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Demetriou D, Lockhat Z, Brzozowski L, Saini KS, Dlamini Z, Hull R. The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics. Cancers (Basel) 2024; 16:1076. [PMID: 38473432 DOI: 10.3390/cancers16051076] [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: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.
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Affiliation(s)
- Demetra Demetriou
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Zarina Lockhat
- Department of Radiology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Luke Brzozowski
- Translational Research and Core Facilities, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Kamal S Saini
- Fortrea Inc., 8 Moore Drive, Durham, NC 27709, USA
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Zodwa Dlamini
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Rodney Hull
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
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Sun R, Zhang M, Yang L, Yang S, Li N, Huang Y, Song H, Wang B, Huang C, Hou F, Wang H. Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study. Insights Imaging 2024; 15:21. [PMID: 38270647 PMCID: PMC10811316 DOI: 10.1186/s13244-023-01569-5] [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: 07/05/2023] [Accepted: 11/09/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVE To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.
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Affiliation(s)
- Rui Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Lei Yang
- Department of Radiology, Qingdao Center Hospital, Qingdao, 266042, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250000, Shandong, China
| | - Na Li
- Department of Radiology, The People's Hospital of Zhangqiu Area, Jinan, 250200, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, 457001, Henan, China
| | - Hongzheng Song
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Bo Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, 100080, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
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He YQ, Wang TM, Yang DW, Xue WQ, Deng CM, Li DH, Zhang WL, Liao Y, Xiao RW, Luo LT, Diao H, Tong XT, Wu YX, Chen XY, Zhang JB, Zhou T, Li XZ, Zhang PF, Zheng XH, Zhang SD, Hu YZ, Zhou GQ, Ma J, Sun Y, Jia WH. A comprehensive predictive model for radiation-induced brain injury in risk stratification and personalized radiotherapy of nasopharyngeal carcinoma. Radiother Oncol 2024; 190:109974. [PMID: 37913956 DOI: 10.1016/j.radonc.2023.109974] [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: 03/13/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND AND PURPOSE Radiation-induced brain injury (RBI) is a severe radiotoxicity for nasopharyngeal carcinoma (NPC) patients, greatly affecting their long-term life quality and survival. We aim to establish a comprehensive predictive model including clinical factors and newly developed genetic variants to improve the precision of RBI risk stratification. MATERIALS AND METHODS By performing a large registry-based retrospective study with magnetic resonance imaging follow-up on RBI development, we conducted a genome-wide association study and developed a polygenic risk score (PRS) for RBI in 1189 NPC patients who underwent intensity-modulated radiotherapy. We proposed a tolerance dose scheme for temporal lobe radiation based on the risk predicted by PRS. Additionally, we established a nomogram by combining PRS and clinical factors for RBI risk prediction. RESULTS The 38-SNP PRS could effectively identify high-risk individuals of RBI (P = 1.42 × 10-34). Based on genetic risk calculation, the recommended tolerance doses of temporal lobes should be 57.6 Gy for individuals in the top 10 % PRS subgroup and 68.1 Gy for individuals in the bottom 50 % PRS. Notably, individuals with high genetic risk (PRS > P50) and receiving high radiation dose in the temporal lobes (D0.5CC > 65 Gy) had an approximate 50-fold risk over individuals with low PRS and receiving low radiation dose (HR = 50.09, 95 %CI = 24.27-103.35), showing an additive joint effect (Pinteraction < 0.001). By combining PRS with clinical factors including age, tumor stage, and radiation dose of temporal lobes, the predictive accuracy was significantly improved with C-index increased from 0.78 to 0.85 (P = 1.63 × 10-2). CONCLUSIONS The PRS, together with clinical factors, could improve RBI risk stratification and implies personalized radiotherapy.
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Affiliation(s)
- Yong-Qiao He
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Tong-Min Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Da-Wei Yang
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Wen-Qiong Xue
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Chang-Mi Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Dan-Hua Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Wen-Li Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ying Liao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ruo-Wen Xiao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lu-Ting Luo
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Hua Diao
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Xia-Ting Tong
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Yan-Xia Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xue-Yin Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jiang-Bo Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ting Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xi-Zhao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Pei-Fen Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xiao-Hui Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shao-Dan Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ye-Zhu Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Guan-Qun Zhou
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Jun Ma
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China; School of Public Health, Sun Yat-Sen University, Guangzhou, China.
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Zheng Y, Zong J, Chen Y, Guo J, Lu T, Xin X, Chen Y. Lack of association between XRCC1 SNPs and acute radiation‑induced injury or prognosis in patients with nasopharyngeal carcinoma. Oncol Lett 2023; 26:544. [PMID: 38020297 PMCID: PMC10660173 DOI: 10.3892/ol.2023.14130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/05/2023] [Indexed: 12/01/2023] Open
Abstract
The response to radiation therapy (RT) is closely associated with DNA damage repair. X-ray repair cross-complementing group-1 (XRCC1) is a key gene in the DNA damage repair pathway, and SNPs in this gene alter the expression and activity of its effector protein, which may in turn affect sensitivity to RT. Therefore, the course of tumor treatment and local control rate can be influenced. In the present study, a group of 158 patients with nasopharyngeal carcinoma (NPC) who received intensity-modulated RT at Fujian Cancer Hospital (Fuzhou, China) between July 2012 and October 2013 were included in retrospective chart review and followed up. Plasma was collected before treatment for genotype analysis of the three SNPs of XRCC1, namely Arg194Trp, Arg280His and Arg399Gln. Acute radiation-induced injuries sustained during treatment was graded according to the Radiation Therapy Oncology Group scoring criteria. Post-treatment follow-up was performed until August 2020. In the 158 cases of NPC, no statistically significant association was observed between the three SNPs of the XRCC1 gene and the severity of acute radiation-induced injury or prognosis. However, the AA genotype of XRCC1-Arg399Gln tended to be associated with worse progression-free survival (PFS) compared with the GA + GG genotype, although this was not significant (P=0.069). In addition, multivariate logistic analysis showed that nodal stage was significantly associated with the occurrence of acute severe radiation-induced oral mucositis (P=0.018), and there was also a trend towards an association between nodal stage and the incidence of acute severe radiation-induced pharyngitis; however, this was not statistically significant (P=0.061). Furthermore, multivariate Cox regression analysis showed that older age, distant metastasis and higher clinical stage were independent risk factors for PFS in patients with NPC. In conclusion, relying solely on the aforementioned SNPs of the XRCC1 gene may not provide a robust enough basis to predict the response to RT or prognosis in patients with NPC.
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Affiliation(s)
- Yuhong Zheng
- Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
| | - Jingfeng Zong
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
| | - Yansong Chen
- Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
| | - Junying Guo
- Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
| | - Tianzhu Lu
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
| | - Xiaoqin Xin
- Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
| | - Yan Chen
- Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, P.R. China
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Yuan Y, Liu X, Dong Y, Zhang R, Meng Q, Dang X, Li L, Ren Y, Dong J. Association between single nucleotide polymorphism of DNA damage repair related genes and radiosensitivity in healthy individuals. RADIATION PROTECTION DOSIMETRY 2023; 199:1533-1538. [PMID: 37721085 DOI: 10.1093/rpd/ncad204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 05/17/2023] [Accepted: 06/18/2023] [Indexed: 09/19/2023]
Abstract
Radiosensitivity in humans can influence radiation-induced normal tissue toxicity. As radiosensitivity has a genetic predisposition, we aimed to investigate the possible association between four single nucleotide polymorphism (SNP) sites and the radiosensitivity in healthy people. We genotyped four selected SNPs: TRIP12 (rs13018957), UIMC1 (rs1700490) and POLN (rs2022302), and analyzed the association between SNP and the radiosensitivity in healthy people. We distinguished radiosensitivity by chromosome aberration analysis in healthy individuals. Healthy donors were classified into three groups based on chromosomal aberrations: resistant, normal and sensitive. Using the normal group as a reference, the genotypes CT and CC of rs13018957 (CT: OR = 26.13; CC: OR = 15.97), AA of rs1700490 (OR = 32.22) and AG of rs2022302 (OR = 13.98) were risk factors for radiosensitivity. The outcomes of the present study suggest that four SNPs are associated with radiosensitivity. This study lends insights to the underlying mechanisms of radiosensitivity and improves our ability to identify radiosensitive individuals.
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Affiliation(s)
- Yayi Yuan
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Xiaoming Liu
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Yuyang Dong
- Department of nuclear environment, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Ruifeng Zhang
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Qianqian Meng
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Xuhong Dang
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Lin Li
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Yue Ren
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
| | - Juancong Dong
- Department of Radiation Medicine and Environmental Medicine, China Institute for Radiation Protection, 102 Xuefu Street, Taiyuan City 030006, Shanxi Province, China
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Jin N, Qiao B, Zhao M, Li L, Zhu L, Zang X, Gu B, Zhang H. Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics. Cancer Med 2023; 12:19260-19271. [PMID: 37635388 PMCID: PMC10557859 DOI: 10.1002/cam4.6474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND To investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC). METHODS The region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co-expression network analysis was used to construct co-expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features. RESULTS Four hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs. CONCLUSIONS OSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.
