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Qian LD, Zhou ZA, Li SQ, Liu J, Zhang SX, Ren JL, Wang W, Yang J. 18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category. Quant Imaging Med Surg 2024; 14:3131-3145. [PMID: 38617169 PMCID: PMC11007507 DOI: 10.21037/qims-23-494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 02/10/2024] [Indexed: 04/16/2024]
Abstract
Background The MYCN copy number category is closely related to the prognosis of neuroblastoma (NB). Therefore, this study aimed to assess the predictive ability of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features for MYCN copy number in NB. Methods A retrospective analysis was performed on 104 pediatric patients with NB that had been confirmed by pathology. To develop the Bio-omics model (B-model), which incorporated clinical and biological aspects, PET/CT radiographic features, PET quantitative parameters, and significant features with multivariable stepwise logistic regression were preserved. Important radiomics features were identified through least absolute shrinkage and selection operator (LASSO) and univariable analysis. On the basis of radiomics features obtained from PET and CT scans, the radiomics model (R-model) was developed. The significant bio-omics and radiomics features were combined to establish a Multi-omics model (M-model). The above 3 models were established to differentiate MYCN wild from MYCN gain and MYCN amplification (MNA). The calibration curve and receiver operating characteristic (ROC) curve analyses were performed to verify the prediction performance. Post hoc analysis was conducted to compare whether the constructed M-model can distinguish MYCN gain from MNA. Results The M-model showed excellent predictive performance in differentiating MYCN wild from MYCN gain and MNA, which was better than that of the B-model and R-model [area under the curve (AUC) 0.83, 95% confidence interval (CI): 0.74-0.92 vs. 0.81, 95% CI: 0.72-0.90 and 0.79, 95% CI: 0.69-0.89]. The calibration curve showed that the M-model had the highest reliability. Post hoc analysis revealed the great potential of the M-model in differentiating MYCN gain from MNA (AUC 0.95, 95% CI: 0.89-1). Conclusions The M-model model based on bio-omics and radiomics features is an effective tool to distinguish MYCN copy number category in pediatric patients with NB.
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Affiliation(s)
- Luo-Dan Qian
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zi-Ang Zhou
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Si-Qi Li
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jun Liu
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shu-Xin Zhang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Wei Wang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jigang Yang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Salehjahromi M, Karpinets TV, Sujit SJ, Qayati M, Chen P, Aminu M, Saad MB, Bandyopadhyay R, Hong L, Sheshadri A, Lin J, Antonoff MB, Sepesi B, Ostrin EJ, Toumazis I, Huang P, Cheng C, Cascone T, Vokes NI, Behrens C, Siewerdsen JH, Hazle JD, Chang JY, Zhang J, Lu Y, Godoy MCB, Chung C, Jaffray D, Wistuba I, Lee JJ, Vaporciyan AA, Gibbons DL, Gladish G, Heymach JV, Wu CC, Zhang J, Wu J. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Cell Rep Med 2024; 5:101463. [PMID: 38471502 PMCID: PMC10983039 DOI: 10.1016/j.xcrm.2024.101463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 09/07/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.
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Affiliation(s)
| | | | - Sheeba J Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Mohamed Qayati
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina B Saad
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX USA
| | - Julie Lin
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX USA
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Edwin J Ostrin
- Department of General Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Iakovos Toumazis
- Department of Health Services Research, MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Huang
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie I Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey H Siewerdsen
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - John D Hazle
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Myrna C B Godoy
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - David Jaffray
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | - J Jack Lee
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | - Ara A Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carol C Wu
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA; Lung Cancer Genomics Program, MD Anderson Cancer Center, Houston, TX, USA; Lung Cancer Interception Program, MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Xu C, Cao J, Zhou T. Radiogenomics uncovers an interplay between angiogenesis and clinical outcomes in bladder cancer. Environ Toxicol 2024; 39:1374-1387. [PMID: 37975603 DOI: 10.1002/tox.24038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/18/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Precision medicine has become a promising clinical treatment strategy for various cancers, including bladder cancer, where angiogenesis plays a critical role in cancer progression. However, the relationship between angiogenesis, immune cell infiltration, clinical outcomes, chemotherapy, and targeted therapy remains unclear. METHODS We conducted a comprehensive evaluation of angiogenesis-related genes (ARGs) to identify their association with immune cell infiltration, transcription patterns, and clinical outcomes in bladder cancer. An ARG score was constructed to identify angiogenic subgroups in each sample and we evaluated their predictive performance for overall survival rate and treatment response. In addition, we optimized existing clinical detection protocols by performing image data processing. RESULTS Our study revealed the genomic-level mutant landscape and expression patterns of ARGs in bladder cancer specimens. Using analysis, we identified three molecular subgroups where ARG mutations correlated with patients' pathological features, clinical outcomes, and immune cell infiltration. To facilitate clinical applicability, we constructed a precise nomogram based on the ARG score, which significantly correlated with stem cell index and drug sensitivity. Finally, we proposed the radiogenomics model, which combines the precision of genomics with the convenience of radiomics. CONCLUSION Our study sheds light on the prognostic characteristics of ARGs in bladder cancer and provides insights into the tumor environment's characteristics to explore more effective immunotherapy strategies. The findings have significant implications for the development of personalized treatment approaches in bladder cancer and pave the way for future studies in this field.
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Affiliation(s)
- Chentao Xu
- Radiology Department, Changxing People's Hospital, Huzhou, China
| | - Jincheng Cao
- Radiology Department, Changxing People's Hospital, Huzhou, China
| | - Tianjin Zhou
- Radiology Department, Changxing People's Hospital, Huzhou, China
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Mahmoudi K, Kim DH, Tavakkol E, Kihira S, Bauer A, Tsankova N, Khan F, Hormigo A, Yedavalli V, Nael K. Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma. Cancers (Basel) 2024; 16:589. [PMID: 38339340 PMCID: PMC10854536 DOI: 10.3390/cancers16030589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.
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Affiliation(s)
- Keon Mahmoudi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Daniel H. Kim
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Elham Tavakkol
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USA
| | - Nadejda Tsankova
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Fahad Khan
- Department of Pathology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Adilia Hormigo
- Department of Oncology, Montefiore Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Vivek Yedavalli
- Department of Radiology and Radiological Science, Johns Hopkins Bayview Medical Center, Baltimore, MD 21224, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
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Ju H, Kim K, Kim BI, Woo SK. Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics. Int J Mol Sci 2024; 25:698. [PMID: 38255770 PMCID: PMC10815846 DOI: 10.3390/ijms25020698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.
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Affiliation(s)
- Hyemin Ju
- Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Republic of Korea;
- Division of RI-Convergence Research, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
| | - Kangsan Kim
- Division of RI-Convergence Research, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
| | - Byung Il Kim
- Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
| | - Sang-Keun Woo
- Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Republic of Korea;
- Division of RI-Convergence Research, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
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Sanada T, Kinoshita M, Sasaki T, Yamamoto S, Fujikawa S, Fukuyama S, Hayashi N, Fukai J, Okita Y, Nonaka M, Uda T, Arita H, Mori K, Ishibashi K, Takano K, Nishida N, Shofuda T, Yoshioka E, Kanematsu D, Tanino M, Kodama Y, Mano M, Kanemura Y. Prediction of MGMT promotor methylation status in glioblastoma by contrast-enhanced T1-weighted intensity image. Neurooncol Adv 2024; 6:vdae016. [PMID: 38410136 PMCID: PMC10896622 DOI: 10.1093/noajnl/vdae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024] Open
Abstract
Background The study aims to explore MRI phenotypes that predict glioblastoma's (GBM) methylation status of the promoter region of MGMT gene (pMGMT) by qualitatively assessing contrast-enhanced T1-weighted intensity images. Methods A total of 193 histologically and molecularly confirmed GBMs at the Kansai Network for Molecular Diagnosis of Central Nervous Tumors (KANSAI) were used as an exploratory cohort. From the Cancer Imaging Archive/Cancer Genome Atlas (TCGA) 93 patients were used as validation cohorts. "Thickened structure" was defined as the solid tumor component presenting circumferential extension or occupying >50% of the tumor volume. "Methylated contrast phenotype" was defined as indistinct enhancing circumferential border, heterogenous enhancement, or nodular enhancement. Inter-rater agreement was assessed, followed by an investigation of the relationship between radiological findings and pMGMT methylation status. Results Fleiss's Kappa coefficient for "Thickened structure" was 0.68 for the exploratory and 0.55 for the validation cohort, and for "Methylated contrast phenotype," 0.30 and 0.39, respectively. The imaging feature, the presence of "Thickened structure" and absence of "Methylated contrast phenotype," was significantly predictive of pMGMT unmethylation both for the exploratory (p = .015, odds ratio = 2.44) and for the validation cohort (p = .006, odds ratio = 7.83). The sensitivities and specificities of the imaging feature, the presence of "Thickened structure," and the absence of "Methylated contrast phenotype" for predicting pMGMT unmethylation were 0.29 and 0.86 for the exploratory and 0.25 and 0.96 for the validation cohort. Conclusions The present study showed that qualitative assessment of contrast-enhanced T1-weighted intensity images helps predict GBM's pMGMT methylation status.
