1
|
Demetriou D, Lockhat Z, Brzozowski L, Saini KS, Dlamini Z, Hull R. The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics. Cancers (Basel) 2024; 16:1076. [PMID: 38473432 DOI: 10.3390/cancers16051076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.
Collapse
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
| |
Collapse
|
2
|
Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
Collapse
Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
| |
Collapse
|
3
|
Zhao X, Liao Y, Xie J, He X, Zhang S, Wang G, Fang J, Lu H, Yu J. BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification. Comput Biol Med 2023; 164:107255. [PMID: 37499296 DOI: 10.1016/j.compbiomed.2023.107255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/31/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on employing DCE-MR images for breast cancer diagnosis due to the lack of publicly available DCE-MRI datasets. To address this issue, our work releases a new DCE-MRI dataset called BreastDM for breast tumor segmentation and classification. In particular, a dataset of 232 patients selected with DCE-MR images for benign and malignant cases is established. Each case consists of three types of sequences: pre-contrast, post-contrast, and subtraction sequences. To show the difficulty of breast DCE-MRI tumor image segmentation and classification tasks, benchmarks are achieved by state-of-the-art image segmentation and classification algorithms, including conventional hand-crafted based methods and recently-emerged deep learning-based methods. More importantly, a local-global cross attention fusion network (LG-CAFN) is proposed to further improve the performance of breast tumor images classification. Specifically, LG-CAFN achieved the highest accuracy (88.20%, 83.93%) and AUC value (0.9154,0.8826) in both groups of experiments. Extensive experiments are conducted to present strong baselines based on various typical image segmentation and classification algorithms. Experiment results also demonstrate the superiority of the proposed LG-CAFN to other breast tumor images classification methods. The related dataset and evaluation codes are publicly available at smallboy-code/Breast-cancer-dataset.
Collapse
Affiliation(s)
- Xiaoming Zhao
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China; School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Yuehui Liao
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China; School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jiahao Xie
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China; School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xiaxia He
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China
| | - Shiqing Zhang
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China; School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Guoyu Wang
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China.
| | - Jiangxiong Fang
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China
| | - Hongsheng Lu
- Taizhou Central Hospital, Taizhou University, 318000, Taizhou, China
| | - Jun Yu
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China
| |
Collapse
|
4
|
Jin N, Qiao B, Zhao M, Li L, Zhu L, Zang X, Gu B, Zhang H. Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics. Cancer Med 2023; 12:19260-19271. [PMID: 37635388 PMCID: PMC10557859 DOI: 10.1002/cam4.6474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND To investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC). METHODS The region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co-expression network analysis was used to construct co-expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features. RESULTS Four hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs. CONCLUSIONS OSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.
Collapse
Affiliation(s)
- Nenghao Jin
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Bo Qiao
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Min Zhao
- Pharmaceutical Diagnostics, GE HealthcareBeijingChina
- Research Center of Medical Big Data, Chinese PLA General HospitalBeijingChina
| | - Liangbo Li
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Liang Zhu
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Xiaoyi Zang
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Bin Gu
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Haizhong Zhang
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| |
Collapse
|
5
|
Chen W, Sá RC, Bai Y, Napel S, Gevaert O, Lauderdale DS, Giger ML. Machine learning with multimodal data for COVID-19. Heliyon 2023; 9:e17934. [PMID: 37483733 PMCID: PMC10362086 DOI: 10.1016/j.heliyon.2023.e17934] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023] Open
Abstract
In response to the unprecedented global healthcare crisis of the COVID-19 pandemic, the scientific community has joined forces to tackle the challenges and prepare for future pandemics. Multiple modalities of data have been investigated to understand the nature of COVID-19. In this paper, MIDRC investigators present an overview of the state-of-the-art development of multimodal machine learning for COVID-19 and model assessment considerations for future studies. We begin with a discussion of the lessons learned from radiogenomic studies for cancer diagnosis. We then summarize the multi-modality COVID-19 data investigated in the literature including symptoms and other clinical data, laboratory tests, imaging, pathology, physiology, and other omics data. Publicly available multimodal COVID-19 data provided by MIDRC and other sources are summarized. After an overview of machine learning developments using multimodal data for COVID-19, we present our perspectives on the future development of multimodal machine learning models for COVID-19.
Collapse
Affiliation(s)
- Weijie Chen
- Medical Imaging and Data Resource Center (MIDRC), USA
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, USA
| | - Rui C. Sá
- Medical Imaging and Data Resource Center (MIDRC), USA
- Department of Medicine, University of California, San Diego, USA
| | - Yuntong Bai
- Medical Imaging and Data Resource Center (MIDRC), USA
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, USA
| | - Sandy Napel
- Medical Imaging and Data Resource Center (MIDRC), USA
- Department of Radiology, Stanford University, USA
| | - Olivier Gevaert
- Medical Imaging and Data Resource Center (MIDRC), USA
- Department of Medicine and Department of Biomedical Data Science, Stanford University, USA
| | - Diane S. Lauderdale
- Medical Imaging and Data Resource Center (MIDRC), USA
- Department of Public Health Sciences, University of Chicago, USA
| | - Maryellen L. Giger
- Medical Imaging and Data Resource Center (MIDRC), USA
- Department of Radiology, University of Chicago, USA
| |
Collapse
|
6
|
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] [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.