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Affiliation(s)
- Nenghao Jin
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Bo Qiao
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Min Zhao
- Pharmaceutical Diagnostics, GE HealthcareBeijingChina
- Research Center of Medical Big Data, Chinese PLA General HospitalBeijingChina
| | - Liangbo Li
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Liang Zhu
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Xiaoyi Zang
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Bin Gu
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Haizhong Zhang
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
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Yang J, Nittala MR, Velazquez AE, Buddala V, Vijayakumar S. An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series. Cureus 2023; 15:e37889. [PMID: 37113463 PMCID: PMC10129036 DOI: 10.7759/cureus.37889] [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] [Accepted: 04/19/2023] [Indexed: 04/29/2023] Open
Abstract
Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.
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Affiliation(s)
- Johnny Yang
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Mary R Nittala
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | | | - Vedanth Buddala
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
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Aguado-Barrera ME, Sosa-Fajardo P, Gómez-Caamaño A, Taboada-Valladares B, Couñago F, López-Guerra JL, Vega A. Radiogenomics in lung cancer: Where are we? Lung Cancer 2023; 176:56-74. [PMID: 36621035 DOI: 10.1016/j.lungcan.2023.01.001] [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: 10/25/2022] [Revised: 12/22/2022] [Accepted: 01/01/2023] [Indexed: 01/04/2023]
Abstract
Huge technological and biomedical advances have improved the survival and quality of life of lung cancer patients treated with radiotherapy. However, during treatment planning, a probability that the patient will experience adverse effects is assumed. Radiotoxicity is a complex entity that is largely dose-dependent but also has important intrinsic factors. One of the most studied is the genetic variants that may be associated with susceptibility to the development of adverse effects of radiotherapy. This review aims to present the current status of radiogenomics in lung cancer, integrating results obtained in association studies of SNPs (single nucleotide polymorphisms) related to radiotherapy toxicities. We conclude that despite numerous publications in this field, methodologies and endpoints vary greatly, making comparisons between studies difficult. Analyzing SNPs from the candidate gene approach, together with the study in cohorts limited by the sample size, has complicated the possibility of having validated results. All this delays the incorporation of genetic biomarkers in predictive models for clinical application. Thus, from all analysed SNPs, only 12 have great potential as esophagitis genetic risk factors and deserve further exploration. This review highlights the efforts that have been made to date in the radiogenomic study of radiotoxicity in lung cancer.
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Affiliation(s)
- Miguel E Aguado-Barrera
- Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Av. Choupana s/n, Edif. D, Planta 1, 15706, Santiago de Compostela, A Coruña, Spain; Fundación Pública Galega de Medicina Xenómica (FPGMX), Av. Choupana s/n, Edif. Consultas, Planta menos 2, 15706, Santiago de Compostela, A Coruña, Spain
| | - Paloma Sosa-Fajardo
- Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Av. Choupana s/n, Edif. D, Planta 1, 15706, Santiago de Compostela, A Coruña, Spain; Department of Radiation Oncology, University Hospital Virgen del Rocío, Av. Manuel Siurot, s/n, 41013, Seville, Spain
| | - Antonio Gómez-Caamaño
- Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Av. Choupana s/n, Edif. D, Planta 1, 15706, Santiago de Compostela, A Coruña, Spain; Department of Radiation Oncology, Hospital Clínico Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Av. Choupana s/n, Edif. Consultas, Planta menos 3, 15706, Santiago de Compostela, A Coruña, Spain
| | - Begoña Taboada-Valladares
- Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Av. Choupana s/n, Edif. D, Planta 1, 15706, Santiago de Compostela, A Coruña, Spain; Department of Radiation Oncology, Hospital Clínico Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Av. Choupana s/n, Edif. Consultas, Planta menos 3, 15706, Santiago de Compostela, A Coruña, Spain
| | - Felipe Couñago
- Department of Radiation Oncology, Hospital Universitario Quirónsalud Madrid, C. del Maestro Ángel Llorca 8, 28003, Madrid, Spain
| | - José Luis López-Guerra
- Department of Radiation Oncology, University Hospital Virgen del Rocío, Av. Manuel Siurot, s/n, 41013, Seville, Spain; Instituto de Biomedicina de Sevilla (IBIS/HUVR/CSIC/Universidad de Sevilla), C. Antonio Maura Montaner s/n, 41013, Seville, Spain
| | - Ana Vega
- Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Av. Choupana s/n, Edif. D, Planta 1, 15706, Santiago de Compostela, A Coruña, Spain; Fundación Pública Galega de Medicina Xenómica (FPGMX), Av. Choupana s/n, Edif. Consultas, Planta menos 2, 15706, Santiago de Compostela, A Coruña, Spain; Biomedical Network on Rare Diseases (CIBERER), Av. Monforte de Lemos, 3-5. Pabellón 11. Planta 0, 28029, Madrid, Spain.
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Vasilyev SA, Savchenko RR, Belenko AA, Skryabin NA, Sleptsov AA, Fishman VS, Murashkina AA, Gribova OV, Startseva ZA, Sukhikh ES, Vertinskiy AV, Sukhikh LG, Serov OL, Lebedev IN. ADAMTS1 Is Differentially Expressed in Human Lymphocytes with Various Frequencies of Endogenous γH2AX Foci and Radiation-Induced Micronuclei. RUSS J GENET+ 2022. [DOI: 10.1134/s102279542210012x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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10
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Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study. J Clin Med 2022; 11:jcm11133623. [PMID: 35806913 PMCID: PMC9267569 DOI: 10.3390/jcm11133623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.
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11
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Aguado-Flor E, Fuentes-Raspall MJ, Gonzalo R, Alonso C, Ramón Y Cajal T, Fisas D, Seoane A, Sánchez-Pla Á, Giralt J, Díez O, Gutiérrez-Enríquez S. Cell Senescence-Related Pathways Are Enriched in Breast Cancer Patients With Late Toxicity After Radiotherapy and Low Radiation-Induced Lymphocyte Apoptosis. Front Oncol 2022; 12:825703. [PMID: 35686103 PMCID: PMC9170959 DOI: 10.3389/fonc.2022.825703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Radiation-induced late effects are a common cause of morbidity among cancer survivors. The biomarker with the best evidence as a predictive test of late reactions is the radiation-induced lymphocyte apoptosis (RILA) assay. We aimed to investigate the molecular basis underlying the distinctive RILA levels by using gene expression analysis in patients with and without late effects and in whom we had also first identified differences in RILA levels. Patients and Methods Peripheral blood mononuclear cells of 10 patients with late severe skin complications and 10 patients without symptoms, selected from those receiving radiotherapy from 1993 to 2007, were mock-irradiated or irradiated with 8 Gy. The 48-h response was analyzed in parallel by RILA assay and gene expression profiling with Affymetrix microarrays. Irradiated and non-irradiated gene expression profiles were compared between both groups. Gene set enrichment analysis was performed to identify differentially expressed biological processes. Results Although differentially expressed mRNAs did not reach a significant adjusted p-value between patients suffering and not suffering clinical toxicity, the enriched pathways indicated significant differences between the two groups, either in irradiated or non-irradiated cells. In basal conditions, the main differentially expressed pathways between the toxicity and non-toxicity groups were the transport of small molecules, interferon signaling, and transcription. After 8 Gy, the differences lay in pathways highly related to cell senescence like cell cycle/NF-κB, G-protein-coupled receptors, and interferon signaling. Conclusion Patients at risk of developing late toxicity have a distinctive pathway signature driven by deregulation of immune and cell cycle pathways related to senescence, which in turn may underlie their low RILA phenotype.
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Affiliation(s)
- Ester Aguado-Flor
- Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | | | - Ricardo Gonzalo
- Statistics and Bioinformatics Unit, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Carmen Alonso
- Medical Oncology Department, Santa Creu i Sant Pau Hospital, Barcelona, Spain
| | | | - David Fisas
- Medical Oncology Department, Santa Creu i Sant Pau Hospital, Barcelona, Spain
| | - Alejandro Seoane
- Medical Physics Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Álex Sánchez-Pla
- Statistics and Bioinformatics Unit, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Barcelona, Spain
| | - Jordi Giralt
- Radiation Oncology Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Radiation Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Orland Díez
- Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Area of Clinical and Molecular Genetics, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Sara Gutiérrez-Enríquez
- Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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12
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Ionizing Radiation-Induced Brain Cell Aging and the Potential Underlying Molecular Mechanisms. Cells 2021; 10:cells10123570. [PMID: 34944078 PMCID: PMC8700624 DOI: 10.3390/cells10123570] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/02/2021] [Accepted: 12/16/2021] [Indexed: 01/10/2023] Open
Abstract
Population aging is occurring rapidly worldwide, challenging the global economy and healthcare services. Brain aging is a significant contributor to various age-related neurological and neuropsychological disorders, including Alzheimer's disease and Parkinson's disease. Several extrinsic factors, such as exposure to ionizing radiation, can accelerate senescence. Multiple human and animal studies have reported that exposure to ionizing radiation can have varied effects on organ aging and lead to the prolongation or shortening of life span depending on the radiation dose or dose rate. This paper reviews the effects of radiation on the aging of different types of brain cells, including neurons, microglia, astrocytes, and cerebral endothelial cells. Further, the relevant molecular mechanisms are discussed. Overall, this review highlights how radiation-induced senescence in different cell types may lead to brain aging, which could result in the development of various neurological and neuropsychological disorders. Therefore, treatment targeting radiation-induced oxidative stress and neuroinflammation may prevent radiation-induced brain aging and the neurological and neuropsychological disorders it may cause.