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Affiliation(s)
- Takahiro Sanada
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
| | - Takahiro Sasaki
- Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
- Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan
| | - Shota Yamamoto
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Osaka General Medical Center, Osaka, Japan
| | - Seiya Fujikawa
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Japanese Red Cross Kitami Hospital, Kitami, Japan
| | - Shusei Fukuyama
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Nobuhide Hayashi
- Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan
| | - Junya Fukai
- Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
| | - Yoshiko Okita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
| | - Masahiro Nonaka
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
- Department of Neurosurgery, Kansai Medical University, Hirakata, Japan
| | - Takehiro Uda
- Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kanji Mori
- Department of Neurosurgery, Yao Municipal Hospital, Yao, Japan
| | - Kenichi Ishibashi
- Department of Neurosurgery, Osaka City General Hospital, Osaka, Japan
| | - Koji Takano
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
- Department of Neurosurgery, Toyonaka Municipal Hospital, Toyonaka, Japan
| | - Namiko Nishida
- Department of Neurosurgery, Tazuke Kofukai Foundation, Medical Research Institute, Kitano Hospital, Osaka, Japan
| | - Tomoko Shofuda
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Ema Yoshioka
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Daisuke Kanematsu
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Mishie Tanino
- Department of Diagnostic Pathology, Asahikawa Medical University Hospital, Asahikawa, Japan
| | - Yoshinori Kodama
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
- Department of Diagnostic Pathology and Cytology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayuki Mano
- Department of Central Laboratory and Surgical Pathology, NHO Osaka National Hospital, Osaka, Japan
| | - Yonehiro Kanemura
- Department of Diagnostic Pathology and Cytology, Osaka International Cancer Institute, Osaka, Japan
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Chen P, Wang P, Gao B. The application value of deep learning in the background of precision medicine in glioblastoma. Sci Prog 2024; 107:368504231223353. [PMID: 38262933 PMCID: PMC10807326 DOI: 10.1177/00368504231223353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Introduction: Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current conventional treatment regimen because of different molecular subtypes, in the context of precision medicine, the application of deep learning to identify the salient features of tumors on brain imaging, prognostic predictive assessment combined with clinical data to maximize the benefits of each patient from the treatment regimen is a non-invasive and feasible regimen. Methods: We conducted a comprehensive review of the existing literature on the role of deep learning in glioblastomas, covering molecular classification and diagnosis, prognosis assessment. Results: Data based on a variety of magnetic resonance imaging sequences, genetic information, and clinical combinations enable noninvasive predictive tumor diagnosis of glioblastoma and assess overall survival and treatment response accuracy. For images, standardized image acquisition and data extraction techniques can be effectively translated into learning models for clinical practice. However, it must be recognized that interventions in the treatment of glioblastoma using deep learning are still in their infancy, and the robustness of the model is challenged, as the current total number of glioblastoma samples is insufficient for large-scale experimental methods, which is directly related to the difficulty of application of the model. Conclusion: Compared to radiomics and shallow machine learning, deep learning can be a more robust, non-invasive, and effective approach, providing more valuable information as clinicians develop personalized medical protocols for glioblastoma patients.
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Affiliation(s)
- Pengyu Chen
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guizhou, China
| | - Ping Wang
- Key Laboratory of Brain Imaging, Guizhou Medical University, Guiyang, China
| | - Bo Gao
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, Guizhou, China
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9
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Qureshi TA, Chen X, Xie Y, Murakami K, Sakatani T, Kita Y, Kobayashi T, Miyake M, Knott SRV, Li D, Rosser CJ, Furuya H. MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer. Int J Mol Sci 2023; 25:88. [PMID: 38203254 PMCID: PMC10778815 DOI: 10.3390/ijms25010088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
Accurate staging of bladder cancer assists in identifying optimal treatment (e.g., transurethral resection vs. radical cystectomy vs. bladder preservation). However, currently, about one-third of patients are over-staged and one-third are under-staged. There is a pressing need for a more accurate staging modality to evaluate patients with bladder cancer to assist clinical decision-making. We hypothesize that MRI/RNA-seq-based radiogenomics and artificial intelligence can more accurately stage bladder cancer. A total of 40 magnetic resonance imaging (MRI) and matched formalin-fixed paraffin-embedded (FFPE) tissues were available for analysis. Twenty-eight (28) MRI and their matched FFPE tissues were available for training analysis, and 12 matched MRI and FFPE tissues were used for validation. FFPE samples were subjected to bulk RNA-seq, followed by bioinformatics analysis. In the radiomics, several hundred image-based features from bladder tumors in MRI were extracted and analyzed. Overall, the model obtained mean sensitivity, specificity, and accuracy of 94%, 88%, and 92%, respectively, in differentiating intra- vs. extra-bladder cancer. The proposed model demonstrated improvement in the three matrices by 17%, 33%, and 25% and 17%, 16%, and 17% as compared to the genetic- and radiomic-based models alone, respectively. The radiogenomics of bladder cancer provides insight into discriminative features capable of more accurately staging bladder cancer. Additional studies are underway.
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Affiliation(s)
- Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (T.A.Q.); (Y.X.); (D.L.)
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
| | - Xingyu Chen
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (T.A.Q.); (Y.X.); (D.L.)
| | - Kaoru Murakami
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
- Department of Urology, Kyoto University, Kyoto 606-8507, Japan; (Y.K.); (T.K.)
| | - Toru Sakatani
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
| | - Yuki Kita
- Department of Urology, Kyoto University, Kyoto 606-8507, Japan; (Y.K.); (T.K.)
| | - Takashi Kobayashi
- Department of Urology, Kyoto University, Kyoto 606-8507, Japan; (Y.K.); (T.K.)
| | - Makito Miyake
- Department of Urology, Nara Medical University, Kashihara 634-8522, Japan;
| | - Simon R. V. Knott
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (T.A.Q.); (Y.X.); (D.L.)
| | - Charles J. Rosser
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
| | - Hideki Furuya
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
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10
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Stefano A, Bertelli E, Comelli A, Gatti M, Stanzione A. Editorial: Radiomics and radiogenomics in genitourinary oncology: artificial intelligence and deep learning applications. Front Radiol 2023; 3:1325594. [PMID: 38192376 PMCID: PMC10773800 DOI: 10.3389/fradi.2023.1325594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024]
Affiliation(s)
- Alessandro Stefano
- Institute ofMolecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy
| | - Elena Bertelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | | | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
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11
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O’Sullivan NJ, Temperley HC, Horan MT, Corr A, Mehigan BJ, Larkin JO, McCormick PH, Kavanagh DO, Meaney JFM, Kelly ME. Radiogenomics: Contemporary Applications in the Management of Rectal Cancer. Cancers (Basel) 2023; 15:5816. [PMID: 38136361 PMCID: PMC10741704 DOI: 10.3390/cancers15245816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of 'imaging biomarkers' that have the potential to serve as an alternative 'liquid-biopsy' in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology.
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Affiliation(s)
- Niall J. O’Sullivan
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Hugo C. Temperley
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Michelle T. Horan
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
| | - Brian J. Mehigan
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - John O. Larkin
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Paul H. McCormick
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Dara O. Kavanagh
- Department of Surgery, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Surgery, Royal College of Surgeons, D02 YN77 Dublin, Ireland
| | - James F. M. Meaney
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Michael E. Kelly
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
- Trinity St. James’s Cancer Institute (TSJCI), D08 NHY1 Dublin, Ireland
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12
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Maimaiti A, Abulaiti A, Tang B, Dilixiati Y, Li X, Yakufu S, Wang Y, Jiang L, Shao H. Radiogenomic landscape: Assessment of specific phagocytosis regulators in lower-grade gliomas. Exp Biol Med (Maywood) 2023; 248:2289-2303. [PMID: 38062999 PMCID: PMC10903236 DOI: 10.1177/15353702231211939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/28/2023] [Indexed: 01/23/2024] Open
Abstract
Genome-wide CRISPR-Cas9 knockout screens have emerged as a powerful method for identifying key genes driving tumor growth. The aim of this study was to explore the phagocytosis regulators (PRs) specifically associated with lower-grade glioma (LGG) using the CRISPR-Cas9 screening database. Identifying these core PRs could lead to novel therapeutic targets and pave the way for a non-invasive radiogenomics approach to assess LGG patients' prognosis and treatment response. We selected 24 PRs that were overexpressed and lethal in LGG for analysis. The identified PR subtypes (PRsClusters, geneClusters, and PRs-score models) effectively predicted clinical outcomes in LGG patients. Immune response markers, such as CTLA4, were found to be significantly associated with PR-score. Nine radiogenomics models using various machine learning classifiers were constructed to uncover survival risk. The area under the curve (AUC) values for these models in the test and training datasets were 0.686 and 0.868, respectively. The CRISPR-Cas9 screen identified novel prognostic radiogenomics biomarkers that correlated well with the expression status of specific PR-related genes in LGG patients. These biomarkers successfully stratified patient survival outcomes and treatment response using The Cancer Genome Atlas (TCGA) database. This study has important implications for the development of precise clinical treatment strategies and holds promise for more accurate therapeutic approaches for LGG patients in the future.