Collapse
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
| |
Collapse
|
7
|
Jamshidi N, Senthilvelan J, Dawson DW, Donahue TR, Kuo MD. Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma. BMC Cancer 2023; 23:189. [PMID: 36843111 PMCID: PMC9969670 DOI: 10.1186/s12885-023-10658-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 02/17/2023] [Indexed: 02/28/2023] Open
Abstract
BACKGROUND Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC. METHODS In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses. RESULTS Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05). CONCLUSIONS A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.
Collapse
Affiliation(s)
- Neema Jamshidi
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 757 Westwood Ave, Suite 2125, Los Angeles, CA, 90095, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA.
| | - Jayasuriya Senthilvelan
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 757 Westwood Ave, Suite 2125, Los Angeles, CA 90095 USA
| | - David W. Dawson
- grid.19006.3e0000 0000 9632 6718Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Pathology, University of California, Los Angeles, CA USA
| | - Timothy R. Donahue
- grid.19006.3e0000 0000 9632 6718Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Surgical Oncology, University of California, Los Angeles, CA USA
| | - Michael D. Kuo
- grid.194645.b0000000121742757Medical AI Laboratory Program, The University of Hong Kong, Hong Kong SAR, Hong Kong
| |
Collapse
|
8
|
Lin G, Wang X, Ye H, Cao W. Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment. Technol Cancer Res Treat 2023; 22:15330338231218227. [PMID: 38111330 PMCID: PMC10734346 DOI: 10.1177/15330338231218227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/22/2023] [Accepted: 11/16/2023] [Indexed: 12/20/2023] Open
Abstract
Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.
Collapse
Affiliation(s)
- Guang Lin
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Xiaojia Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hunan Ye
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Wenming Cao
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| |
Collapse
|
9
|
Chen H, Lan X, Yu T, Li L, Tang S, Liu S, Jiang F, Wang L, Huang Y, Cao Y, Wang W, Wang X, Zhang J. Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study. Front Oncol 2022; 12:1076267. [PMID: 36644636 PMCID: PMC9837803 DOI: 10.3389/fonc.2022.1076267] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 12/31/2022] Open
Abstract
Introduction To develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model. Methods This retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group. Radiomics features were extracted from dynamic contrast-enhanced (DCE)-MRI of breast cancer, and genomics features were derived from differentially expressed gene analysis of transcriptome data. Boruta was used for genomics and radiomics data dimension reduction and feature selection. Logistic regression was applied to develop genomics, radiomics, and radiogenomics models to predict ALNM. The performance of the three models was assessed by receiver operating characteristic curves and compared by the Delong test. Results The genomics model was established by nine genomics features, and the radiomics model was established by three radiomics features. The two models showed good discrimination performance in predicting ALNM in breast cancer, with areas under the curves (AUCs) of 0.80, 0.67, and 0.52 for the genomics model and 0.72, 0.68, and 0.71 for the radiomics model in the training, testing and external validation groups, respectively. The radiogenomics model integrated with five genomics features and three radiomics features had a better performance, with AUCs of 0.84, 0.75, and 0.82 in the three groups, respectively, which was higher than the AUC of the radiomics model in the training group and the genomics model in the external validation group (both P < 0.05). Conclusion The radiogenomics model combining radiomics features and genomics features improved the performance to predict ALNM in breast cancer.
Collapse
|
10
|
Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:795-816. [PMID: 36654817 PMCID: PMC9834285 DOI: 10.37349/etat.2022.00113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 12/28/2022] Open
Abstract
The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.
Collapse
Affiliation(s)
- Maurizio Cè
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Maria E. Pellegrino
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Mariachiara Basile
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Adriana Sorce
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | | | - Giancarlo Oliva
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| |
Collapse
|
11
|
Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study. Genes (Basel) 2022; 14:genes14010028. [PMID: 36672769 PMCID: PMC9858814 DOI: 10.3390/genes14010028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/12/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients. METHODS Two radiogenomic cohorts were analyzed (n = 246). Tumor regions were segmented semiautomatically, and 174 imaging features were extracted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to identify significant imaging-pathway associations. Random forest regression was used to predict pathway enrichment scores. Five-fold cross-validation and grid search were used to determine the optimal preprocessing operation and hyperparameters. RESULTS We identified 43 pathways, and 101 radiomic features were significantly related in the discovery cohort (p-value < 0.05). The imaging features of the tumor shape and mid-to-late post-contrast stages showed more transcriptional connections. Ten pathways relevant to functions such as cell cycle showed a high correlation with imaging in both cohorts. The prediction model for the mTORC1 signaling pathway achieved the best performance with the mean absolute errors (MAEs) of 27.29 and 28.61% in internal and external test sets, respectively. CONCLUSIONS The DCE-MRI features were associated with hallmark activities and may improve individualized medicine for BC by noninvasively predicting pathway-level heterogeneity.
