in the training and testing cohorts (all p-value > 0.05). In addition, the T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the AC and ASC subgroups (all p-value > 0.05). CONCLUSION The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI+DWI+LNS-MRI-based SVM models showed similar good discrimination ability and performed better than the morphologic criteria of LNS-MRI in predicting LNM in patients with cervical non-SCC. The inclusion of the CE-T1WI sequence and morphologic criteria of LNS-MRI did not significantly improve the performance of the T2WI + DWI-based model. The T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the subgroup analysis.
Collapse
Affiliation(s)
- Mei Ling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China; Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Yan Wei
- Department of Automation, Zhejiang University of Technology, Hangzhou, China
| | - Jing Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Jun Ming Jian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers, Shanghai, China
| | - Zi Jing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Lan Qian
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Guo Fu Zhang
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
| |
Collapse
|
15
|
Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
Collapse
Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| |
Collapse
|
16
|
Zhu M, Li S, Kuang Y, Hill VB, Heimberger AB, Zhai L, Zhai S. Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective. Front Oncol 2022; 12:924245. [PMID: 35982952 PMCID: PMC9379255 DOI: 10.3389/fonc.2022.924245] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022] Open
Abstract
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.
Collapse
Affiliation(s)
- Ming Zhu
- Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Sijia Li
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Yu Kuang
- Medical Physics Program, Department of Health Physics, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Virginia B. Hill
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Amy B. Heimberger
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Lijie Zhai
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- *Correspondence: Lijie Zhai, ; Shengjie Zhai,
| | - Shengjie Zhai
- Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United States
- *Correspondence: Lijie Zhai, ; Shengjie Zhai,
| |
Collapse
|
17
|
Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
Collapse
Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| |
Collapse
|
18
|
Park CJ, Park YW, Ahn SS, Kim D, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation. Korean J Radiol 2022; 23:77-88. [PMID: 34983096 PMCID: PMC8743155 DOI: 10.3348/kjr.2021.0421] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/05/2021] [Accepted: 08/05/2021] [Indexed: 12/14/2022] Open
Abstract
Objective Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.
Collapse
Affiliation(s)
- Chae Jung Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dain Kim
- Department of Psychology, Yonsei University, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
| |
Collapse
|
19
|
Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
Collapse
Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| |
Collapse
|
20
|
Zhang K, Zhang Y, Fang X, Dong J, Qian L. MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis. BMC Cancer 2021; 21:1266. [PMID: 34819042 PMCID: PMC8611883 DOI: 10.1186/s12885-021-08988-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/10/2021] [Indexed: 01/13/2023] Open
Abstract
Background To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). Methods One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group. Results ADCmean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADCmean, ADCmin and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADCmean and radscore were independent risk factors for recurrence, which were verified in the test group. Conclusions ADCmean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08988-x.
Collapse
Affiliation(s)
- Kaiyue Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Yu Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Xin Fang
- Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Jiangning Dong
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China. .,Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China.
| | - Liting Qian
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China. .,Department of Radiation Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei, 230001, China.
| |
Collapse
|
21
|
Pandya S, Thakur A, Saxena S, Jassal N, Patel C, Modi K, Shah P, Joshi R, Gonge S, Kadam K, Kadam P. A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2021; 21:7786. [PMID: 34883787 PMCID: PMC8659723 DOI: 10.3390/s21237786] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 12/13/2022]
Abstract
The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.
Collapse
Affiliation(s)
- Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Aanchal Thakur
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Santosh Saxena
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Nandita Jassal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Chirag Patel
- Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research, Changa 388421, India;
| | - Kirit Modi
- Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India;
| | - Pooja Shah
- Information Technology Department, Gandhinagar Institute of Technology, Ahmedabad 382010, India;
| | - Rahul Joshi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Sudhanshu Gonge
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Kalyani Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Prachi Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| |
Collapse
|
22
|
Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature. BIOMED RESEARCH INTERNATIONAL 2021; 2021:7643209. [PMID: 34337051 PMCID: PMC8324343 DOI: 10.1155/2021/7643209] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/18/2021] [Accepted: 07/06/2021] [Indexed: 11/21/2022]
Abstract
Background The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.
Collapse
|