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Affiliation(s)
- Aierpati Maimaiti
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Aimitaji Abulaiti
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Bin Tang
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | | | - Xueqi Li
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Suobinuer Yakufu
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Yongxin Wang
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Lei Jiang
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Hua Shao
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
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13
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Ioannidis GS, Pigott LE, Iv M, Surlan-Popovic K, Wintermark M, Bisdas S, Marias K. Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas. Front Neurol 2023; 14:1249452. [PMID: 38046592 PMCID: PMC10690367 DOI: 10.3389/fneur.2023.1249452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
Objective This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers. Methods To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.
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Affiliation(s)
- Georgios S. Ioannidis
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
| | - Laura Elin Pigott
- Institute of Health and Social Care, London South Bank University, London, United Kingdom
- Faculty of Brain Science, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery University College London, London, United Kingdom
| | - Michael Iv
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Katarina Surlan-Popovic
- Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Neuroradiology, University Medical Centre, Ljubljana, Slovenia
| | - Max Wintermark
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London, United Kingdom
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
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14
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Tam A, Mercier BD, Thomas RM, Tizpa E, Wong IG, Shi J, Garg R, Hampel H, Gray SW, Williams T, Bazan JG, Li YR. Moving the Needle Forward in Genomically-Guided Precision Radiation Treatment. Cancers (Basel) 2023; 15:5314. [PMID: 38001574 PMCID: PMC10669735 DOI: 10.3390/cancers15225314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/06/2023] [Accepted: 10/13/2023] [Indexed: 11/26/2023] Open
Abstract
Radiation treatment (RT) is a mainstay treatment for many types of cancer. Recommendations for RT and the radiation plan are individualized to each patient, taking into consideration the patient's tumor pathology, staging, anatomy, and other clinical characteristics. Information on germline mutations and somatic tumor mutations is at present rarely used to guide specific clinical decisions in RT. Many genes, such as ATM, and BRCA1/2, have been identified in the laboratory to confer radiation sensitivity. However, our understanding of the clinical significance of mutations in these genes remains limited and, as individual mutations in such genes can be rare, their impact on tumor response and toxicity remains unclear. Current guidelines, including those from the National Comprehensive Cancer Network (NCCN), provide limited guidance on how genetic results should be integrated into RT recommendations. With an increasing understanding of the molecular underpinning of radiation response, genomically-guided RT can inform decisions surrounding RT dose, volume, concurrent therapies, and even omission to further improve oncologic outcomes and reduce risks of toxicities. Here, we review existing evidence from laboratory, pre-clinical, and clinical studies with regard to how genetic alterations may affect radiosensitivity. We also summarize recent data from clinical trials and explore potential future directions to utilize genetic data to support clinical decision-making in developing a pathway toward personalized RT.
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Affiliation(s)
- Andrew Tam
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Benjamin D. Mercier
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
- Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (H.H.); (S.W.G.)
| | - Reeny M. Thomas
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Eemon Tizpa
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Irene G. Wong
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Juncong Shi
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Rishabh Garg
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Heather Hampel
- Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (H.H.); (S.W.G.)
| | - Stacy W. Gray
- Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (H.H.); (S.W.G.)
| | - Terence Williams
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Jose G. Bazan
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
| | - Yun R. Li
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd., Duarte, CA 91010, USA; (A.T.); (B.D.M.); (R.M.T.); (E.T.); (I.G.W.); (J.S.); (R.G.); (T.W.)
- Department of Cancer Genetics and Epigenetics, City of Hope National Medical Center, Duarte, CA 91010, USA
- Division of Quantitative Medicine & Systems Biology, Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, AZ 85022, USA
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15
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Dagher SA, Lochner RH, Ozkara BB, Schomer DF, Wintermark M, Fuller GN, Ucisik FE. The T2-FLAIR mismatch sign in oncologic neuroradiology: History, current use, emerging data, and future directions. Neuroradiol J 2023:19714009231212375. [PMID: 37924213 DOI: 10.1177/19714009231212375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2023] Open
Abstract
The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed.
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Affiliation(s)
- Samir A Dagher
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Riley Hideo Lochner
- Section of Neuropathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Burak Berksu Ozkara
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Donald F Schomer
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gregory N Fuller
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Section of Neuropathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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16
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van der Hulst HJ, Jansen RW, Vens C, Bos P, Schats W, de Jong MC, Martens RM, Bodalal Z, Beets-Tan RGH, van den Brekel MWM, de Graaf P, Castelijns JA. The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:5077. [PMID: 37894447 PMCID: PMC10605807 DOI: 10.3390/cancers15205077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC.
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Affiliation(s)
- Hedda J. van der Hulst
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, 6211 LK Maastricht, The Netherlands
| | - Robin W. Jansen
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Conchita Vens
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- School of Cancer Science, University of Glasgow, Glasgow G61 1QH, UK
| | - Paula Bos
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Winnie Schats
- Scientific Information Service, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Marcus C. de Jong
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Roland M. Martens
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, 6211 LK Maastricht, The Netherlands
| | - Regina G. H. Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, 6211 LK Maastricht, The Netherlands
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark
| | - Michiel W. M. van den Brekel
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Department of Otolaryngology and Head & Neck Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HZ Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, 1081 HV Amsterdam, The Netherlands
| | - Jonas A. Castelijns
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
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17
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Wang Z, Li W, Jin D, Fan B. Radiomics in the Diagnosis of Gastric Cancer: Current Status and Future Perspectives. Curr Med Imaging 2023:CMIR-EPUB-135372. [PMID: 37881084 DOI: 10.2174/0115734056246452231011042418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 09/05/2023] [Accepted: 09/15/2023] [Indexed: 10/27/2023]
Abstract
Gastric cancer is a malignant cancerous lesion with high morbidity and mortality. Preoperative diagnosis of gastric cancer is challenging owing to the presentation of atypical symptoms and the diversity of occurrence of focal gastric lesions. Therefore, an endoscopic biopsy is used to diagnose gastric cancer in combination with imaging examination for a comprehensive evaluation of the local tumor range (T), lymph node status (N), and distant metastasis (M). The resolution of imaging examinations has significantly improved with the technological advancement in this sector. However, imaging examinations can barely provide valuable information. In clinical practice, an examination method that can provide information on the biological behavior of the tumor is critical to strategizing the treatment plan. Artificial intelligence (AI) allows for such an inspection procedure by reflecting the histological features of lesions using quantitative information extracted from images. Currently, AI is widely employed across various medical fields, especially in the processing of medical images. The basic application process of radiomics has been described in this study, and its role in clinical studies of gastric cancer has been discussed.
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Affiliation(s)
- Zhiqiang Wang
- Department of Radiology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, NanChang 330006, Jiangxi, China
| | - Weiran Li
- Department of Radiology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, NanChang 330006, Jiangxi, China
| | - Di Jin
- Department of Radiology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, NanChang 330006, Jiangxi, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, NanChang 330006, Jiangxi, China
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18
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Tobiasz J, Al-Harbi N, Bin Judia S, Majid Wakil S, Polanska J, Alsbeih G. Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation. Front Oncol 2023; 13:1154222. [PMID: 37849808 PMCID: PMC10577171 DOI: 10.3389/fonc.2023.1154222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/11/2023] [Indexed: 10/19/2023] Open
Abstract
Introduction The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation. Methods We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs. Results SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges. Discussion The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.
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Affiliation(s)
- Joanna Tobiasz
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
- Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
| | - Najla Al-Harbi
- Radiation Biology Section, Biomedical Physics Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Sara Bin Judia
- Radiation Biology Section, Biomedical Physics Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Salma Majid Wakil
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
- Laboratory of Neurogenetics, National Institutes of Health, Rockville, MD, United States
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Ghazi Alsbeih
- Radiation Biology Section, Biomedical Physics Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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19
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Greco F, Panunzio A, Tafuri A, Bernetti C, Pagliarulo V, Beomonte Zobel B, Scardapane A, Mallio CA. Radiogenomic Features of GIMAP Family Genes in Clear Cell Renal Cell Carcinoma: An Observational Study on CT Images. Genes (Basel) 2023; 14:1832. [PMID: 37895181 PMCID: PMC10606653 DOI: 10.3390/genes14101832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 10/29/2023] Open
Abstract
GTPases of immunity-associated proteins (GIMAP) genes include seven functional genes and a pseudogene. Most of the GIMAPs have a role in the maintenance and development of lymphocytes. GIMAPs could inhibit the development of tumors by increasing the amount and antitumor activity of infiltrating immunocytes. Knowledge of key factors that affect the tumor immune microenvironment for predicting the efficacy of immunotherapy and establishing new targets in ccRCC is of great importance. A computed tomography (CT)-based radiogenomic approach was used to detect the imaging phenotypic features of GIMAP family gene expression in ccRCC. In this retrospective study we enrolled 193 ccRCC patients divided into two groups: ccRCC patients with GIMAP expression (n = 52) and ccRCC patients without GIMAP expression (n = 141). Several imaging features were evaluated on preoperative CT scan. A statistically significant correlation was found with absence of endophytic growth pattern (p = 0.049), tumor infiltration (p = 0.005), advanced age (p = 0.018), and high Fuhrman grade (p = 0.024). This study demonstrates CT imaging features of GIMAP expression in ccRCC. These results could allow the collection of data on GIMAP expression through a CT-approach and could be used for the development of a targeted therapy.