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13
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Cosar R, Özen A, Tastekin E, Süt N, Cakina S, Demir S, Parlar S, Nurlu D, Kavuzlu Y, Koçak Z. Does Gender Difference Effect Radiation-Induced Lung Toxicity? An Experimental Study by Genetic and Histopathological Predictors. Radiat Res 2021; 197:280-288. [PMID: 34735567 DOI: 10.1667/rade-21-00075.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 09/30/2021] [Indexed: 11/03/2022]
Abstract
Several studies have reported differences in radiation toxicity between the sexes, but these differences have not been tested with respect to histopathology and genes. This animal study aimed to show an association between histopathological findings of radiation-induced lung toxicity and the genes ATM, SOD2, TGF-β1, XRCC1, XRCC3 and HHR2. In all, 120 animals were randomly divided into 2 control groups (male and female) and experimental groups comprising fifteen rats stratified by sex, radiotherapy (0 Gy vs. 10 Gy), and time to sacrifice (6, 12, and 24 weeks postirradiation). Histopathological evaluations for lung injury, namely, intra-alveolar edema, alveolar neutrophils, intra-alveolar erythrocytes, activated macrophages, intra-alveolar fibrosis, hyaline arteriosclerosis, and collapse were performed under a light microscope using a grid system; the evaluations were semi quantitatively scored. Then, the alveolar wall thickness was measured. Real-time quantitative reverse transcription PCR (RT-qPCR) was used to determine gene expression differences in ATM, TGF-β1, XRCC1, XRCC3, SOD2 and HHR2L among the groups. Histopathological data showed that radiation-induced acute, subacute, and chronic lung toxicity were worse in male rats. The expression levels of the evaluated genes were significantly higher in females than males in the control group, but this difference was lost over time after radiotherapy. Less toxicity in females may be attributable to the fact that the expression of the evaluated genes was higher in normal lung tissue in females than in males and the changes in gene expression patterns in the postradiotherapy period played a protective role in females. Additional data related to pulmonary function, lung weights, imaging, or outcomes are needed to support this data that is based on histopathology alone.
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Affiliation(s)
- Rusen Cosar
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Alaattin Özen
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Ebru Tastekin
- Department of Pathology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Necdet Süt
- Department of Biostatistics and Informatics, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Suat Cakina
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Selma Demir
- Department of Medical Genetics, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Sule Parlar
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Dilek Nurlu
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Yusuf Kavuzlu
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
| | - Zafer Koçak
- Department of Radiation Oncology, Trakya University, Faculty of Medicine, Edirne, Turkey
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14
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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Li H, Zhu M, Jian L, Bi F, Zhang X, Fang C, Wang Y, Wang J, Wu N, Yu X. Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer. Front Oncol 2021; 11:706043. [PMID: 34485139 PMCID: PMC8415417 DOI: 10.3389/fonc.2021.706043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/19/2021] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVES Accurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer. METHODS This retrospective study included 106 patients with cervical cancer (FIGO stage IB1-IVa) between October 2017 and May 2019. Patients were randomly divided into a training cohort (n = 74) and validation cohort (n = 32). All patients underwent contrast-enhanced computed tomography (CT) prior to treatment. The ITK-SNAP software was used to delineate the region of interest on pre-treatment standard-of-care CT scans. We extracted 792 two-dimensional radiomic features by the Analysis Kit (AK) software. Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features. The radiomic signature (i.e., Radscore) was constructed via Adaboost with Leave-one-out cross-validation. Prognostic models were built by Cox regression model using Akaike information criterion (AIC) as the stopping rule. A nomogram was established to individually predict the OS of patients. Patients were then stratified into high- and low-risk groups according to the Youden index. Kaplan-Meier curves were used to compare the survival difference between the high- and low-risk groups. RESULTS Six textural features were identified, including one gray-level co-occurrence matrix feature and five gray-level run-length matrix features. Only the FIGO stage and Radscore were independent risk factors associated with OS (p < 0.05). The C-index of the FIGO stage in the training and validation cohorts was 0.703 (95% CI: 0.572-0.834) and 0.700 (95% CI: 0.526-0.874), respectively. Correspondingly, the C-index of Radscore was 0.794 (95% CI: 0.707-0.880) and 0.754 (95% CI: 0.623-0.885). The incorporation of the FIGO stage and Radscore achieved better performance, with a C-index of 0.830 (95% CI: 0.738-0.922) and 0.772 (95% CI: 0.615-0.929), respectively. The nomogram based on the FIGO stage and Radscore could individually predict the OS probability with good discrimination and calibration. The high-risk patients had shorter OS compared with the low-risk patients (p < 0.05). CONCLUSION Radiomics has the potential for noninvasive risk stratification and may improve the prediction of OS in patients with cervical cancer when added to the FIGO stage.
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Affiliation(s)
- Handong Li
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Miaochen Zhu
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Feng Bi
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoye Zhang
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Chao Fang
- Department of Clinical Pharmaceutical Research Institution, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, China
| | - Ying Wang
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Jing Wang
- Gynecological Oncology Clinical Research Center, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, China
| | - Nayiyuan Wu
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoping Yu
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
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16
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Lei F, Lei T, Huang Y, Yang M, Liao M, Huang W. Radio-Susceptibility of Nasopharyngeal Carcinoma: Focus on Epstein- Barr Virus, MicroRNAs, Long Non-Coding RNAs and Circular RNAs. Curr Mol Pharmacol 2021; 13:192-205. [PMID: 31880267 DOI: 10.2174/1874467213666191227104646] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/22/2019] [Accepted: 12/29/2019] [Indexed: 02/07/2023]
Abstract
Nasopharyngeal carcinoma (NPC) is a type of head and neck cancer. As a neoplastic disorder, NPC is a highly malignant squamous cell carcinoma that is derived from the nasopharyngeal epithelium. NPC is radiosensitive; radiotherapy or radiotherapy combining with chemotherapy are the main treatment strategies. However, both modalities are usually accompanied by complications and acquired resistance to radiotherapy is a significant impediment to effective NPC therapy. Therefore, there is an urgent need to discover effective radio-sensitization and radio-resistance biomarkers for NPC. Recent studies have shown that Epstein-Barr virus (EBV)-encoded products, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), which share several common signaling pathways, can function in radio-related NPC cells or tissues. Understanding these interconnected regulatory networks will reveal the details of NPC radiation sensitivity and resistance. In this review, we discuss and summarize the specific molecular mechanisms of NPC radio-sensitization and radio-resistance, focusing on EBV-encoded products, miRNAs, lncRNAs and circRNAs. This will provide a foundation for the discovery of more accurate, effective and specific markers related to NPC radiotherapy. EBVencoded products, miRNAs, lncRNAs and circRNAs have emerged as crucial molecules mediating the radio-susceptibility of NPC. This understanding will improve the clinical application of markers and inform the development of novel therapeutics for NPC.
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Affiliation(s)
- Fanghong Lei
- Cancer Research Institute, Hengyang Medical College of University of South China; Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology (2016TP1015), Hengyang 421001, Hunan Province, China
| | - Tongda Lei
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yun Huang
- Cancer Research Institute, Hengyang Medical College of University of South China; Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology (2016TP1015), Hengyang 421001, Hunan Province, China
| | - Mingxiu Yang
- Cancer Research Institute, Hengyang Medical College of University of South China; Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology (2016TP1015), Hengyang 421001, Hunan Province, China
| | - Mingchu Liao
- Department of Oncology, The First Affiliated Hospital of University of South China, Hengyang 421001, Hunan Province, China
| | - Weiguo Huang
- Cancer Research Institute, Hengyang Medical College of University of South China; Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology (2016TP1015), Hengyang 421001, Hunan Province, China
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Savchenko RR, Murashkina AA, Fishman VS, Sukhikh ES, Vertinsky AV, Sukhikh LG, Serov OL, Lebedev IN, Vasilyev SA. Effect of ADAMTS1 Differential Expression on the Radiation-Induced Response of HеLа Cell Line. RUSS J GENET+ 2021. [DOI: 10.1134/s1022795421070127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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18
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Senevirathna JDM, Asakawa S. Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales. Life (Basel) 2021; 11:364. [PMID: 33923876 PMCID: PMC8074237 DOI: 10.3390/life11040364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/09/2021] [Accepted: 04/17/2021] [Indexed: 11/25/2022] Open
Abstract
Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identifying unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challenging. As an environmental stress, the acoustic radiation has a significant effect on the alteration of lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics applications. This review concluded that a combination of many of the omics areas may elucidate the metabolism of lipids and possible hazards on lipids in toothed whales by radiation.
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Affiliation(s)
- Jayan D. M. Senevirathna
- Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan;
- Department of Animal Science, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka
| | - Shuichi Asakawa
- Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan;
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Lin P, Lin YQ, Gao RZ, Wen R, Qin H, He Y, Yang H. Radiomic profiling of clear cell renal cell carcinoma reveals subtypes with distinct prognoses and molecular pathways. Transl Oncol 2021; 14:101078. [PMID: 33862522 PMCID: PMC8065300 DOI: 10.1016/j.tranon.2021.101078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/09/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022] Open
Abstract
Radiomics profile of clear cell renal cell carcinoma is heterogeneity. Multi-scale Radiogenomics could link molecular features and images. Radiomic subtypes could be used for risk stratification.