Collapse
|
12
|
Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics. Cancers (Basel) 2022; 14:cancers14225507. [PMID: 36428600 PMCID: PMC9688868 DOI: 10.3390/cancers14225507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/06/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.
Collapse
|
13
|
Ming W, Li F, Zhu Y, Bai Y, Gu W, Liu Y, Sun X, Liu X, Liu H. Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique. Comput Biol Med 2022; 150:106147. [PMID: 36201887 DOI: 10.1016/j.compbiomed.2022.106147] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/06/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.
Collapse
Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China; Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, PR China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China.
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
| |
Collapse
|
14
|
Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
Collapse
|
15
|
Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions. Methods Protoc 2022; 5:mps5050078. [PMID: 36287050 PMCID: PMC9611546 DOI: 10.3390/mps5050078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 12/05/2022] Open
Abstract
Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).
Collapse
|
16
|
Ming W, Zhu Y, Bai Y, Gu W, Li F, Hu Z, Xia T, Dai Z, Yu X, Li H, Gu Y, Yuan S, Zhang R, Li H, Zhu W, Ding J, Sun X, Liu Y, Liu H, Liu X. Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer. Front Oncol 2022; 12:943326. [PMID: 35965527 PMCID: PMC9366134 DOI: 10.3389/fonc.2022.943326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.
Collapse
Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zixi Hu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Tiansong Xia
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuolei Dai
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiafei Yu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huamei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yu Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Shaoxun Yuan
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Rongxin Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Haitao Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wenyong Zhu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Jianing Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| |
Collapse
|
17
|
Liu H, Wang Y, Liu Y, Lin D, Zhang C, Zhao Y, Chen L, Li Y, Yuan J, Chen Z, Yu J, Kong W, Chen T. Contrast-Enhanced Computed Tomography–Based Radiogenomics Analysis for Predicting Prognosis in Gastric Cancer. Front Oncol 2022; 12:882786. [PMID: 35814414 PMCID: PMC9257248 DOI: 10.3389/fonc.2022.882786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
Objective The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC). Material RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers. Methods The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated. Results Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. Conclusions The novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.
Collapse
Affiliation(s)
- Han Liu
- Department of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yiyun Wang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Yingqiao Liu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Dingyi Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Cangui Zhang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Yuyun Zhao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Li Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Yi Li
- Department of Radiology, Southern Medical University, Guangzhou, China
| | - Jianyu Yuan
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Zhao Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Wentao Kong
- Department of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
- *Correspondence: Tao Chen, ; Wentao Kong,
| | - Tao Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
- *Correspondence: Tao Chen, ; Wentao Kong,
| |
Collapse
|
18
|
Darvish L, Bahreyni-Toossi MT, Roozbeh N, Azimian H. The role of radiogenomics in the diagnosis of breast cancer: a systematic review. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00310-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
One of the most common cancers diagnosed worldwide is breast cancer (BC), which is the leading cause of cancer death among women. The radiogenomics method is more accurate for managing and inhibiting this disease, which takes individual diagnosis on genes, environments, and lifestyles of each person. The present study aims to highlight the current state-of-the-art, the current role and limitations, and future directions of radiogenomics in breast cancer.
Method
This systematic review article was searched from databases such as Embase, PubMed, Web of Science, Google Scholar, Scopus, and Cochrane Library without any date or language limitations of databases. Searches were performed using Boolean OR and AND operators between the main terms and keywords of particular topic of the subject under investigation. All retrospective, prospective, cohort, and pilot studies were included, which were provided with more details about the topic. Articles such as letter to the editor, review, and short communications were excluded because of lack of information, discussions, or use of radiogenomics method on other cancers. For quality assessment of articles, STROBE checklist was used.
Result
For the systematic review, 18 articles were approved after assessing the full text of selected articles. In this review, 3614 patients with BC of selected articles were evaluated, and all radiogenomics were associated with more power in classification, differential diagnosis, and prognosis of BC. Among the various modalities to predict genomic indicators and molecular subtypes, DCE-MRI has the higher performance and finally the highest amount of AUC value (0.956) belonged to PI3K gene.
Conclusion
This review shows that radiogenomics can help with the diagnosis and treatment of breast cancer in patients. It has shown that recognizing and specifying radiogenomic phenotypes in the genomic signatures can be helpful in treatment and diagnosis of disease. The molecular methods used in these articles are limited to miRNAs expression, gene expression, Ki67 proliferation index, next-generation RNA sequencing, whole RNA sequencing, and molecular histopathology that can be completed in future studies by other methods such as exosomal miRNAs, specific proteins expression, DNA repair capacity, and other biomarkers that have prognostic and predictive value for cancer treatment response. Studies with control group and large sample size for evaluation of radiogenomics in diagnosis and treatment recommended.
Collapse
|
19
|
Kayadibi Y, Kocak B, Ucar N, Akan YN, Akbas P, Bektas S. Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models. Acad Radiol 2022; 29 Suppl 1:S116-S125. [PMID: 33744071 DOI: 10.1016/j.acra.2021.02.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/28/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer. METHODS In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model. RESULTS Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively. CONCLUSION ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.