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Affiliation(s)
- Federico Greco
- Department of Radiology, Cittadella Della Salute, Azienda Sanitaria Locale di Lecce, Piazza Filippo Bottazzi, 2, 73100 Lecce, Italy
| | - Andrea Panunzio
- Department of Urology, “Vito Fazzi” Hospital, Piazza Filippo Muratore, 1, 73100 Lecce, Italy; (A.P.); (A.T.); (V.P.)
| | - Alessandro Tafuri
- Department of Urology, “Vito Fazzi” Hospital, Piazza Filippo Muratore, 1, 73100 Lecce, Italy; (A.P.); (A.T.); (V.P.)
| | - Caterina Bernetti
- Department of Medicine and Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy; (C.B.); (B.B.Z.); (C.A.M.)
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Vincenzo Pagliarulo
- Department of Urology, “Vito Fazzi” Hospital, Piazza Filippo Muratore, 1, 73100 Lecce, Italy; (A.P.); (A.T.); (V.P.)
| | - Bruno Beomonte Zobel
- Department of Medicine and Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy; (C.B.); (B.B.Z.); (C.A.M.)
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Arnaldo Scardapane
- Dipartimento Interdisciplinare di Medicina, Sezione di Diagnostica Per Immagini, Università degli Studi di Bari “Aldo Moro”, Piazza Giulio Cesare, 11, 70124 Bari, Italy;
| | - Carlo Augusto Mallio
- Department of Medicine and Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy; (C.B.); (B.B.Z.); (C.A.M.)
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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20
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Abstract
Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.
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21
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Wang Y, Gao B, Xia C, Peng X, Liu H, Wu S. Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma. Quant Imaging Med Surg 2023; 13:5803-5814. [PMID: 37711809 PMCID: PMC10498241 DOI: 10.21037/qims-22-840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 07/14/2023] [Indexed: 09/16/2023]
Abstract
Background The tumour microenvironment (TME) has occupied a potent position in the tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Radiogenomics is an emerging field that integrates imaging and genetic information, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and treatment response. However, optimal evaluation methodologies for radiogenomics in patients with HCC have not been well established. Therefore, this study aims to develop a radiogenomics models, associating contrast-enhanced computed tomography (CECT) based radiomics features and transcriptomics data with TME, to increase predictive precision for overall survival (OS) in patients with HCC. Methods Transcriptome profiles of 365 patients with HCC from The Cancer Genome Atlas (TCGA)-HCC cohort were used to obtain TME-related genes by differential expression analysis. TME-related radiomics features of 53 patients with HCC from The Cancer Imaging Archive (TCIA)-HCC cohort matched with the TCGA-HCC cohort were screened via correlation analysis. Furthermore, a radiogenomics score-based prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCIA-HCC cohort. Finally, the ability to predict prognosis and the value of the model in identifying the abundance of immune cell infiltration were investigated. Results A radiogenomics prognostic model was developed, which incorporated 1 radiomics feature [original_gray-level co-occurrence matrix (glcm)_inverse difference normalized (Idn)] and 3 genes [spen paralogue and orthologue C‑terminal domain containing 1 (SPOCD1); killer cell lectin like receptor B1 (KLRB1); G protein-coupled receptor 182 (GPR182)]. The model performed satisfactorily in the training and test sets [1-year, 2-year, 3-year area under the curve (AUC) of 0.81, 0.85 and 0.87 in the training set, respectively; and 0.73, 0.83, and 0.84 in the test set, respectively]. Moreover, the model showed that higher radiogenomics scores were associated with worse OS and lower levels of immune infiltration. Conclusions The novel CECT-based radiogenomics model may provide valuable insights for prognostic stratification and TME assessment of patients with HCC.
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Affiliation(s)
- Yaqi Wang
- Department of Radiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China
| | - Bin Gao
- Department of Radiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China
| | - Chunhua Xia
- Department of Radiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China
| | - Xiaozheng Peng
- Department of Interventional Radiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China
| | - Haifeng Liu
- Department of Interventional Radiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China
| | - Senlin Wu
- Department of Interventional Radiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, China
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22
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Panigrahi S, Mohammed S, Rao A, Baladandayuthapani V. Integrative Bayesian models using Post-selective inference: A case study in radiogenomics. Biometrics 2023; 79:1801-1813. [PMID: 35973786 PMCID: PMC9931934 DOI: 10.1111/biom.13740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 08/02/2022] [Indexed: 11/27/2022]
Abstract
Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection of these associations with accuracy. In this paper, we develop selection-aware Bayesian methods, which (1) counteract the impact of model selection bias through a "selection-aware posterior" in a flexible class of integrative Bayesian models post a selection of promising variables via ℓ1 -regularized algorithms; (2) strike an inevitable trade-off between the quality of model selection and inferential power when the same data set is used for both selection and uncertainty estimation. Central to our methodological development, a carefully constructed conditional likelihood function deployed with a reparameterization mapping provides tractable updates when gradient-based Markov chain Monte Carlo (MCMC) sampling is used for estimating uncertainties from the selection-aware posterior. Applying our methods to a radiogenomic analysis, we successfully recover several important gene pathways and estimate uncertainties for their associations with patient survival times.
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Affiliation(s)
| | - Shariq Mohammed
- Department of Biostatistics, University of Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan
| | - Arvind Rao
- Department of Biostatistics, University of Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan
- Department of Biomedical Engineering, University of Michigan
- Department of Radiation Oncology, University of Michigan
| | - Veerabhadran Baladandayuthapani
- Department of Biostatistics, University of Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan
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23
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Lum RTW, Wang X, Zhang M, Zhang X, Ho JYK, Chow SCY, Fujikawa T, Wong RHL. Emerging role of radiogenomics in genetically triggered thoracic aortic aneurysm and dissection: a narrative review. Ann Transl Med 2023; 11:356. [PMID: 37675315 PMCID: PMC10477616 DOI: 10.21037/atm-22-6149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/17/2023] [Indexed: 09/08/2023]
Abstract
Background and Objective Thoracic aortic aneurysm and dissection (TAAD) and its complications are life-threatening conditions. Hypertension and atherosclerosis had all along been recognized as the predominant risk factors for the development of TAAD. However, it was increasingly reported that genetic factors, such as single nucleotide polymorphisms (SNPs), are playing an important role in the disease development. The development of next-generation sequencing (NGS) and the rapid growth in radiomics provide a promising new platform to evaluate genetically triggered thoracic aortic aneurysm and dissection (GTAAD) from a new angle. This review is to present an overview of currently available knowledge regarding the use of radiomics and radiogenomics in GTAAD. Methods We performed literature searches in PubMed, EMBASE and Cochrane database from 2012 to 2022 regarding the use of radiomics and radiogenomics in GTAAD. Key Content and Findings There were only 13 studies on radiomics and 4 studies on radiogenomics integration retrieved from the search and it signifies there is still a significant knowledge gap in this field of translational medicine. An overview of the current knowledge of GTAAD, the workflow and role of radiomics, the radiogenomics integration for GTAAD including its potential role in the development of polygenic scores, as well as the implications, challenges, and limitations of radiogenomics research were discussed. Conclusions In the contemporary era, radiogenomics has been emerging as a state-of-the art approach to establish statistical correlation with radiomics features with genomic information in diagnosis, risk modeling and prediction and treatment decision in TAAD.