Background To identify radiomic subtypes of clear cell renal cell carcinoma (ccRCC) patients with distinct clinical significance and molecular characteristics reflective of the heterogeneity of ccRCC. Methods Quantitative radiomic features of ccRCC were extracted from preoperative CT images of 160 ccRCC patients. Unsupervised consensus cluster analysis was performed to identify robust radiomic subtypes based on these features. The Kaplan–Meier method and chi-square test were used to assess the different clinicopathological characteristics and gene mutations among the radiomic subtypes. Subtype-specific marker genes were identified, and gene set enrichment analyses were performed to reveal the specific molecular characteristics of each subtype. Moreover, a gene expression-based classifier of radiomic subtypes was developed using the random forest algorithm and tested in another independent cohort (n = 101). Results Radiomic profiling revealed three ccRCC subtypes with distinct clinicopathological features and prognoses. VHL, MUC16, FBN2, and FLG were found to have different mutation frequencies in these radiomic subtypes. In addition, transcriptome analysis revealed that the dysregulation of cell cycle-related pathways may be responsible for the distinct clinical significance of the obtained subtypes. The prognostic value of the radiomic subtypes was further validated in another independent cohort (log-rank P = 0.015). Conclusion In the present multi-scale radiogenomic analysis of ccRCC, radiomics played a central role. Radiomic subtypes could help discern genomic alterations and non-invasively stratify ccRCC patients.
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Affiliation(s)
- Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Yi-Qun Lin
- Department of Radiology, The Affiliated Dongnan Hospital of Xiamen University, Zhangzhou, Fujian Province 363020, China
| | - Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
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Subedi P, Gomolka M, Moertl S, Dietz A. Ionizing Radiation Protein Biomarkers in Normal Tissue and Their Correlation to Radiosensitivity: A Systematic Review. J Pers Med 2021; 11:jpm11020140. [PMID: 33669522 PMCID: PMC7922485 DOI: 10.3390/jpm11020140] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/05/2021] [Accepted: 02/14/2021] [Indexed: 12/16/2022] Open
Abstract
Background and objectives: Exposure to ionizing radiation (IR) has increased immensely over the past years, owing to diagnostic and therapeutic reasons. However, certain radiosensitive individuals show toxic enhanced reaction to IR, and it is necessary to specifically protect them from unwanted exposure. Although predicting radiosensitivity is the way forward in the field of personalised medicine, there is limited information on the potential biomarkers. The aim of this systematic review is to identify evidence from a range of literature in order to present the status quo of our knowledge of IR-induced changes in protein expression in normal tissues, which can be correlated to radiosensitivity. Methods: Studies were searched in NCBI Pubmed and in ISI Web of Science databases and field experts were consulted for relevant studies. Primary peer-reviewed studies in English language within the time-frame of 2011 to 2020 were considered. Human non-tumour tissues and human-derived non-tumour model systems that have been exposed to IR were considered if they reported changes in protein levels, which could be correlated to radiosensitivity. At least two reviewers screened the titles, keywords, and abstracts of the studies against the eligibility criteria at the first phase and full texts of potential studies at the second phase. Similarly, at least two reviewers manually extracted the data and accessed the risk of bias (National Toxicology Program/Office for Health Assessment and Translation—NTP/OHAT) for the included studies. Finally, the data were synthesised narratively in accordance to synthesis without meta analyses (SWiM) method. Results: In total, 28 studies were included in this review. Most of the records (16) demonstrated increased residual DNA damage in radiosensitive individuals compared to normo-sensitive individuals based on γH2AX and TP53BP1. Overall, 15 studies included proteins other than DNA repair foci, of which five proteins were selected, Vascular endothelial growth factor (VEGF), Caspase 3, p16INK4A (Cyclin-dependent kinase inhibitor 2A, CDKN2A), Interleukin-6, and Interleukin-1β, that were connected to radiosensitivity in normal tissue and were reported at least in two independent studies. Conclusions and implication of key findings: A majority of studies used repair foci as a tool to predict radiosensitivity. However, its correlation to outcome parameters such as repair deficient cell lines and patients, as well as an association to moderate and severe clinical radiation reactions, still remain contradictory. When IR-induced proteins reported in at least two studies were considered, a protein network was discovered, which provides a direction for further studies to elucidate the mechanisms of radiosensitivity. Although the identification of only a few of the commonly reported proteins might raise a concern, this could be because (i) our eligibility criteria were strict and (ii) radiosensitivity is influenced by multiple factors. Registration: PROSPERO (CRD42020220064).
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Shui L, Ren H, Yang X, Li J, Chen Z, Yi C, Zhu H, Shui P. The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front Oncol 2021; 10:570465. [PMID: 33575207 PMCID: PMC7870863 DOI: 10.3389/fonc.2020.570465] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/08/2020] [Indexed: 02/05/2023] Open
Abstract
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.
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Affiliation(s)
- Lin Shui
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haoyu Ren
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany
| | - Xi Yang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jian Li
- Department of Pharmacy, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Ziwei Chen
- Department of Nephrology, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, China
| | - Cheng Yi
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhu
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pixian Shui
- School of Pharmacy, Southwest Medical University, Luzhou, China
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Vinnikov V, Hande MP, Wilkins R, Wojcik A, Zubizarreta E, Belyakov O. Prediction of the Acute or Late Radiation Toxicity Effects in Radiotherapy Patients Using Ex Vivo Induced Biodosimetric Markers: A Review. J Pers Med 2020; 10:E285. [PMID: 33339312 PMCID: PMC7766345 DOI: 10.3390/jpm10040285] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/02/2020] [Accepted: 12/11/2020] [Indexed: 12/14/2022] Open
Abstract
A search for effective methods for the assessment of patients' individual response to radiation is one of the important tasks of clinical radiobiology. This review summarizes available data on the use of ex vivo cytogenetic markers, typically used for biodosimetry, for the prediction of individual clinical radiosensitivity (normal tissue toxicity, NTT) in cells of cancer patients undergoing therapeutic irradiation. In approximately 50% of the relevant reports, selected for the analysis in peer-reviewed international journals, the average ex vivo induced yield of these biodosimetric markers was higher in patients with severe reactions than in patients with a lower grade of NTT. Also, a significant correlation was sometimes found between the biodosimetric marker yield and the severity of acute or late NTT reactions at an individual level, but this observation was not unequivocally proven. A similar controversy of published results was found regarding the attempts to apply G2- and γH2AX foci assays for NTT prediction. A correlation between ex vivo cytogenetic biomarker yields and NTT occurred most frequently when chromosome aberrations (not micronuclei) were measured in lymphocytes (not fibroblasts) irradiated to relatively high doses (4-6 Gy, not 2 Gy) in patients with various grades of late (not early) radiotherapy (RT) morbidity. The limitations of existing approaches are discussed, and recommendations on the improvement of the ex vivo cytogenetic testing for NTT prediction are provided. However, the efficiency of these methods still needs to be validated in properly organized clinical trials involving large and verified patient cohorts.
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Affiliation(s)
- Volodymyr Vinnikov
- S.P. Grigoriev Institute for Medical Radiology and Oncology, National Academy of Medical Science of Ukraine, 61024 Kharkiv, Ukraine
| | - Manoor Prakash Hande
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, MD9, 2 Medical Drive, Singapore 117593, Singapore;
| | - Ruth Wilkins
- Consumer and Clinical Radiation Protection Bureau, Health Canada, 775 Brookfield Road, Ottawa, ON K1A 1C1, Canada;
| | - Andrzej Wojcik
- Centre for Radiation Protection Research, MBW Department, Stockholm University, Svante Arrhenius väg 20C, Room 515, 10691 Stockholm, Sweden;
| | - Eduardo Zubizarreta
- Section of Applied Radiation Biology and Radiotherapy, Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria;
| | - Oleg Belyakov
- Section of Applied Radiation Biology and Radiotherapy, Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria;
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Porcu M, Solinas C, Mannelli L, Micheletti G, Lambertini M, Willard-Gallo K, Neri E, Flanders AE, Saba L. Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol 2020; 154:103068. [PMID: 32805498 DOI: 10.1016/j.critrevonc.2020.103068] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/13/2020] [Accepted: 07/23/2020] [Indexed: 02/06/2023] Open
Abstract
In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy.
| | - Cinzia Solinas
- Medical Oncology, Azienda Tutela Salute Sardegna, Hospital Antonio Segni, Ozieri, SS, Italy
| | | | - Giulio Micheletti
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy
| | - Matteo Lambertini
- Department of Medical Oncology, U.O.C. Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genova, Italy; Department of Internal Medicine and Medical Specialties (DiMI), School of Medicine, University of Genova, Genova, Italy
| | | | | | - Adam E Flanders
- Department of Radiology, Division of Neuroradiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Luca Saba
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy
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Cao X, Xia W, Tang Y, Zhang B, Yang J, Zeng Y, Geng D, Zhang J. Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging. J Stroke Cerebrovasc Dis 2020; 29:105268. [PMID: 32992167 DOI: 10.1016/j.jstrokecerebrovasdis.2020.105268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/07/2020] [Accepted: 08/20/2020] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE To build radiomic model in differentiating dissecting aneurysm (DA) from complicated saccular aneurysm (SA) based on high-resolution magnetic resonance imaging (HR-MRI) through machine-learning algorithm. METHODS Overall, 851 radiomic features from 77 cases were retrospectively analyzed, and the ElasticNet algorithm was used to build the radiomic model. A clinico-radiological model using clinical features and conventional MRI findings was also built. An integrated model was then built by incorporating the radiomic model and clinico-radiological model. The diagnostic abilities of these models were evaluated using leave one out cross validation and quantified using the receiver operating characteristic (ROC) analysis. The diagnostic performance of radiologists was also evaluated for comparison. RESULTS Five features were used to form the radiomic model, which yielded an area under the ROC curve (AUC) of 0.912 (95 % CI 0.846-0.976), sensitivity of 0.852, and specificity of 0.861. The radiomic model achieved a better diagnostic performance than the clinico-radiological model (AUC=0.743, 95 % CI 0.623-0.862), integrated model (AUC=0.888, 95 % CI 0.811-0.965), and even many radiologists. CONCLUSION Radiomic features derived from HR-MRI can reliably be used to build a radiomic model for effectively differentiating between DA and complicated SA, and it can provide an objective basis for the selection of clinical treatment plan.