Collapse
|
20
|
Fan M, Cui Y, You C, Liu L, Gu Y, Peng W, Bai Q, Gao X, Li L. Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor-Positive Breast Cancer: A Multicohort Study. Radiology 2021; 302:516-524. [PMID: 34846204 DOI: 10.1148/radiol.2021210738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background Radiogenomics explores the association between imaging features and genomic assays to uncover relevant prognostic features; however, the prognostic implications of the derived signatures remain unclear. Purpose To identify preoperative radiogenomic signatures of estrogen receptor-positive breast cancer associated with the Oncotype DX recurrence score (RS) and to evaluate whether they are biomarkers for survival and responses to neoadjuvant chemotherapy (NACT). Materials and Methods In this retrospective multicohort study, three data sets were analyzed. The radiogenomic development data set, with preoperative dynamic contrast-enhanced MRI and RS data obtained between January 2016 and October 2019 was used to identify radiogenomic signatures. Prognostic implications of the imaging signatures were assessed by measuring overall survival and recurrence-free survival in the prognostic assessment data set using a multivariable Cox proportional hazards model. The therapeutic implication of the radiogenomic signatures was evaluated by determining their ability to predict the response to NACT using the treatment assessment data set obtained between August 2015 and March 2019. Prediction performance was estimated by using the area under the receiver operating characteristic curve (AUC). Results The final cohorts included a radiogenomic development data set with 130 women (mean age, 52 years ± 10 [standard deviation]), a prognostic assessment data set with 116 women (mean age, 48 years ± 9), and a treatment assessment data set with 135 women (mean age, 50 years ± 11). Radiogenomic signatures (n = 11) of texture and morphologic and statistical features were identified to generate the predicted RS (R2 = 0.33, P < .001). A predicted RS greater than 29.9 was associated with poor overall and recurrence-free survival (P = .001 and P = .007, respectively); predicted RS was greater in women with a good NACT response (30.51 ± 6.92 vs 27.35 ± 4.04 [responders vs nonresponders], P = .001). By combining the predicted RS and complementary features, the model achieved improved performance in prediction of the NACT response (AUC, 0.85; P < .001). Conclusion Radiogenomic signatures associated with genomic assays provide markers of prognosis and treatment in estrogen receptor-positive breast cancer. © RSNA, 2021 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Ming Fan
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Yajing Cui
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Chao You
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Li Liu
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Yajia Gu
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Weijun Peng
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Qianming Bai
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Xin Gao
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| | - Lihua Li
- From the Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, Zhejiang, China (M.F., Y.C., L. Li); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (C.Y., L. Liu, Y.G., W.P., Q.B.); and Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (X.G.)
| |
Collapse
|
21
|
Huang Y, Wei L, Hu Y, Shao N, Lin Y, He S, Shi H, Zhang X, Lin Y. Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer. Front Oncol 2021; 11:706733. [PMID: 34490107 PMCID: PMC8416497 DOI: 10.3389/fonc.2021.706733] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 07/28/2021] [Indexed: 12/30/2022] Open
Abstract
Objective To investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way. Materials and Methods Patients diagnosed with clinical T2–4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed. A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). The performances of binary classification models were assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). And the performances of multiclass classification models were assessed via AUC, overall accuracy, precision, recall rate, and F1-score. Results A total of 162 patients (mean age, 46.91 ± 10.08 years) were enrolled in this study; 30 were low-AR expression and 132 were high-AR expression. HR+/HER2− cancers were diagnosed in 56 cases (34.6%), HER2+ cancers in 81 cases (50.0%), and TNBC in 25 patients (15.4%). There was no significant difference in clinicopathologic characteristics between low-AR and high-AR groups (P > 0.05), except the menopausal status, ER, PR, HER2, and Ki-67 index (P = 0.043, <0.001, <0.001, 0.015, and 0.006, respectively). No significant difference in clinicopathologic characteristics was observed among three molecular subtypes except the AR status and Ki-67 (P = <0.001 and 0.012, respectively). The Multilayer Perceptron (MLP) showed the best performance in discriminating AR expression, with an AUC of 0.907 and an accuracy of 85.8% in the testing dataset. The highest performances were obtained for discriminating TNBC vs. non-TNBC (AUC: 0.965, accuracy: 92.6%), HER2+ vs. HER2− (AUC: 0.840, accuracy: 79.0%), and HR+/HER2− vs. others (AUC: 0.860, accuracy: 82.1%) using MLP as well. The micro-AUC of MLP multiclass classification model was 0.896, and the overall accuracy was 0.735. Conclusions Multi-parametric MRI-based radiomics combining with machine learning approaches provide a promising method to predict the molecular subtype and AR expression of breast cancer non-invasively.
Collapse
Affiliation(s)
- Yuhong Huang
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lihong Wei
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yalan Hu
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Nan Shao
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shaofu He
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huijuan Shi
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
22
|
Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, Mao L, Ren W, Wang J, Lin L, Wu Z, Liu J, Ou Q, Hu Q, Li A, Chen K, Li C, Lu N, Li X, Su F, Liu Q, Xie C, Yao H. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine 2021; 69:103460. [PMID: 34233259 PMCID: PMC8261009 DOI: 10.1016/j.ebiom.2021.103460] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/30/2021] [Accepted: 06/11/2021] [Indexed: 01/26/2023] Open
Abstract
Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer. Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558. Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.