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Affiliation(s)
- Ray Tak Wai Lum
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Xin Wang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Miaoru Zhang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Xianrui Zhang
- Department of Biomedical Science, The City University of Hong Kong, Hong Kong, China
| | - Jacky Yan Kit Ho
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Simon Chi Ying Chow
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Takuya Fujikawa
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Randolph Hung Leung Wong
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
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24
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Skouras P, Markouli M, Kalamatianos T, Stranjalis G, Korkolopoulou P, Piperi C. Advances on Liquid Biopsy Analysis for Glioma Diagnosis. Biomedicines 2023; 11:2371. [PMID: 37760812 PMCID: PMC10525418 DOI: 10.3390/biomedicines11092371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Gliomas comprise the most frequent primary central nervous system (CNS) tumors, characterized by remarkable genetic and epigenetic heterogeneity, difficulty in monitoring, and increased relapse and mortality rates. Tissue biopsy is an established method of tumor cell collection and analysis that enables diagnosis, classification of different tumor types, and prediction of prognosis upon confirmation of tumor's location for surgical removal. However, it is an invasive and often challenging procedure that cannot be used for frequent patient screening, detection of mutations, disease monitoring, or resistance to therapy. To this end, the minimally invasive procedure of liquid biopsy has emerged, allowing effortless tumor sampling and enabling continuous monitoring. It is considered a novel preferable way to obtain faster data on potential tumor risk, personalized diagnosis, prognosis, and recurrence evaluation. The purpose of this review is to describe the advances on liquid biopsy for glioma diagnosis and management, indicating several biomarkers that can be utilized to analyze tumor characteristics, such as cell-free DNA (cfDNA), cell-free RNA (cfRNA), circulating proteins, circulating tumor cells (CTCs), and exosomes. It further addresses the benefit of combining liquid biopsy with radiogenomics to facilitate early and accurate diagnoses, enable precise prognostic assessments, and facilitate real-time disease monitoring, aiming towards more optimal treatment decisions.
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Affiliation(s)
- Panagiotis Skouras
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
- 1st Department of Neurosurgery, Evangelismos Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.K.); (G.S.)
| | - Mariam Markouli
- Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA;
| | - Theodosis Kalamatianos
- 1st Department of Neurosurgery, Evangelismos Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.K.); (G.S.)
| | - George Stranjalis
- 1st Department of Neurosurgery, Evangelismos Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.K.); (G.S.)
| | - Penelope Korkolopoulou
- Department of Pathology, Medical School, National and Kapodistrian University of Athens, 75 M. Asias Street, 11527 Athens, Greece;
| | - Christina Piperi
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
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25
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Ismail M, Craig S, Ahmed R, de Blank P, Tiwari P. Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors. Diagnostics (Basel) 2023; 13:2727. [PMID: 37685265 PMCID: PMC10487205 DOI: 10.3390/diagnostics13172727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.
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Affiliation(s)
- Marwa Ismail
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
| | - Stephen Craig
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
| | - Raheel Ahmed
- Department of Neurosurgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Peter de Blank
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA;
| | - Pallavi Tiwari
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
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Sukhadia SS, Muller KE, Workman AA, Nagaraj SH. Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study. Cancers (Basel) 2023; 15:3960. [PMID: 37568776 PMCID: PMC10416932 DOI: 10.3390/cancers15153960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023] Open
Abstract
Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging.
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Affiliation(s)
- Shrey S. Sukhadia
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA; (K.E.M.); (A.A.W.)
| | - Kristen E. Muller
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA; (K.E.M.); (A.A.W.)
| | - Adrienne A. Workman
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA; (K.E.M.); (A.A.W.)
| | - Shivashankar H. Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
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Kalaroopan D, Lasocki A. MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas. J Med Imaging Radiat Oncol 2023; 67:492-498. [PMID: 36919468 DOI: 10.1111/1754-9485.13522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 02/16/2023] [Indexed: 03/16/2023]
Abstract
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.
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Affiliation(s)
- Dinusha Kalaroopan
- Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Radiology, The University of Melbourne, Melbourne, Victoria, Australia
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Monti S, Truppa ME, Albanese S, Mancini M. Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review. J Pers Med 2023; 13:1204. [PMID: 37623455 PMCID: PMC10455673 DOI: 10.3390/jpm13081204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by exploring the relationship between imaging characteristics, genomic information, and outcomes that qualitative interpretations may have overlooked, offering valuable insights for personalized medicine. Preclinical research allows for a controlled environment where various aspects of a pathology can be replicated in animal models, providing radiomic and radiogenomic approaches with the unique opportunity to investigate the causal connection between imaging and molecular factors. The aim of this review is to present the current state of the art in the application of radiomics and radiogenomics on murine models. This review will provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings.
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Affiliation(s)
| | | | - Sandra Albanese
- National Research Council, Institute of Biostructures and Bioimaging, 80145 Naples, Italy; (S.M.); (M.E.T.); (M.M.)
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Porto-Álvarez J, Cernadas E, Aldaz Martínez R, Fernández-Delgado M, Huelga Zapico E, González-Castro V, Baleato-González S, García-Figueiras R, Antúnez-López JR, Souto-Bayarri M. CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study. Biomedicines 2023; 11:2144. [PMID: 37626641 PMCID: PMC10452272 DOI: 10.3390/biomedicines11082144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common types of cancer worldwide. The KRAS mutation is present in 30-50% of CRC patients. This mutation confers resistance to treatment with anti-EGFR therapy. This article aims at proving that computer tomography (CT)-based radiomics can predict the KRAS mutation in CRC patients. The piece is a retrospective study with 56 CRC patients from the Hospital of Santiago de Compostela, Spain. All patients had a confirmatory pathological analysis of the KRAS status. Radiomics features were obtained using an abdominal contrast enhancement CT (CECT) before applying any treatments. We used several classifiers, including AdaBoost, neural network, decision tree, support vector machine, and random forest, to predict the presence or absence of KRAS mutation. The most reliable prediction was achieved using the AdaBoost ensemble on clinical patient data, with a kappa and accuracy of 53.7% and 76.8%, respectively. The sensitivity and specificity were 73.3% and 80.8%. Using texture descriptors, the best accuracy and kappa were 73.2% and 46%, respectively, with sensitivity and specificity of 76.7% and 69.2%, also showing a correlation between texture patterns on CT images and KRAS mutation. Radiomics could help manage CRC patients, and in the future, it could have a crucial role in diagnosing CRC patients ahead of invasive methods.
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Affiliation(s)
- Jacobo Porto-Álvarez
- Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (J.P.-Á.); (R.A.M.); (E.H.Z.); (R.G.-F.); (M.S.-B.)
| | - Eva Cernadas
- Centro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain;
| | - Rebeca Aldaz Martínez
- Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (J.P.-Á.); (R.A.M.); (E.H.Z.); (R.G.-F.); (M.S.-B.)
| | - Manuel Fernández-Delgado
- Centro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain;
| | - Emilio Huelga Zapico
- Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (J.P.-Á.); (R.A.M.); (E.H.Z.); (R.G.-F.); (M.S.-B.)
| | - Víctor González-Castro
- Department of Electrical, Systems and Automation Engineering, Universidad de León, 24071 León, Spain;
| | - Sandra Baleato-González
- Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (J.P.-Á.); (R.A.M.); (E.H.Z.); (R.G.-F.); (M.S.-B.)
| | - Roberto García-Figueiras
- Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (J.P.-Á.); (R.A.M.); (E.H.Z.); (R.G.-F.); (M.S.-B.)
| | - J Ramon Antúnez-López
- Department of Pathology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
| | - Miguel Souto-Bayarri
- Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (J.P.-Á.); (R.A.M.); (E.H.Z.); (R.G.-F.); (M.S.-B.)
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Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. Explor Target Antitumor Ther 2023; 4:545-555. [PMID: 37720347 PMCID: PMC10501892 DOI: 10.37349/etat.2023.00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.
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Affiliation(s)
- Teresa Perillo
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Marco de Giorgi
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Umberto Maria Papace
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Antonietta Serino
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy
| | - Andrea Manto
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
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31
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Danilov G, Makashova E, Galkin M, Karandasheva K. Radiogenomics in NF2-Associated Schwannomatosis (Neurofibromatosis Type II): Exploratory Data Analysis. Stud Health Technol Inform 2023; 305:588-591. [PMID: 37387099 DOI: 10.3233/shti230565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Our pilot study aimed at exploratory radiogenomic data analysis in patients with NF2-associated schwannomatosis (formerly neurofibromatosis type II) to assume the potential of image biomarkers in this pathology. Fifty-three unrelated patients (37 (69.8%) women, avg. age 30.2 ± 11.2 y.o.) were enrolled in the study. First-order, gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and geometry-based statistics were calculated (3718 features per region of interest). We demonstrated imaging patterns and statistically significant differences in radiomic features potentially related to the genotype and clinical phenotype of the disease. However, the clinical utility of these patterns should be further evaluated. The study was supported by the Russian Science Foundation grant 21-15-00262.