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Affiliation(s)
- Xin Cao
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Wei Xia
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; Academy for Engineering and Technology, Fudan University, 20 Handan Road, Yangpu District, Shanghai 200433, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou New District, Suzhou 215163, Jiangsu, China
| | - Ye Tang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Bo Zhang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Jinming Yang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Yanwei Zeng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China.
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China.
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Biolatti V, Negrin L, Bellora N, Ibañez IL. High-throughput meta-analysis and validation of differentially expressed genes as potential biomarkers of ionizing radiation-response. Radiother Oncol 2020; 154:21-28. [PMID: 32931891 DOI: 10.1016/j.radonc.2020.09.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 08/20/2020] [Accepted: 09/06/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND AND PURPOSE The high-throughput analysis of gene expression in ionizing radiation (IR)-exposed human peripheral white blood cells (WBC) has emerged as a novel method for biodosimetry markers detection. We aimed to detect IR-exposure differential expressed genes (DEGs) as potential predictive biomarkers for biodosimetry and radioinduced-response. MATERIALS AND METHODS We performed a meta-analysis of raw data from public microarrays of ex vivo low linear energy transfer-irradiated human peripheral WBC. Functional enrichment and transcription factors (TF) detection from resulting DEGs were assessed. Six selected DEGs among studies were validated by qRT-PCR on mRNA from human peripheral blood samples from nine healthy human donors 24 h after ex vivo X-rays-irradiation. RESULTS We identified 275 DEGs after IR-exposure (parameters: |lfc| ≥ 0.7, q value <0.05), enriched in processes such as regulation after IR-exposure, DNA damage checkpoint, signal transduction by p53 and mitotic cell cycle checkpoint. Among these DEGs, DRAM1, NUDT15, PCNA, PLK2 and TIGAR were selected for qRT-PCR validation. Their expression levels significantly increased at 1-4 Gy respect to non-irradiated controls. Particularly, PCNA increased dose dependently. Curiously, TCF4 (Entrez Gene: 6925), detected as overrepresented TF in the radioinduced DEGs set, significantly decreased post-irradiation. CONCLUSION These six DEGs show potential to be proposed as candidates for IR-exposure biomarkers, considering their observed molecular radioinduced-response. Among them, TCF4, bioinformatically detected, was validated herein as an IR-responsive gene.
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Affiliation(s)
- Vanesa Biolatti
- National Atomic Energy Commission (CNEA), Bariloche Nuclear Medicine and Radiotherapy Integral Center - Institute of Nuclear Technologies for Health Foundation (INTECNUS); Laboratory of Radiobiology and Biodosimetry, S.C. de Bariloche, Argentina.
| | - Lara Negrin
- National Atomic Energy Commission (CNEA), Bariloche Nuclear Medicine and Radiotherapy Integral Center - Institute of Nuclear Technologies for Health Foundation (INTECNUS); Laboratory of Radiobiology and Biodosimetry, S.C. de Bariloche, Argentina.
| | - Nicolás Bellora
- National Scientific and Technical Research Council (CONICET), Scientific Technical Center CONICET - North Patagonia, Patagonian Andean Institute of Biological and Geo-Environmental Technologies (IPATEC), S.C. de Bariloche, Argentina.
| | - Irene L Ibañez
- National Scientific and Technical Research Council (CONICET), Institute of Nanocience and Nanotechnology (INN), Constituyentes Node (C1425FQB), CABA, Argentina; National Atomic Energy Commission (CNEA), Constituyentes Atomic Center, Research and Applications Management, Buenos Aires, Argentina.
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Kim G, Kim J, Cha H, Park WY, Ahn JS, Ahn MJ, Park K, Park YJ, Choi JY, Lee KH, Lee SH, Moon SH. Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations. Sci Rep 2020; 10:13231. [PMID: 32764738 PMCID: PMC7411040 DOI: 10.1038/s41598-020-70168-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/24/2020] [Indexed: 12/22/2022] Open
Abstract
This study investigated the associations between image features extracted from tumor 18F-fluorodeoxyglucose (FDG) uptake and genetic alterations in patients with lung cancer. A total of 137 patients (age, 62.7 ± 10.2 years) who underwent FDG positron emission tomography/computed tomography (PET/CT) and targeted deep sequencing analysis for a tumor lesion, comprising 61 adenocarcinoma (ADC), 31 squamous cell carcinoma (SQCC), and 45 small cell lung cancer (SCLC) patients, were enrolled in this study. From the tumor lesions, 86 image features were extracted, and 381 genes were assessed. PET features were associated with genetic mutations: 41 genes with 24 features in ADC; 35 genes with 22 features in SQCC; and 43 genes with 25 features in SCLC (FDR < 0.05). Clusters based on PET features showed an association with alterations in oncogenic signaling pathways: Cell cycle and WNT signaling pathways in ADC (p = 0.023, p = 0.035, respectively); Cell cycle, p53, and WNT in SQCC (p = 0.045, 0.009, and 0.029, respectively); and TGFβ in SCLC (p = 0.030). In addition, SUVpeak and SUVmax were associated with a mutation of the TGFβ signaling pathway in ADC (FDR = 0.001, < 0.001). In this study, PET image features had significant associations with alterations in genes and oncogenic signaling pathways in patients with lung cancer.
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Affiliation(s)
- Gahyun Kim
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jinho Kim
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hongui Cha
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Samsung Advanced Institute of Health Science and Technology, Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin Seok Ahn
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Keunchil Park
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yong-Jin Park
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Kyung-Han Lee
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Se-Hoon Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. .,Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Seung Hwan Moon
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea.
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Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade. AJR Am J Roentgenol 2020; 215:963-969. [PMID: 32755226 DOI: 10.2214/ajr.19.22147] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE. The purpose of this study was to assess the value of radiomics features for differentiating soft tissue sarcomas (STSs) of different histopathologic grades. MATERIALS AND METHODS. The T1-weighted and fat-suppressed T2-weighted MR images of 70 STSs of varying grades (35 low-grade [grades 1 and 2], 35 high-grade [grade 3]) formed the primary dataset used to train multiple machine learning algorithms for the construction of models for assigning STS grade. The models were tested with a separate validation dataset. RESULTS. Different machine learning algorithms had different strengths and weaknesses. The best classification algorithm for the prediction of STS grade had a combination of the least absolute shrinkage and selection operator feature selection method and the random forest classification algorithm (AUC, 0.9216; 95% CI, 0.8437-0.9995) in the validation set. The accuracy of the combined methods applied to the validation set was 91.43%; sensitivity, 88.24%; and specificity, 94.44%. CONCLUSION. Because of tumor heterogeneity, initial biopsy grade may be an underestimate of the final grade identified in extensive histopathologic analysis of surgical specimens. This creates an urgent need to construct an accurate preoperative approach to grading STS. This radiomics study revealed the optimal machine learning approaches for differentiating STS grades. This capability can enhance the precision of preoperative diagnosis.
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Kang J, Coates JT, Strawderman RL, Rosenstein BS, Kerns SL. Genomics models in radiotherapy: From mechanistic to machine learning. Med Phys 2020; 47:e203-e217. [PMID: 32418335 PMCID: PMC8725063 DOI: 10.1002/mp.13751] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/28/2019] [Accepted: 07/17/2019] [Indexed: 12/28/2022] Open
Abstract
Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.
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Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - James T. Coates
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert L. Strawderman
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA
| | - Barry S. Rosenstein
- Department of Radiation Oncology and the Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah L. Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14642, USA
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Park YJ, Shin MH, Moon SH. Radiogenomics Based on PET Imaging. Nucl Med Mol Imaging 2020; 54:128-138. [PMID: 32582396 DOI: 10.1007/s13139-020-00642-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 04/02/2020] [Accepted: 04/30/2020] [Indexed: 02/07/2023] Open
Abstract
Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.