Collapse
Affiliation(s)
- Yunfang Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zifan He
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jie Ouyang
- Department of Breast Surgery, Tungwah Hospital, Sun Yat-sen University, Dongguan, China
| | - Yujie Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongjian Chen
- Department of Medical Oncology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yang Gu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Luhui Mao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Ren
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jue Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lili Lin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhuo Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jingwen Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qiyun Ou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University, Foshan, China
| | - Anlin Li
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chenchen Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Nian Lu
- Imaging Diagnostic and Interventional Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaohong Li
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Fengxi Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chuanmiao Xie
- Imaging Diagnostic and Interventional Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Herui Yao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
23
|
Cipollari S, Jamshidi N, Du L, Sung K, Huang D, Margolis DJ, Huang J, Reiter RE, Kuo MD. Tissue clearing techniques for three-dimensional optical imaging of intact human prostate and correlations with multi-parametric MRI. Prostate 2021; 81:521-529. [PMID: 33876838 PMCID: PMC9014804 DOI: 10.1002/pros.24129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 03/26/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Tissue clearing technologies have enabled remarkable advancements for in situ characterization of tissues and exploration of the three-dimensional (3D) relationships between cells, however, these studies have predominantly been performed in non-human tissues and correlative assessment with clinical imaging has yet to be explored. We sought to evaluate the feasibility of tissue clearing technologies for 3D imaging of intact human prostate and the mapping of structurally and molecularly preserved pathology data with multi-parametric volumetric MR imaging (mpMRI). METHODS Whole-mount prostates were processed with either hydrogel-based CLARITY or solvent-based iDISCO. The samples were stained with a nuclear dye or fluorescently labeled with antibodies against androgen receptor, alpha-methylacyl coenzyme-A racemase, or p63, and then imaged with 3D confocal microscopy. The apparent diffusion coefficient and Ktrans maps were computed from preoperative mpMRI. RESULTS Quantitative analysis of cleared normal and tumor prostate tissue volumes displayed differences in 3D tissue architecture, marker-specific cell staining, and cell densities that were significantly correlated with mpMRI measurements in this initial, pilot cohort. CONCLUSIONS 3D imaging of human prostate volumes following tissue clearing is a feasible technique for quantitative radiology-pathology correlation analysis with mpMRI and provides an opportunity to explore functional relationships between cellular structures and cross-sectional clinical imaging.
Collapse
Affiliation(s)
- Stefano Cipollari
- Medical Artificial Intelligence Laboratory Program, The University of Hong Kong, Hong Kong SAR
- Department of Radiology, La Sapienza, The University of Rome, Italy
| | - Neema Jamshidi
- Medical Artificial Intelligence Laboratory Program, The University of Hong Kong, Hong Kong SAR
- Department of Radiological Sciences, University of California, Los Angeles, David Geffen School of Medicine, California
| | - Liutao Du
- Department of Radiological Sciences, University of California, Los Angeles, David Geffen School of Medicine, California
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California, Los Angeles, David Geffen School of Medicine, California
| | - Danshan Huang
- Department of Radiological Sciences, University of California, Los Angeles, David Geffen School of Medicine, California
| | | | - Jiaoti Huang
- Department of Pathology, Duke University School of Medicine, North Carolina
| | - Robert E. Reiter
- Department of Urology, University of California, Los Angeles, David Geffen School of Medicine, California
| | - Michael D. Kuo
- Medical Artificial Intelligence Laboratory Program, The University of Hong Kong, Hong Kong SAR
- Correspondence should be addressed to MDK ()
| |
Collapse
|
24
|
Abstract
With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.
Collapse
Affiliation(s)
- Z. Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - I. Wamelink
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - S. Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
25
|
Leithner D, Bernard-Davila B, Martinez DF, Horvat JV, Jochelson MS, Marino MA, Avendano D, Ochoa-Albiztegui RE, Sutton EJ, Morris EA, Thakur SB, Pinker K. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Mol Imaging Biol 2021; 22:453-461. [PMID: 31209778 PMCID: PMC7062654 DOI: 10.1007/s11307-019-01383-w] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Purpose To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.
Collapse
Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Blanca Bernard-Davila
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Joao V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Nuevo Leon, Mexico
| | - R Elena Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth J Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA. .,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
| |
Collapse
|
26
|
Shui L, Ren H, Yang X, Li J, Chen Z, Yi C, Zhu H, Shui P. The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front Oncol 2021; 10:570465. [PMID: 33575207 PMCID: PMC7870863 DOI: 10.3389/fonc.2020.570465] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/08/2020] [Indexed: 02/05/2023] Open
Abstract
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.