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Affiliation(s)
- Gleb Danilov
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Elizaveta Makashova
- Radiotherapy department, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Mikhail Galkin
- Radiotherapy department, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
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Prencipe B, Delprete C, Garolla E, Corallo F, Gravina M, Natalicchio MI, Buongiorno D, Bevilacqua V, Altini N, Brunetti A. An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients. Bioengineering (Basel) 2023; 10:747. [PMID: 37508774 PMCID: PMC10376018 DOI: 10.3390/bioengineering10070747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/17/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
The complex pathobiology of lung cancer, and its spread worldwide, has prompted research studies that combine radiomic and genomic approaches. Indeed, the early identification of genetic alterations and driver mutations affecting the tumor is fundamental for correctly formulating the prognosis and therapeutic response. In this work, we propose a radiogenomic workflow to detect the presence of KRAS and EGFR mutations using radiomic features extracted from computed tomography images of patients affected by lung adenocarcinoma. To this aim, we investigated several feature selection algorithms to identify the most significant and uncorrelated sets of radiomic features and different classification models to reveal the mutational status. Then, we employed the SHAP (SHapley Additive exPlanations) technique to increase the understanding of the contribution given by specific radiomic features to the identification of the investigated mutations. Two cohorts of patients with lung adenocarcinoma were used for the study. The first one, obtained from the Cancer Imaging Archive (TCIA), consisted of 60 cases (25% EGFR, 23% KRAS); the second one, provided by the Azienda Ospedaliero-Universitaria 'Ospedali Riuniti' of Foggia, was composed of 55 cases (16% EGFR, 28% KRAS). The best-performing models proposed in our study achieved an AUC of 0.69 and 0.82 on the validation set for predicting the mutational status of EGFR and KRAS, respectively. The Multi-layer Perceptron model emerged as the top-performing model for both oncogenes, in some cases outperforming the state of the art. This study showed that radiomic features can be associated with EGFR and KRAS mutational status in patients with lung adenocarcinoma.
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Affiliation(s)
- Berardino Prencipe
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Claudia Delprete
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Emilio Garolla
- Department of Medical and Surgical Sciences, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Fabio Corallo
- Department of Medical and Surgical Sciences, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Matteo Gravina
- Department of Medical and Surgical Sciences, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Maria Iole Natalicchio
- Molecular Oncology and Pharmacogenomics Laboratory, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
- Apulian Bioengineering SRL, Via delle Violette 14, 70026 Modugno, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
- Apulian Bioengineering SRL, Via delle Violette 14, 70026 Modugno, Italy
| | - Nicola Altini
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
- Apulian Bioengineering SRL, Via delle Violette 14, 70026 Modugno, Italy
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Illimoottil M, Ginat D. Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans. Cancers (Basel) 2023; 15:3267. [PMID: 37444376 PMCID: PMC10339989 DOI: 10.3390/cancers15133267] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/25/2023] [Accepted: 05/27/2023] [Indexed: 07/15/2023] Open
Abstract
Deep learning techniques have been developed for analyzing head and neck cancer imaging. This review covers deep learning applications in cancer imaging, emphasizing tumor detection, segmentation, classification, and response prediction. In particular, advanced deep learning techniques, such as convolutional autoencoders, generative adversarial networks (GANs), and transformer models, as well as the limitations of traditional imaging and the complementary roles of deep learning and traditional techniques in cancer management are discussed. Integration of radiomics, radiogenomics, and deep learning enables predictive models that aid in clinical decision-making. Challenges include standardization, algorithm interpretability, and clinical validation. Key gaps and controversies involve model generalizability across different imaging modalities and tumor types and the role of human expertise in the AI era. This review seeks to encourage advancements in deep learning applications for head and neck cancer management, ultimately enhancing patient care and outcomes.
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Affiliation(s)
- Mathew Illimoottil
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64018, USA
| | - Daniel Ginat
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
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Dinis Fernandes C, Schaap A, Kant J, van Houdt P, Wijkstra H, Bekers E, Linder S, Bergman AM, van der Heide U, Mischi M, Zwart W, Eduati F, Turco S. Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer. Cancers (Basel) 2023; 15:3074. [PMID: 37370685 DOI: 10.3390/cancers15123074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.
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Affiliation(s)
- Catarina Dinis Fernandes
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Annekoos Schaap
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Joan Kant
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Petra van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Department of Urology, Amsterdam University Medical Centers, 1100 DD Amsterdam, The Netherlands
| | - Elise Bekers
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Simon Linder
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Division of Medical Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Uulke van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Massimo Mischi
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Wilbert Zwart
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Federica Eduati
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Simona Turco
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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Dabbs D, Mittal K, Heineman S, Whitworth P, Shah C, Savala J, Shivers SC, Bremer T. Analytical validation of the 7-gene biosignature for prediction of recurrence risk and radiation therapy benefit for breast ductal carcinoma in situ. Front Oncol 2023; 13:1069059. [PMID: 37274253 PMCID: PMC10236475 DOI: 10.3389/fonc.2023.1069059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 04/11/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose Ductal carcinoma in situ (DCIS), is a noninvasive breast cancer, representing 20-25% of breast cancer diagnoses in the USA. Current treatment options for DCIS include mastectomy or breast-conserving surgery (BCS) with or without radiation therapy (RT), but optimal risk-adjusted treatment selection remains a challenge. Findings from past and recent clinical trials have failed to identify a 'low risk' group of patients who do not benefit significantly from RT after BCS. To address this unmet need, a DCIS biosignature, DCISionRT (PreludeDx, Laguna Hills, CA), was developed and validated in multiple cohorts. DCISionRT is a molecular assay with an algorithm reporting a recurrence risk score for patients diagnosed with DCIS intended to guide DCIS treatment. In this study, we present results from analytical validity, performance assessment, and clinical performance validation and clinical utility for the DCISionRT test comprised of multianalyte assays with algorithmic analysis. Methods The analytical validation of each molecular assay was performed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines Quality Assurance for Design Control and Implementation of Immunohistochemistry Assays and the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO) recommendations for analytic validation of immunohistochemical assays. Results The analytic validation showed that the molecular assays that are part of DCISionRT test have high sensitivity, specificity, and accuracy/reproducibility (≥95%). The analytic precision of the molecular assays under controlled non-standard conditions had a total standard deviation of 6.6 (100-point scale), where the analytic variables (Lot, Machine, Run) each contributed <1% of the total variance. Additionally, the precision in the DCISionRT test result (DS) had a 95%CI ≤0.4 DS units under controlled non-standard conditions (Day, Lot, and Machine) for molecular assays over a wide range of clinicopathologic factor values. Clinical validation showed that the test identified 37% of patients in a low-risk group with a 10-year invasive IBR rate of ~3% and an absolute risk reduction (ARR) from RT of 1% (number needed to treat, NNT=100), while remaining patients with higher DS scores (elevated-risk) had an ARR for RT of 9% (NNT=11) and 96% clinical sensitivity for RT benefit. Conclusion The analytical performance of the PreludeDx DCISionRT molecular assays was high in representative formalin-fixed, paraffin-embedded breast tumor specimens. The DCISionRT test has been analytically validated and has been clinically validated in multiple peer-reviewed published studies.
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Affiliation(s)
| | | | | | - Pat Whitworth
- University of Tennessee, Knoxville, TN, United States
- Nashville Breast Center, Nashville, TN, United States
| | - Chirag Shah
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, United States
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Gao S, Wang F, Sun W, Qian X, Ji Y, Cheng Y, Wang X, Liu L, Sheng R, Zeng M. Preliminary radiogenomic study of hepatitis B virus-related hepatocellular carcinoma: associations between MRI features and mutations. Per Med 2023. [PMID: 37199498 DOI: 10.2217/pme-2022-0093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Aim: To investigate associations between MRI features and high-frequency mutations of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Methods: This study included 58 HCC patients who underwent contrast-enhanced MRI prior to surgical resection and genome sequencing. MRI features and mutation information were evaluated. Results: The top five most frequently mutated genes in HCC were TP53 (53.45%), TAF1 (24.14%), PDE4DIP (22.41%), ABCA13 (18.97%) and LRP1B (17.24%). Mutations in TP53 and LRP1B were associated with tumor necrosis (p = 0.035) and mosaic architecture (p = 0.015), respectively. Mutations in ABCA13 were associated with mosaic architecture (p = 0.025) and necrosis (p = 0.010). Conclusion: This preliminary radiogenomics analysis showed associations between MRI features and high-frequency mutations in HBV-related HCCs.
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Affiliation(s)
- Shanshan Gao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Feihang Wang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
| | - Xianling Qian
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yunfeng Cheng
- Institute of Clinical Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiaolin Wang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lingxiao Liu
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
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O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
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Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
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Gu Y, Huang H, Tong Q, Cao M, Ming W, Zhang R, Zhu W, Wang Y, Sun X. Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15082338. [PMID: 37190266 DOI: 10.3390/cancers15082338] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/13/2023] [Accepted: 04/15/2023] [Indexed: 05/17/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide, and the pronounced intra- and inter-tumor heterogeneity restricts clinical benefits. Dissecting molecular heterogeneity in HCC is commonly explored by endoscopic biopsy or surgical forceps, but invasive tissue sampling and possible complications limit the broadeer adoption. The radiomics framework is a promising non-invasive strategy for tumor heterogeneity decoding, and the linkage between radiomics and immuno-oncological characteristics is worth further in-depth study. In this study, we extracted multi-view imaging features from contrast-enhanced CT (CE-CT) scans of HCC patients, followed by developing a fused imaging feature subtyping (FIFS) model to identify two distinct radiomics subtypes. We observed two subtypes of patients with distinct texture-dominated radiomics profiles and prognostic outcomes, and the radiomics subtype identified by FIFS model was an independent prognostic factor. The heterogeneity was mainly attributed to inflammatory pathway activity and the tumor immune microenvironment. The predominant radiogenomics association was identified between texture-related features and immune-related pathways by integrating network analysis, and was validated in two independent cohorts. Collectively, this work described the close connections between multi-view radiomics features and immuno-oncological characteristics in HCC, and our integrative radiogenomics analysis strategy may provide clues to non-invasive inflammation-based risk stratification.