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Affiliation(s)
- Yong-Jin Park
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Mu Heon Shin
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
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Burnet NG, Mackay RI, Smith E, Chadwick AL, Whitfield GA, Thomson DJ, Lowe M, Kirkby NF, Crellin AM, Kirkby KJ. Proton beam therapy: perspectives on the National Health Service England clinical service and research programme. Br J Radiol 2020; 93:20190873. [PMID: 31860337 PMCID: PMC7066938 DOI: 10.1259/bjr.20190873] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/05/2019] [Accepted: 12/16/2019] [Indexed: 12/19/2022] Open
Abstract
The UK has an important role in the evaluation of proton beam therapy (PBT) and takes its place on the world stage with the opening of the first National Health Service (NHS) PBT centre in Manchester in 2018, and the second in London coming in 2020. Systematic evaluation of the role of PBT is a key objective. By September 2019, 108 patients had started treatment, 60 paediatric, 19 teenagers and young adults and 29 adults. Obtaining robust outcome data is vital, if we are to understand the strengths and weaknesses of current treatment approaches. This is important in demonstrating when PBT will provide an advantage and when it will not, and in quantifying the magnitude of benefit.The UK also has an important part to play in translational PBT research, and building a research capability has always been the vision. We are perfectly placed to perform translational pre-clinical biological and physical experiments in the dedicated research room in Manchester. The nature of DNA damage from proton irradiation is considerably different from X-rays and this needs to be more fully explored. A better understanding is needed of the relative biological effectiveness (RBE) of protons, especially at the end of the Bragg peak, and of the effects on tumour and normal tissue of PBT combined with conventional chemotherapy, targeted drugs and immunomodulatory agents. These experiments can be enhanced by deterministic mathematical models of the molecular and cellular processes of DNA damage response. The fashion of ultra-high dose rate FLASH irradiation also needs to be explored.
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Affiliation(s)
| | | | - Ed Smith
- The Christie NHS Foundation Trust, Manchester, and University of Manchester, M20 4BX, UK
| | - Amy L Chadwick
- Division of Cancer Sciences, University of Manchester, Manchester Cancer Research Centre, Manchester Academic Health Science Centre, and The Christie NHS Foundation Trust, Manchester, M20 4BX, UK
| | - Gillian A Whitfield
- The Christie NHS Foundation Trust, Manchester, and University of Manchester, M20 4BX, UK
| | - David J Thomson
- The Christie NHS Foundation Trust, Manchester, and University of Manchester, M20 4BX, UK
| | | | - Norman F Kirkby
- Division of Cancer Sciences, University of Manchester, Manchester Cancer Research Centre, Manchester Academic Health Science Centre, and The Christie NHS Foundation Trust, Manchester, M20 4BX, UK
| | | | - Karen J Kirkby
- Division of Cancer Sciences, University of Manchester, Manchester Cancer Research Centre, Manchester Academic Health Science Centre, and The Christie NHS Foundation Trust, Manchester, M20 4BX, UK
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Wang H, Zhang J, Bao S, Liu J, Hou F, Huang Y, Chen H, Duan S, Hao D, Liu J. Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study. J Magn Reson Imaging 2020; 52:873-882. [PMID: 32112598 DOI: 10.1002/jmri.27111] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 02/16/2020] [Accepted: 02/18/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Preoperative differentiation between malignant and benign soft-tissue masses is important for treatment decisions. PURPOSE/HYPOTHESIS To construct/validate a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses. STUDY TYPE Retrospective. POPULATION In all, 206 cases. FIELD STRENGTH/SEQUENCE The T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352-550/2.75-19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700-6370/40-120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort. ASSESSMENT Twelve machine-learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively). STATISTICAL TESTS 1) Demographic characteristics: a one-way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) values. RESULTS The LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2. DATA CONCLUSION A machine-learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft-tissue masses. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:873-882.
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Affiliation(s)
- Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jian Zhang
- Department of General Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shan Bao
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, Shandong, China
| | - Jingwei Liu
- Department of Pediatric Surgery, Shandong University Qilu Hospital, Jinan, Shandong, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang City Oilfield General Hospital, Puyang, Henan, China
| | - Haisong Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | | | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jihua Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Abstract
CLINICAL ISSUE The reproducible and exhaustive extraction of information from radiological images is a central task in the practice of radiology. Dynamic developments in the fields of artificial intelligence (AI) and machine learning are introducing new methods for this task. Radiomics is one such method and offers new opportunities and challenges for the future of radiology. METHODOLOGICAL INNOVATIONS Radiomics describes the quantitative evaluation, interpretation, and clinical assessment of imaging markers in radiological data. Components of a radiomics analysis are data acquisition, data preprocessing, data management, segmentation of regions of interest, computation and selection of imaging markers, as well as the development of a radiomics model used for diagnosis and prognosis. This article explains these components and aims at providing an introduction to the field of radiomics while highlighting existing limitations. MATERIALS AND METHODS This article is based on a selective literature search with the PubMed search engine. ASSESSMENT Even though radiomics applications have yet to arrive in routine clinical practice, the quantification of radiological data in terms of radiomics is underway and will increase in the future. This holds the potential for lasting change in the discipline of radiology. Through the successful extraction and interpretation of all the information encoded in radiological images the next step in the direction of a more personalized, future-oriented form of medicine can be taken.
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Affiliation(s)
- Jacob M Murray
- AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland.,Heidelberg University, Heidelberg, Deutschland
| | - Georgios Kaissis
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland
| | - Rickmer Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland
| | - Jens Kleesiek
- AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. .,German Cancer Consortium (DKTK), Heidelberg, Deutschland.
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Averbeck D, Candéias S, Chandna S, Foray N, Friedl AA, Haghdoost S, Jeggo PA, Lumniczky K, Paris F, Quintens R, Sabatier L. Establishing mechanisms affecting the individual response to ionizing radiation. Int J Radiat Biol 2020; 96:297-323. [PMID: 31852363 DOI: 10.1080/09553002.2019.1704908] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Purpose: Humans are increasingly exposed to ionizing radiation (IR). Both low (<100 mGy) and high doses can cause stochastic effects, including cancer; whereas doses above 100 mGy are needed to promote tissue or cell damage. 10-15% of radiotherapy (RT) patients suffer adverse reactions, described as displaying radiosensitivity (RS). Sensitivity to IR's stochastic effects is termed radiosusceptibility (RSu). To optimize radiation protection we need to understand the range of individual variability and underlying mechanisms. We review the potential mechanisms contributing to RS/RSu focusing on RS following RT, the most tractable RS group.Conclusions: The IR-induced DNA damage response (DDR) has been well characterized. Patients with mutations in the DDR have been identified and display marked RS but they represent only a small percentage of the RT patients with adverse reactions. We review the impacting mechanisms and additional factors influencing RS/RSu. We discuss whether RS/RSu might be genetically determined. As a recommendation, we propose that a prospective study be established to assess RS following RT. The study should detail tumor site and encompass a well-defined grading system. Predictive assays should be independently validated. Detailed analysis of the inflammatory, stress and immune responses, mitochondrial function and life style factors should be included. Existing cohorts should also be optimally exploited.
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Affiliation(s)
| | - Serge Candéias
- CEA, CNRS, LCMB, University of Grenoble Alpes, Grenoble, France
| | - Sudhir Chandna
- Division of Radiation Biosciences, Institute of Nuclear Medicine & Allied Sciences, Delhi, India
| | - Nicolas Foray
- Inserm UA8 Unit Radiations: Defense, Health and Environment, Lyon, France
| | - Anna A Friedl
- Department of Radiation Oncology, University Hospital, LMU, Munich, Germany
| | - Siamak Haghdoost
- Cimap-Laria, Advanced Resource Center for HADrontherapy in Europe (ARCHADE,), University of Caen Normandy, France.,Centre for Radiation Protection Research, Department of Molecular Bioscience, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Penelope A Jeggo
- Genome Damage and Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK
| | - Katalin Lumniczky
- Department of Radiation Medicine, Division of Radiobiology and Radiohygiene, National Public Health Center, Budapest, Hungary
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Prasanna PGS, Narayanan D, Zhang K, Rahbar A, Coleman CN, Vikram B. Radiation Biomarkers: Can Small Businesses Drive Accurate Radiation Precision Medicine? Radiat Res 2020; 193:199-208. [PMID: 31910120 DOI: 10.1667/rr15553.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Radiation therapy is an essential component of cancer treatment. Currently, tumor control and normal tissue complication probabilities derived from a general patient population guide radiation treatment. Its outcome could be improved if radiation biomarkers could be incorporated into approaches to treatment. A substantial number of cancer patients suffer from side effects of radiation therapy. These side effects can result in treatment interruption. Such unplanned treatment interruptions not only jeopardize anticancer treatment efficacy but also result in poor post-treatment quality-of-life. To develop and translate radiation biomarkers for clinical use, NCI's Radiation Research Program, in collaboration with the Small Business Innovation Research Development Center, funded four small businesses through the request for proposals after peer review during 2015-2019. Here, we summarize publicly available information on intellectual property rights, the status of development, ongoing clinical trials, success in obtaining financing and regulatory approval. An analysis of publicly available information indicates all four companies have completed phase I of SBIR funding and advanced to further development, validation and clinical trials with phase II SBIR funding. These biomarkers are: 1. A panel of genomic biomarkers of radiation response to predict toxicity and radioimmune response (MiraDx Inc., Los Angeles, CA); 2. A multiplex assay for single nucleotide polymorphism (SNP) biomarkers of radiation sensitivity to identify a subset of prostate cancer patients for which radiotherapy is contraindicated (L2 Diagnostics, New Haven, CT); 3. A cell-free DNA assay in blood to measure tissue damage shortly after radiation exposure (DiaCarta Inc., Richmond, CA); and 4. A metabolomic/lipidomic assay to predict late effects that adversely affect quality-of-life among patients treated with radiation for prostate cancer (Shuttle Pharmaceuticals, Rockville, MD). This work also provides a bird's eye view of the process of developing radiation biomarkers for use in radiation oncology clinics, some of the challenges and future directions.