Collapse
Affiliation(s)
- Lin Shui
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haoyu Ren
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany
| | - Xi Yang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jian Li
- Department of Pharmacy, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Ziwei Chen
- Department of Nephrology, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, China
| | - Cheng Yi
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhu
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pixian Shui
- School of Pharmacy, Southwest Medical University, Luzhou, China
| |
Collapse
|
27
|
[Multimodal, multiparametric and genetic breast imaging]. Radiologe 2021; 61:183-191. [PMID: 33464404 DOI: 10.1007/s00117-020-00801-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2020] [Indexed: 10/22/2022]
Abstract
CLINICAL/METHODOLOGICAL ISSUE Multiparametric magnetic resonance imaging (MRI) aims to visualize and quantify biological, physiological and pathological processes at the cellular and molecular level and provides valuable information about key processes in cancer development and progression. "Omics" strategies (genomics, transcriptomics, proteomics, metabolomics) have many uses in oncology. STANDARD RADIOLOGICAL METHODS Multiparametric MRI of the breast currently includes T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI (DCE-MRI) METHODOLOGICAL INNOVATIONS: Additional parameters such as proton magetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), blood oxygen level-dependent (BOLD), hyperpolarized (HP) MRI or lipid MRS are currently being developed and are being evaluated in breast cancer diagnostics. ACHIEVEMENTS Radiogenomics is a new direction in medical science that has been made possible by significant advances in imaging and image analysis methods, as well as the development of techniques to extract and correlate various imaging parameters with "omics" data. The aim of radiogenomics is to correlate imaging characteristics (phenotypes) with gene expression patterns, gene mutations and other genome-associated properties and is the evolution of the correlation between radiology and pathology from the anatomical-histological to the molecular level. Quantitative and qualitative imaging biomarkers provide insights into the complex tumor biology. Initial results suggest that radiogemics will play an important role in the diagnosis, prognosis, and treatment of breast cancer. PRACTICAL RECOMMENDATIONS This article provides an overview of the current state of radiogenomics of the breast and future applications and challenges.
Collapse
|
28
|
Arefan D, Chai R, Sun M, Zuley ML, Wu S. Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features. Med Phys 2020; 47:6334-6342. [PMID: 33058224 DOI: 10.1002/mp.14538] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 08/07/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. METHODS A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. RESULTS The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80-0.83) and 0.82 (95% CI: 0.81-0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). CONCLUSIONS Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.
Collapse
Affiliation(s)
- Dooman Arefan
- Department of Radiology, University of Pittsburgh, School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Ruimei Chai
- Department of Radiology, First Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Min Sun
- UPMC Hillman Cancer Center at St. Margaret, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15215, USA
| | - Margarita L Zuley
- Department of Radiology, University of Pittsburgh, School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.,Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA
| | - Shandong Wu
- Departments of Radiology of Biomedical Informatics of Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| |
Collapse
|
29
|
Incoronato M, Mirabelli P, Grimaldi AM, Soricelli A, Salvatore M. Correlating imaging parameters with molecular data: An integrated approach to improve the management of breast cancer patients. Int J Biol Markers 2020; 35:47-50. [PMID: 32079469 DOI: 10.1177/1724600819899665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The goal of this review is to provide an overview of the studies aimed at integrating imaging parameters with molecular biomarkers for improving breast cancer patient's diagnosis and prognosis. The use of diagnostic imaging to extract quantitative parameters related to the morphology, metabolism, and functionality of tumors, as well as their correlation with cancer tissue biomarkers is an emerging research topic. Thanks to the development of imaging biobanks and the technological tools required for extraction of imaging parameters including radiomic features, it is possible to integrate imaging markers with genetic data. This new field of study represents the evolution of radiology-pathology correlation from an anatomic-histologic level to a genetic level, which paves new interesting perspectives for breast cancer management.
Collapse
Affiliation(s)
| | | | | | - Andrea Soricelli
- IRCCS SDN, Naples, Italy.,Department of Motor Sciences & Healthiness, University of Naples Parthenope, Naples, Italy
| | | |
Collapse
|
30
|
Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm. J Digit Imaging 2020; 32:276-282. [PMID: 30706213 DOI: 10.1007/s10278-019-00179-2] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer. A CNN architecture was designed with 14 layers. Residual connections were used in the earlier layers to allow stabilization of gradients during backpropagation. Inception style layers were utilized deeper in the network to allow learned segregation of more complex feature mappings. Extensive regularization was utilized including dropout, L2, feature map dropout, and transition layers. The class imbalance was addressed by doubling the input of underrepresented classes and utilizing a class sensitive cost function. Parameters were tuned based on a 20% validation group. A class balanced holdout set of 40 patients was utilized as the testing set. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. Seventy-four luminal A, 106 luminal B, 13 HER2+, and 23 basal breast tumors were evaluated. Testing set accuracy was measured at 70%. The class normalized macro area under receiver operating curve (ROC) was measured at 0.853. Non-normalized micro-aggregated AUC was measured at 0.871, representing improved discriminatory power for the highly represented Luminal A and Luminal B subtypes. Aggregate sensitivity and specificity was measured at 0.603 and 0.958. MRI analysis of breast cancers utilizing a novel CNN can predict the molecular subtype of breast cancers. Larger data sets will likely improve our model.
Collapse
|
31
|
Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging. Diagnostics (Basel) 2020; 10:diagnostics10070492. [PMID: 32708512 PMCID: PMC7400681 DOI: 10.3390/diagnostics10070492] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/13/2020] [Accepted: 07/15/2020] [Indexed: 01/01/2023] Open
Abstract
The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.