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Affiliation(s)
- Yu Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Hao Huang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Qi Tong
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Meng Cao
- Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Rongxin Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Wenyong Zhu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yuqi Wang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
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Bellini D, Milan M, Bordin A, Rizzi R, Rengo M, Vicini S, Onori A, Carbone I, De Falco E. A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer. Int J Mol Sci 2023; 24:ijms24087214. [PMID: 37108377 PMCID: PMC10138689 DOI: 10.3390/ijms24087214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.
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Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marika Milan
- UOC Neurology, Fondazione Ca'Granda, Ospedale Maggiore Policlinico, Via F. Sforza, 28, 20122 Milan, Italy
| | - Antonella Bordin
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Roberto Rizzi
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Alessandro Onori
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Elena De Falco
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Napoli, Italy
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Jin Y, Arimura H, Cui Y, Kodama T, Mizuno S, Ansai S. CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:cancers15082220. [PMID: 37190150 DOI: 10.3390/cancers15082220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.
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Affiliation(s)
- Yu Jin
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - YunHao Cui
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Takumi Kodama
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Shinichi Mizuno
- Division of Medical Technology, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Satoshi Ansai
- Laboratory of Genome Editing Breeding, Graduate School of Agriculture, Kyoto University, Kyoto 606-8520, Japan
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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Nabi G. Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer. J Clin Med 2023; 12:jcm12072605. [PMID: 37048688 PMCID: PMC10095552 DOI: 10.3390/jcm12072605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023] Open
Abstract
OBJECTIVES To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. MATERIALS AND METHODS Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed. RESULTS The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively. CONCLUSION This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.
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Affiliation(s)
- Chidozie N Ogbonnaya
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- College of Basic Medical Sciences, Abia State University, Uturu 441103, Nigeria
| | - Basim S O Alsaedi
- Statistics Department, University of Tabuk, Tabuk 47512, Saudi Arabia
| | - Abeer J Alhussaini
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- Department of Medical Imaging, Al-Amiri Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Ghulam Nabi
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- School of Medicine, Ninewells Hospital, Dundee DD1 9SY, UK
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Doria AS. Applications of artificial intelligence in clinical management, research and health administration: imaging perspectives with a focus on hemophilia. Expert Rev Hematol 2023:1-15. [PMID: 36939638 DOI: 10.1080/17474086.2023.2192474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
INTRODUCTION Joints of persons with hemophilia are frequently affected by repetitive hemarthrosis. In this paper concepts, perks and quirks of the use of artificial intelligence (AI), machine learning (ML) and deep learning are reviewed within clinical and research contexts of hemophilia and other blood-induced disorders' patient care, targeted to the imaging diagnosis of hemophilic joints, under the perspective of different stakeholders (radiologists, hematologists, nurses, physiotherapists, technologists, researchers, managers and patients/caregivers). AREAS COVERED Rubrics that determine the suitability of the utilization of AI in blood-induced disorders' patient care, including diagnosis and follow-up of patients are discussed, focusing on features in which AI can replace or augment the role of radiology in the clinical management and in research of patients. Insights on features in the design and conduct of AI projects in which the human intervention remains critical are provided. EXPERT OPINION The author discusses research concepts in radiogenomics, and challenges of the utilization of AI in different healthcare fields such as patient safety, data sharing and privacy regulations, workforce education and future jobs' shortage. Finally, the author proposes alternatives and potential solutions to mitigate challenges in successfully deploying ML algorithms into clinical practice.
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Affiliation(s)
- Andrea S Doria
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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Fischer S, Spath N, Hamed M. Data-Driven Radiogenomic Approach for Deciphering Molecular Mechanisms Underlying Imaging Phenotypes in Lung Adenocarcinoma: A Pilot Study. Int J Mol Sci 2023; 24:ijms24054947. [PMID: 36902378 PMCID: PMC10003564 DOI: 10.3390/ijms24054947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/13/2023] [Accepted: 02/23/2023] [Indexed: 03/08/2023] Open
Abstract
The heterogeneity of lung tumor nodules is reflected in their phenotypic characteristics in radiological images. The radiogenomics field employs quantitative image features combined with transcriptome expression levels to understand tumor heterogeneity molecularly. Due to the different data acquisition techniques for imaging traits and genomic data, establishing meaningful connections poses a challenge. We analyzed 86 image features describing tumor characteristics (such as shape and texture) with the underlying transcriptome and post-transcriptome profiles of 22 lung cancer patients (median age 67.5 years, from 42 to 80 years) to unravel the molecular mechanisms behind tumor phenotypes. As a result, we were able to construct a radiogenomic association map (RAM) linking tumor morphology, shape, texture, and size with gene and miRNA signatures, as well as biological correlates of GO terms and pathways. These indicated possible dependencies between gene and miRNA expression and the evaluated image phenotypes. In particular, the gene ontology processes "regulation of signaling" and "cellular response to organic substance" were shown to be reflected in CT image phenotypes, exhibiting a distinct radiomic signature. Moreover, the gene regulatory networks involving the TFs TAL1, EZH2, and TGFBR2 could reflect how the texture of lung tumors is potentially formed. The combined visualization of transcriptomic and image features suggests that radiogenomic approaches could identify potential image biomarkers for underlying genetic variation, allowing a broader view of the heterogeneity of the tumors. Finally, the proposed methodology could also be adapted to other cancer types to expand our knowledge of the mechanistic interpretability of tumor phenotypes.
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Affiliation(s)
- Sarah Fischer
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Ernst-Heydemannstr. 8, 18057 Rostock, Germany
- Department of Systems Biology and Bioinformatics, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany
| | - Nicolas Spath
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Ernst-Heydemannstr. 8, 18057 Rostock, Germany
- Department of Medicine II, Hematology and Oncology, University Hospital Schleswig-Holstein, Arnold-Hellerstr. 3, 24105 Kiel, Germany
| | - Mohamed Hamed
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Ernst-Heydemannstr. 8, 18057 Rostock, Germany
- Correspondence:
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von Ende E, Ryan S, Crain MA, Makary MS. Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology. Diagnostics (Basel) 2023; 13:diagnostics13050892. [PMID: 36900036 PMCID: PMC10000832 DOI: 10.3390/diagnostics13050892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/12/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice.
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Ferro M, Musi G, Marchioni M, Maggi M, Veccia A, Del Giudice F, Barone B, Crocetto F, Lasorsa F, Antonelli A, Schips L, Autorino R, Busetto GM, Terracciano D, Lucarelli G, Tataru OS. Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24. [PMID: 36902045 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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Delgado-Ortet M, Reinius MAV, McCague C, Bura V, Woitek R, Rundo L, Gill AB, Gehrung M, Ursprung S, Bolton H, Haldar K, Pathiraja P, Brenton JD, Crispin-Ortuzar M, Jimenez-Linan M, Escudero Sanchez L, Sala E. Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Front Oncol 2023; 13:1085874. [PMID: 36860310 PMCID: PMC9969130 DOI: 10.3389/fonc.2023.1085874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Background High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours. Methods In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process. Results Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments. Conclusions We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.
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Affiliation(s)
- Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Marika A. V. Reinius
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom,Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Vlad Bura
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom,Department of Radiology, Clinical Emergency Children’s Hospital, Cluj-Napoca, Romania
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom,Research Center for Medical Image Analysis & Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Helen Bolton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Krishnayan Haldar
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Pubudu Pathiraja
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - James D. Brenton
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom,Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom,Cancer Research UK Cambridge Centre, Cambridge, United Kingdom,Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom,Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy,Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy,*Correspondence: Evis Sala,
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47
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Hartmann K, Sadée CY, Satwah I, Carrillo-Perez F, Gevaert O. Imaging genomics: data fusion in uncovering disease heritability. Trends Mol Med 2023; 29:141-151. [PMID: 36470817 PMCID: PMC10507799 DOI: 10.1016/j.molmed.2022.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/28/2022] [Accepted: 11/03/2022] [Indexed: 12/04/2022]
Abstract
Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.
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Affiliation(s)
- Katherine Hartmann
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | - Christoph Y Sadée
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Ishan Satwah
- College of Medicine, Drexel University, Philadelphia, PA, USA
| | - Francisco Carrillo-Perez
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Granada, Spain
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
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48
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Duan J, Zhang Z, Chen Y, Zhao Y, Sun Q, Wang W, Zheng H, Liang D, Cheng J, Yan J, Li ZC. Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: A multicenter study. Mol Oncol 2023; 17:629-646. [PMID: 36688633 PMCID: PMC10061289 DOI: 10.1002/1878-0261.13380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/23/2022] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment-specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia-induced immunosuppression, a "cold" subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10-feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics-correlated biological pathways were validated externally. Our data suggest that adult-type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.