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Affiliation(s)
| | - Deepa Narayanan
- Division of Small Business Innovation Research (SBIR) Development Center, National Cancer Institute, Bethesda, Maryland 20892
| | - Kehui Zhang
- Division of Small Business Innovation Research (SBIR) Development Center, National Cancer Institute, Bethesda, Maryland 20892
| | - Amir Rahbar
- Division of Small Business Innovation Research (SBIR) Development Center, National Cancer Institute, Bethesda, Maryland 20892
| | - C Norman Coleman
- Division of Cancer Treatment and Diagnosis, Radiation Research Program
| | - Bhadrasain Vikram
- Division of Cancer Treatment and Diagnosis, Radiation Research Program
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Kiser KJ, Smith BD, Wang J, Fuller CD. "Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era. Front Oncol 2019; 9:983. [PMID: 31632914 PMCID: PMC6779062 DOI: 10.3389/fonc.2019.00983] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/16/2019] [Indexed: 12/17/2022] Open
Abstract
Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.
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Affiliation(s)
- Kendall J Kiser
- John P. and Kathrine G. McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States.,School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.,Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin D Smith
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jihong Wang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Giuranno L, Ient J, De Ruysscher D, Vooijs MA. Radiation-Induced Lung Injury (RILI). Front Oncol 2019; 9:877. [PMID: 31555602 PMCID: PMC6743286 DOI: 10.3389/fonc.2019.00877] [Citation(s) in RCA: 183] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 08/23/2019] [Indexed: 12/12/2022] Open
Abstract
Radiation pneumonitis (RP) and radiation fibrosis (RF) are two dose-limiting toxicities of radiotherapy (RT), especially for lung, and esophageal cancer. It occurs in 5-20% of patients and limits the maximum dose that can be delivered, reducing tumor control probability (TCP) and may lead to dyspnea, lung fibrosis, and impaired quality of life. Both physical and biological factors determine the normal tissue complication probability (NTCP) by Radiotherapy. A better understanding of the pathophysiological sequence of radiation-induced lung injury (RILI) and the intrinsic, environmental and treatment-related factors may aid in the prevention, and better management of radiation-induced lung damage. In this review, we summarize our current understanding of the pathological and molecular consequences of lung exposure to ionizing radiation, and pharmaceutical interventions that may be beneficial in the prevention or curtailment of RILI, and therefore enable a more durable therapeutic tumor response.
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Affiliation(s)
- Lorena Giuranno
- Department of Radiotherapy, GROW School for Oncology Maastricht University Medical Centre, Maastricht, Netherlands
| | - Jonathan Ient
- Department of Radiotherapy, GROW School for Oncology Maastricht University Medical Centre, Maastricht, Netherlands
| | - Dirk De Ruysscher
- Department of Radiotherapy, GROW School for Oncology Maastricht University Medical Centre, Maastricht, Netherlands
| | - Marc A Vooijs
- Department of Radiotherapy, GROW School for Oncology Maastricht University Medical Centre, Maastricht, Netherlands
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Gomolka M, Blyth B, Bourguignon M, Badie C, Schmitz A, Talbot C, Hoeschen C, Salomaa S. Potential screening assays for individual radiation sensitivity and susceptibility and their current validation state. Int J Radiat Biol 2019; 96:280-296. [PMID: 31347938 DOI: 10.1080/09553002.2019.1642544] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Purpose: The workshop on 'Individual Radiosensitivity and Radiosusceptibility' organized by MELODI and CONCERT on Malta in 2018, evaluated the current state of assays to identify sensitive and susceptible subgroups. The authors provide an overview on potential screening assays detecting individuals showing moderate to severe early and late radiation reactions or are at increased risk to develop cancer upon radiation exposure.Conclusion: It is necessary to separate clearly between tissue reactions and stochastic effects such as cancer when comparing the existing literature to validate various test systems. Requirements for the assays are set up. The literature is reviewed for assays that are reliable and robust. Sensitivity and specificity of the assays are regarded and scrutinized for modifying factors. Accuracy of an assay system is required to be more than 90% to balance risks of adverse reactions against risk to fail to cure the cancer. No assay/biomarker is in routine use. Assays that have shown predictive potential for radiosensitivity include SNPs, the RILA assay, and the pATM assay. A tree of risk guideline for radiologists is provided to assist medical treatment decisions. Recommendations for effective research include the setup of common retrospective and prospective cohorts/biobanks to validate current and future tests.
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Affiliation(s)
- Maria Gomolka
- Federal Office for Radiation Protection, Neuherberg, Germany
| | - Benjamin Blyth
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | | | - Christophe Badie
- Cancer Mechanisms and Biomarkers Group, Radiation Effects Department Centre for Radiation, Chemical and Environmental Hazards Public Health England, Didcot, United Kingdom
| | - Annette Schmitz
- Institut de Radiobiologie Cellulaire et Moléculaire, Institut de Biologie François Jacob, Direction de la Recherche Fondamentale, CEA, Paris, France
| | - Christopher Talbot
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Christoph Hoeschen
- Faculty of Electrical Engineering and Information Technology, Institute for Medical Technology, Otto-von-Guericke-University, Magdeburg, Germany
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Qian Z, Li Y, Wang Y, Li L, Li R, Wang K, Li S, Tang K, Zhang C, Fan X, Chen B, Li W. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett 2019; 451:128-135. [DOI: 10.1016/j.canlet.2019.02.054] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 01/26/2019] [Accepted: 02/28/2019] [Indexed: 12/22/2022]
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Huang L, Chen J, Hu W, Xu X, Liu D, Wen J, Lu J, Cao J, Zhang J, Gu Y, Wang J, Fan M. Assessment of a Radiomic Signature Developed in a General NSCLC Cohort for Predicting Overall Survival of ALK-Positive Patients With Different Treatment Types. Clin Lung Cancer 2019; 20:e638-e651. [PMID: 31375452 DOI: 10.1016/j.cllc.2019.05.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/08/2019] [Accepted: 05/03/2019] [Indexed: 12/24/2022]
Abstract
BACKGROUND The purpose of the study was to investigate the potential of a radiomic signature developed in a general non-small-cell lung cancer (NSCLC) cohort for predicting the overall survival of anaplastic lymphoma kinase (ALK)-positive (ALK+) patients with different treatment types. MATERIALS AND METHODS After test-retest in the Reference Image Database to Evaluate Therapy Response data set, 132 features (intraclass correlation coefficient > 0.9) were selected in the least absolute shrinkage and selection operator Cox regression model with a leave-one-out cross-validation. The NSCLC radiomics collection from The Cancer Imaging Archive was randomly divided into a training set (n = 254) and a validation set (n = 63) to develop a general radiomic signature for NSCLC. In our ALK+ set, 35 patients received targeted therapy and 19 patients received nontargeted therapy. The developed signature was tested later in this ALK+ set. Performance of the signature was evaluated with the concordance index (C-index) and stratification analysis. RESULTS The general signature had good performance (C-index > 0.6; log rank P < .05) in the NSCLC radiomics collection. It includes 5 features: Geom_va_ratio, W_GLCM_Std, W_GLCM_DV, W_GLCM_IM2, and W_his_mean. Its accuracy of predicting overall survival in the ALK+ set achieved 0.649 (95% confidence interval [CI], 0.640-0.658). Nonetheless, impaired performance was observed in the targeted therapy group (C-index = 0.573; 95% CI, 0.556-0.589) whereas significantly improved performance was observed in the nontargeted therapy group (C-index = 0.832; 95% CI, 0.832-0.852). Stratification analysis also showed that the general signature could only identify high- and low-risk patients in the nontargeted therapy group (log rank P = .00028). CONCLUSION This preliminary study suggests that the applicability of a general signature to ALK+ patients is limited. The general radiomic signature seems to be only applicable to ALK+ patients who had received nontargeted therapy, which indicates that developing special radiomics signatures for patients treated with tyrosine kinase inhibitors might be necessary.
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Affiliation(s)
- Lyu Huang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jiayan Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xinyan Xu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Di Liu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Junmiao Wen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jiayu Lu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jianzhao Cao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Junhua Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yu Gu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Min Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
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Brothwell MRS, West CM, Dunning AM, Burnet NG, Barnett GC. Radiogenomics in the Era of Advanced Radiotherapy. Clin Oncol (R Coll Radiol) 2019; 31:319-325. [PMID: 30914148 DOI: 10.1016/j.clon.2019.02.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 02/15/2019] [Indexed: 10/27/2022]
Abstract
Most radiogenomics studies investigate how genetic variation can help to explain the differences in early and late radiotherapy toxicity between individuals. The field of radiogenomics in photon beam therapy has grown rapidly in recent years, carving out a unique translational discipline, which has progressed from candidate gene studies to larger scale genome-wide association studies, meta-analyses and now prospective validation studies. Genotyping is increasingly sophisticated and affordable, and whole-genome sequencing may soon become readily available as a diagnostic tool in the clinic. The ultimate aim of radiogenomics research is to tailor treatment to the individual with a test based on a combination of treatment, clinical and genetic factors. This personalisation would allow the greatest tumour control while minimising acute and long-term toxicity. Here we discuss the evolution of the field of radiogenomics with reference to the most recent developments and challenges.