Collapse
|
32
|
Cho N. Breast Cancer Radiogenomics: Association of Enhancement Pattern at DCE MRI with Deregulation of mTOR Pathway. Radiology 2020; 296:288-289. [PMID: 32478609 DOI: 10.1148/radiol.2020201607] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Nariya Cho
- From the Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| |
Collapse
|
33
|
The emerging role of the long non-coding RNA HOTAIR in breast cancer development and treatment. J Transl Med 2020; 18:152. [PMID: 32245498 PMCID: PMC7119166 DOI: 10.1186/s12967-020-02320-0] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 03/27/2020] [Indexed: 01/17/2023] Open
Abstract
Despite considering vast majority of the transcribed molecules as merely noise RNA in the last decades, recent advances in the field of molecular biology revealed the mysterious role of long non-coding RNAs (lncRNAs), as a massive part of functional non-protein-coding RNAs. As a crucial lncRNA, HOX antisense intergenic RNA (HOTAIR) has been shown to participate in different processes of normal cell development. Aberrant overexpression of this lncRNA contributes to breast cancer progression, through different molecular mechanisms. In this review, we briefly discuss the structure of HOTAIR in the context of genome and impact of this lncRNA on normal human development. We subsequently summarize the potential role of HOTAIR overexpression on different processes of breast cancer development. Ultimately, the relationship of this lncRNA with different therapeutic approaches is discussed.
Collapse
|
34
|
Reig B, Heacock L, Lewin A, Cho N, Moy L. Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer. J Magn Reson Imaging 2020; 52. [DOI: 10.1002/jmri.27145] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 12/25/2022] Open
Affiliation(s)
- Beatriu Reig
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Laura Heacock
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Alana Lewin
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Nariya Cho
- Department of Radiology Seoul National University Hospital Seoul Republic of Korea
- Department of Radiology Seoul National University College of Medicine Seoul Republic of Korea
| | - Linda Moy
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
- Bernard and Irene Schwartz Center for Biomedical Imaging Department of Radiology, New York University Grossman School of Medicine New York New York USA
- Center for Advanced Imaging Innovation and Research (CAI2 R) New York University Grossman School of Medicine New York New York USA
| |
Collapse
|
35
|
Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence. Sci Rep 2020; 10:2976. [PMID: 32076078 PMCID: PMC7031504 DOI: 10.1038/s41598-020-59923-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 02/05/2020] [Indexed: 01/09/2023] Open
Abstract
This paper evaluated 3-dimensional radiomics features of breast magnetic resonance imaging (MRI) as prognostic factors for predicting systemic recurrence in triple-negative breast cancer (TNBC) and validated the results with a different MRI scanner. The Rad score was generated from 3-dimensional radiomic features of MRI for 231 TNBCs (training set (GE scanner), n = 182; validation set (Philips scanner), n = 49). The Clinical and Rad models to predict systemic recurrence were built up and the models were externally validated. In the training set, the Rad score was significantly higher in the group with systemic recurrence (median, −8.430) than the group without (median, −9.873, P < 0.001). The C-index of the Rad model to predict systemic recurrence in the training set was 0.97, which was significantly higher than in the Clinical model (0.879; P = 0.009). When the models were externally validated, the C-index of the Rad model was 0.848, lower than the 0.939 of the Clinical model, although the difference was not statistically significant (P = 0.100). The Rad model for predicting systemic recurrence in TNBC showed a significantly higher C-index than the Clinical model. However, external validation with a different MRI scanner did not show the Rad model to be superior over the Clinical model.
Collapse
|
36
|
Park AY, Han MR, Park KH, Kim JS, Son GS, Lee HY, Chang YW, Park EK, Cha SH, Cho Y, Hong H, Cho KR, Song SE, Woo OH, Lee JH, Cha J, Seo BK. Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing. Radiology 2020; 295:24-34. [PMID: 32013793 DOI: 10.1148/radiol.2020191368] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Radiogenomic investigations for breast cancer provide an understanding of tumor heterogeneity and discover image phenotypes of genetic variation. However, there is little research on the correlations between US features of breast cancer and whole-transcriptome profiling. Purpose To explore US phenotypes reflecting genetic alteration relevant to breast cancer treatment and prognosis by comparing US images of tumor with their RNA sequencing results. Materials and Methods From January to October 2016, B-mode and vascular US images in 31 women (mean age, 49 years ± 9 [standard deviation]) with breast cancer were prospectively analyzed. B-mode features included size, shape, echo pattern, orientation, margin, and calcifications. Vascular features were evaluated by using microvascular US and contrast agent-enhanced US: vascular index, vessel morphologic features, distribution, penetrating vessels, enhancement degree, order, margin, internal homogeneity, and perfusion defect. RNA sequencing was conducted with total RNA obtained from a surgical specimen by using next-generation sequencing. US features were compared with gene expression profiles, and ingenuity pathway analysis was used to analyze gene networks, enriched functions, and canonical pathways associated with breast cancer. The P value for differential expression was extracted by using a parametric F test comparing nested linear models. Results Thirteen US features were associated with various patterns of 340 genes (P < .05). Nonparallel orientation at B-mode US was associated with upregulation of TFF1 (log twofold change [log2FC] = 4.0; P < .001), TFF3 (log2FC = 2.5; P < .001), AREG (log2FC = 2.6; P = .005), and AGR3 (log2FC = 2.6; P = .003). Complex vessel morphologic structure was associated with upregulation of FZD8 (log2FC = 2.0; P = .01) and downregulation of IGF1R (log2FC = -2.0; P = .006) and CRIPAK (log2FC = -2.4; P = .01). The top networks with regard to orientation or vessel morphologic structure were associated with cell cycle, death, and proliferation. Conclusion Compared with RNA sequencing, B-mode and vascular US features reflected genomic alterations associated with hormone receptor status, angiogenesis, or prognosis in breast cancer. © RSNA, 2020 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Ah Young Park