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Affiliation(s)
- Jingxian Duan
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuanshen Zhao
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hairong Zheng
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,National Innovation Center for Advanced Medical Devices, Shenzhen, China.,Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
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49
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Bonham LW, Geier EG, Sirkis DW, Leong JK, Ramos EM, Wang Q, Karydas A, Lee SE, Sturm VE, Sawyer RP, Friedberg A, Ichida JK, Gitler AD, Sugrue L, Cordingley M, Bee W, Weber E, Kramer JH, Rankin KP, Rosen HJ, Boxer AL, Seeley WW, Ravits J, Miller BL, Yokoyama JS. Radiogenomics of C9orf72 Expansion Carriers Reveals Global Transposable Element Derepression and Enables Prediction of Thalamic Atrophy and Clinical Impairment. J Neurosci 2023. [PMID: 36446586 DOI: 10.1101/2022.04.29.490104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023] Open
Abstract
Hexanucleotide repeat expansion (HRE) within C9orf72 is the most common genetic cause of frontotemporal dementia (FTD). Thalamic atrophy occurs in both sporadic and familial FTD but is thought to distinctly affect HRE carriers. Separately, emerging evidence suggests widespread derepression of transposable elements (TEs) in the brain in several neurodegenerative diseases, including C9orf72 HRE-mediated FTD (C9-FTD). Whether TE activation can be measured in peripheral blood and how the reduction in peripheral C9orf72 expression observed in HRE carriers relates to atrophy and clinical impairment remain unknown. We used FreeSurfer software to assess the effects of C9orf72 HRE and clinical diagnosis (n = 78 individuals, male and female) on atrophy of thalamic nuclei. We also generated a novel, human, whole-blood RNA-sequencing dataset to determine the relationships among peripheral C9orf72 expression, TE activation, thalamic atrophy, and clinical severity (n = 114 individuals, male and female). We confirmed global thalamic atrophy and reduced C9orf72 expression in HRE carriers. Moreover, we identified disproportionate atrophy of the right mediodorsal lateral nucleus in HRE carriers and showed that C9orf72 expression associated with clinical severity, independent of thalamic atrophy. Strikingly, we found global peripheral activation of TEs, including the human endogenous LINE-1 element L1HS L1HS levels were associated with atrophy of multiple pulvinar nuclei, a thalamic region implicated in C9-FTD. Integration of peripheral transcriptomic and neuroimaging data from human HRE carriers revealed atrophy of specific thalamic nuclei, demonstrated that C9orf72 levels relate to clinical severity, and identified marked derepression of TEs, including L1HS, which predicted atrophy of FTD-relevant thalamic nuclei.SIGNIFICANCE STATEMENT Pathogenic repeat expansion in C9orf72 is the most frequent genetic cause of FTD and amyotrophic lateral sclerosis (ALS; C9-FTD/ALS). The clinical, neuroimaging, and pathologic features of C9-FTD/ALS are well characterized, whereas the intersections of transcriptomic dysregulation and brain structure remain largely unexplored. Herein, we used a novel radiogenomic approach to examine the relationship between peripheral blood transcriptomics and thalamic atrophy, a neuroimaging feature disproportionately impacted in C9-FTD/ALS. We confirmed reduction of C9orf72 in blood and found broad dysregulation of transposable elements-genetic elements typically repressed in the human genome-in symptomatic C9orf72 expansion carriers, which associated with atrophy of thalamic nuclei relevant to FTD. C9orf72 expression was also associated with clinical severity, suggesting that peripheral C9orf72 levels capture disease-relevant information.
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Affiliation(s)
- Luke W Bonham
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94158
| | - Ethan G Geier
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Transposon Therapeutics, San Diego, California 92122
| | - Daniel W Sirkis
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Josiah K Leong
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Psychological Science, University of Arkansas, Fayetteville, Arkansas 72701
| | - Eliana Marisa Ramos
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095
| | - Qing Wang
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095
| | - Anna Karydas
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Suzee E Lee
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Russell P Sawyer
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
| | - Adit Friedberg
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Justin K Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, California 90033
| | - Aaron D Gitler
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305
| | - Leo Sugrue
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94158
| | | | - Walter Bee
- Transposon Therapeutics, San Diego, California 92122
| | - Eckard Weber
- Transposon Therapeutics, San Diego, California 92122
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Pathology, University of California, San Francisco, San Francisco, California 94158
| | - John Ravits
- Department of Neurosciences, ALS Translational Research, University of California, San Diego, La Jolla, California 92093
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
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50
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Bonham LW, Geier EG, Sirkis DW, Leong JK, Ramos EM, Wang Q, Karydas A, Lee SE, Sturm VE, Sawyer RP, Friedberg A, Ichida JK, Gitler AD, Sugrue L, Cordingley M, Bee W, Weber E, Kramer JH, Rankin KP, Rosen HJ, Boxer AL, Seeley WW, Ravits J, Miller BL, Yokoyama JS. Radiogenomics of C9orf72 Expansion Carriers Reveals Global Transposable Element Derepression and Enables Prediction of Thalamic Atrophy and Clinical Impairment. J Neurosci 2023; 43:333-345. [PMID: 36446586 PMCID: PMC9838702 DOI: 10.1523/jneurosci.1448-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 12/03/2022] Open
Abstract
Hexanucleotide repeat expansion (HRE) within C9orf72 is the most common genetic cause of frontotemporal dementia (FTD). Thalamic atrophy occurs in both sporadic and familial FTD but is thought to distinctly affect HRE carriers. Separately, emerging evidence suggests widespread derepression of transposable elements (TEs) in the brain in several neurodegenerative diseases, including C9orf72 HRE-mediated FTD (C9-FTD). Whether TE activation can be measured in peripheral blood and how the reduction in peripheral C9orf72 expression observed in HRE carriers relates to atrophy and clinical impairment remain unknown. We used FreeSurfer software to assess the effects of C9orf72 HRE and clinical diagnosis (n = 78 individuals, male and female) on atrophy of thalamic nuclei. We also generated a novel, human, whole-blood RNA-sequencing dataset to determine the relationships among peripheral C9orf72 expression, TE activation, thalamic atrophy, and clinical severity (n = 114 individuals, male and female). We confirmed global thalamic atrophy and reduced C9orf72 expression in HRE carriers. Moreover, we identified disproportionate atrophy of the right mediodorsal lateral nucleus in HRE carriers and showed that C9orf72 expression associated with clinical severity, independent of thalamic atrophy. Strikingly, we found global peripheral activation of TEs, including the human endogenous LINE-1 element L1HS L1HS levels were associated with atrophy of multiple pulvinar nuclei, a thalamic region implicated in C9-FTD. Integration of peripheral transcriptomic and neuroimaging data from human HRE carriers revealed atrophy of specific thalamic nuclei, demonstrated that C9orf72 levels relate to clinical severity, and identified marked derepression of TEs, including L1HS, which predicted atrophy of FTD-relevant thalamic nuclei.SIGNIFICANCE STATEMENT Pathogenic repeat expansion in C9orf72 is the most frequent genetic cause of FTD and amyotrophic lateral sclerosis (ALS; C9-FTD/ALS). The clinical, neuroimaging, and pathologic features of C9-FTD/ALS are well characterized, whereas the intersections of transcriptomic dysregulation and brain structure remain largely unexplored. Herein, we used a novel radiogenomic approach to examine the relationship between peripheral blood transcriptomics and thalamic atrophy, a neuroimaging feature disproportionately impacted in C9-FTD/ALS. We confirmed reduction of C9orf72 in blood and found broad dysregulation of transposable elements-genetic elements typically repressed in the human genome-in symptomatic C9orf72 expansion carriers, which associated with atrophy of thalamic nuclei relevant to FTD. C9orf72 expression was also associated with clinical severity, suggesting that peripheral C9orf72 levels capture disease-relevant information.
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Affiliation(s)
- Luke W Bonham
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94158
| | - Ethan G Geier
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Transposon Therapeutics, San Diego, California 92122
| | - Daniel W Sirkis
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Josiah K Leong
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Psychological Science, University of Arkansas, Fayetteville, Arkansas 72701
| | - Eliana Marisa Ramos
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095
| | - Qing Wang
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095
| | - Anna Karydas
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Suzee E Lee
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Russell P Sawyer
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
| | - Adit Friedberg
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Justin K Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, California 90033
| | - Aaron D Gitler
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305
| | - Leo Sugrue
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94158
| | | | - Walter Bee
- Transposon Therapeutics, San Diego, California 92122
| | - Eckard Weber
- Transposon Therapeutics, San Diego, California 92122
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Pathology, University of California, San Francisco, San Francisco, California 94158
| | - John Ravits
- Department of Neurosciences, ALS Translational Research, University of California, San Diego, La Jolla, California 92093
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94158
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94158, and Trinity College Dublin, Dublin, Ireland
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