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Affiliation(s)
- M R S Brothwell
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - C M West
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Christie Hospital, Manchester, UK
| | - A M Dunning
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - N G Burnet
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Christie Hospital, Manchester, UK
| | - G C Barnett
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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Sone M, Arai Y, Sugawara S, Kubo T, Itou C, Hasegawa T, Umakoshi N, Yamamoto N, Sunami K, Hiraoka N, Kubo T. Feasibility of genomic profiling with next-generation sequencing using specimens obtained by image-guided percutaneous needle biopsy. Ups J Med Sci 2019; 124:119-124. [PMID: 31179853 PMCID: PMC6567228 DOI: 10.1080/03009734.2019.1607635] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Aims: The demand for specimen collection for genomic profiling is rapidly increasing in the era of personalized medicine. Percutaneous needle biopsy is recognized as minimally invasive, but the feasibility of comprehensive genomic analysis using next-generation sequencing (NGS) is not yet clear. The purpose of this study was to evaluate the feasibility of genomic analysis using NGS with specimens obtained by image-guided percutaneous needle biopsy with 18-G needles. Patients and methods: Forty-eight patients who participated in a clinical study of genomic profiling with NGS with the specimen obtained by image-guided needle biopsy were included. All biopsies were performed under local anesthesia, with imaging guidance, using an 18-G cutting needle. A retrospective chart review was performed to determine the rate of successful genomic analysis, technical success rate of biopsy procedure, adverse events, rate of success in pathological diagnosis, and cause of failed genomic analysis. Results: The success rate of genomic analysis was 79.2% (38/48). The causes of failure were unprocessed for DNA extraction due to insufficient specimen volume (6/10), insufficient DNA volume (2/10), and deteriorated DNA quality (2/10). The rate of successful genomic analysis excluding NGS analysis that failed for reasons unrelated to the biopsy procedures was 95.2% (40/42). Technical success of biopsy was achieved in all patients without severe adverse events. The rate of success in the pathological diagnosis was 97.9% (47/48). Conclusions: Image-guided needle biopsy specimens using an 18-G cutting needle yielded a successful NGS genomic analysis rate with no severe adverse events and could be an adoptable method for tissue sampling for NGS.
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Affiliation(s)
- Miyuki Sone
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
- CONTACT Miyuki Sone Department of Diagnostic Radiology, National Cancer Center, 5-1-1, Tsukiji, Chuo-ku, Tokyo1040045, Japan
| | - Yasuaki Arai
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Shunsuke Sugawara
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Takatoshi Kubo
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Chihiro Itou
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Tetsuya Hasegawa
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Noriyuki Umakoshi
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Noboru Yamamoto
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, Japan
| | - Kumiko Sunami
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan
| | - Nobuyoshi Hiraoka
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan
| | - Takashi Kubo
- Division of Translational Genomics, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Tokyo, Japan
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Abstract
Radiotherapy is used in >50% of patients with cancer, both for curative and palliative purposes. Radiotherapy uses ionizing radiation to target and kill tumour tissue, but normal tissue can also be damaged, leading to toxicity. Modern and precise radiotherapy techniques, such as intensity-modulated radiotherapy, may prevent toxicity, but some patients still experience adverse effects. The physiopathology of toxicity is dependent on many parameters, such as the location of irradiation or the functional status of organs at risk. Knowledge of the mechanisms leads to a more rational approach for controlling radiotherapy toxicity, which may result in improved symptom control and quality of life for patients. This improved quality of life is particularly important in paediatric patients, who may live for many years with the long-term effects of radiotherapy. Notably, signs and symptoms occurring after radiotherapy may not be due to the treatment but to an exacerbation of existing conditions or to the development of new diseases. Although differential diagnosis may be difficult, it has important consequences for patients.
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Andreassen C, Eriksen J, Jensen K, Hansen C, Sørensen B, Lassen P, Alsner J, Schack L, Overgaard J, Grau C. IMRT – Biomarkers for dose escalation, dose de-escalation and personalized medicine in radiotherapy for head and neck cancer. Oral Oncol 2018; 86:91-99. [DOI: 10.1016/j.oraloncology.2018.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 09/03/2018] [Indexed: 12/13/2022]
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A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy. JOURNAL OF RADIOTHERAPY IN PRACTICE 2018. [DOI: 10.1017/s1460396918000547] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
AbstractBackgroundThe success of radiation therapy for cancer patients is dependent on the ability to deliver a total tumouricidal radiation dose capable of eradicating all cancer cells within the clinical target volume, however, the radiation dose tolerance of the surrounding healthy tissues becomes the main dose-limiting factor. The normal tissue adverse effects following radiotherapy are common and significantly impact the quality of life of patients. The likelihood of developing these adverse effects following radiotherapy cannot be predicted based only on the radiation treatment parameters. However, there is evidence to suggest that some common genetic variants are associated with radiotherapy response and the risk of developing adverse effects. Radiation genomics is a field that has evolved in recent years investigating the association between patient genomic data and the response to radiation therapy. This field aims to identify genetic markers that are linked to individual radiosensitivity with the potential to predict the risk of developing adverse effects due to radiotherapy using patient genomic information. It also aims to determine the relative radioresponse of patients using their genetic information for the potential prediction of patient radiation treatment response.Methods and materialsThis paper reports on a review of recent studies in the field of radiation genomics investigating the association between genomic data and patients response to radiation therapy, including the investigation of the role of genetic variants on an individual’s predisposition to enhanced radiotherapy radiosensitivity or radioresponse.ConclusionThe potential for early prediction of treatment response and patient outcome is critical in cancer patients to make decisions regarding continuation, escalation, discontinuation, and/or change in treatment options to maximise patient survival while minimising adverse effects and maintaining patients’ quality of life.
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El Naqa I, Pandey G, Aerts H, Chien JT, Andreassen CN, Niemierko A, Ten Haken RK. Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation. Int J Radiat Oncol Biol Phys 2018; 102:1070-1073. [PMID: 30353869 DOI: 10.1016/j.ijrobp.2018.08.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 08/08/2018] [Accepted: 08/12/2018] [Indexed: 01/24/2023]
Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
| | - Gaurav Pandey
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Hugo Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jen-Tzung Chien
- Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan; Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | | | - Andrzej Niemierko
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging 2018; 46:446-454. [DOI: 10.1007/s00259-018-4138-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 08/16/2018] [Indexed: 12/11/2022]
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Liang S, Zhang R, Liang D, Song T, Ai T, Xia C, Xia L, Wang Y. Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas. Genes (Basel) 2018; 9:E382. [PMID: 30061525 PMCID: PMC6115744 DOI: 10.3390/genes9080382] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 07/06/2018] [Accepted: 07/16/2018] [Indexed: 01/08/2023] Open
Abstract
Non-invasive prediction of isocitrate dehydrogenase (IDH) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images. In this paper, we proposed a novel multimodal 3D DenseNet (M3D-DenseNet) model to predict IDH genotypes with multimodal magnetic resonance imaging (MRI) data. To evaluate its performance, we conducted experiments on the BRATS-2017 and The Cancer Genome Atlas breast invasive carcinoma (TCGA-BRCA) dataset to get image data as input and gene mutation information as the target, respectively. We achieved 84.6% accuracy (area under the curve (AUC) = 85.7%) on the validation dataset. To evaluate its generalizability, we applied transfer learning techniques to predict World Health Organization (WHO) grade status, which also achieved a high accuracy of 91.4% (AUC = 94.8%) on validation dataset. With the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multimodal radiogenomics problems and has the potential to be applied in clinical decision making.
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Affiliation(s)
- Sen Liang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun 130012, China.
| | - Rongguo Zhang
- Advanced Institute, Infervision, Beijing 100000, China.
| | - Dayang Liang
- School of Mechatronics Engineering, Nanchang University, Nanchang 330031, China.
| | - Tianci Song
- Advanced Institute, Infervision, Beijing 100000, China.
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Wuhan 430030, China.
| | - Chen Xia
- Advanced Institute, Infervision, Beijing 100000, China.
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Wuhan 430030, China.
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun 130012, China.
- Cancer Systems Biology Center, China-Japan Union Hospital, Jilin University, Changchun 130033, China.
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Kang J, Rancati T, Lee S, Oh JH, Kerns SL, Scott JG, Schwartz R, Kim S, Rosenstein BS. Machine Learning and Radiogenomics: Lessons Learned and Future Directions. Front Oncol 2018; 8:228. [PMID: 29977864 PMCID: PMC6021505 DOI: 10.3389/fonc.2018.00228] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 06/04/2018] [Indexed: 12/25/2022] Open
Abstract
Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.
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Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, United States
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Sangkyu Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah L. Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, United States
| | - Jacob G. Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, United States
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States
| | - Russell Schwartz
- Computational Biology Department, Carnegie Mellon School of Computer Science, Pittsburgh, PA, United States
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Seyoung Kim
- Computational Biology Department, Carnegie Mellon School of Computer Science, Pittsburgh, PA, United States
| | - Barry S. Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Rastogi A, Maheshwari S, Shinagare AB, Baheti AD. Computed Tomography Advances in Oncoimaging. Semin Roentgenol 2018; 53:147-156. [PMID: 29861006 DOI: 10.1053/j.ro.2018.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ashita Rastogi
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai, India
| | - Sharad Maheshwari
- Department of Radiology, Kokilaben Dhirubhai Ambani Hospital, Mumbai, India
| | - Atul B Shinagare
- Department of Radiology, Harvard Medical School, Dana-Farber Cancer Institute, Boston, MA
| | - Akshay D Baheti
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai, India.
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