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Mi-Ryung Han
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Kyong Hwa Park
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Jung Sun Kim
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Gil Soo Son
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Hye Yoon Lee
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Young Woo Chang
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Eun Kyung Park
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Sang Hoon Cha
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Yunjung Cho
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Hyosun Hong
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Kyu Ran Cho
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Sung Eun Song
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Ok Hee Woo
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Ju-Han Lee
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Jaehyung Cha
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| | - Bo Kyoung Seo
- From the Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan city, Gyeonggi-do, 15355, Republic of Korea (A.Y.P., E.K.P., S.H.C., B.K.S.); Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (A.Y.P.); Department of Laboratory Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (M.R.H., Y.C., H.H.); Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea (M.R.H.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.H.P.); Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.S.K.); Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (G.S.S., H.Y.L., Y.W.C.); Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea (K.R.C., S.E.S.); Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea (O.H.W.); Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.H.L.); and Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea (J.C.)
| |
Collapse
|
37
|
Jamshidii N, Chang J, Mock K, Nguyen B, Dauphine C, Kuo MD. Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests. PLoS One 2020; 15:e0226634. [PMID: 31923222 PMCID: PMC6953781 DOI: 10.1371/journal.pone.0226634] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Accepted: 12/01/2019] [Indexed: 01/19/2023] Open
Abstract
Purpose The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer. Materials and methods This retrospective, single-institution analysis of 219 patients involved two cohorts using one of two FDA approved transcriptome-based tests that were performed as part of the clinical care of breast cancer patients at Harbor-UCLA Medical Center between April 2008 and January 2013. BI-RADS descriptive terminology was collected from the corresponding ultrasound reports for each patient in conjunction with transcriptomic test results. Recursive partitioning and regression trees were used to test and validate classification of the two cohorts. Results The area under the curve (AUC) of the receiver operator curves (ROC) for the regression classifier between the two FDA approved tests and ultrasound features were 0.77 and 0.65, respectively; they employed the ‘margins’, ‘retrotumoral’, and ‘internal echoes’ feature descriptors. Notably, the ‘retrotumoral’ and mass ‘margins’ features were used in both classification trees. The identification of sonographic correlates of gene tests provides added value to the ultrasound exam without incurring additional procedures or testing. Conclusions The predictive capability using structured language from diagnostic ultrasound reports (BI-RADS) was moderate for the two tests, and provides added value from ultrasound imaging without incurring any additional costs. Incorporation of additional measures, such as ultrasound contrast enhancement, with validation in larger, prospective studies may further substantiate these results and potentially demonstrate even greater predictive utility.
Collapse
Affiliation(s)
- Neema Jamshidii
- UCLA Department of Radiological Sciences, Los Angeles, CA, United States of America
- UCLA David Geffen School of Medicine, Los Angeles, CA, United States of America
- * E-mail: (MDK); (NJ)
| | - Jason Chang
- UCLA David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Kyle Mock
- Harbor-UCLA Medical Center, Department of Surgery, Los Angeles, CA, United States of America
| | - Brian Nguyen
- Harbor-UCLA Medical Center, Department of Surgery, Los Angeles, CA, United States of America
| | - Christine Dauphine
- Harbor-UCLA Medical Center, Department of Surgery, Los Angeles, CA, United States of America
| | - Michael D. Kuo
- Department of Radiology, The University of Hong Kong, Hong Kong, China
- * E-mail: (MDK); (NJ)
| |
Collapse
|
38
|
Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging 2020; 11:1. [PMID: 31901171 PMCID: PMC6942081 DOI: 10.1186/s13244-019-0795-6] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/25/2019] [Indexed: 02/07/2023] Open
Abstract
Background Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing. Main body In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis. Conclusion Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.
Collapse
Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.
| | - Isaac Daimiel
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Wien, Austria
| |
Collapse
|
39
|
Grimm LJ, Mazurowski MA. Breast Cancer Radiogenomics: Current Status and Future Directions. Acad Radiol 2020; 27:39-46. [PMID: 31818385 DOI: 10.1016/j.acra.2019.09.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 07/17/2019] [Accepted: 09/08/2019] [Indexed: 12/13/2022]
Abstract
Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.
Collapse
|
40
|
Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes. Sci Rep 2019; 9:17847. [PMID: 31780739 PMCID: PMC6882909 DOI: 10.1038/s41598-019-54371-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 11/14/2019] [Indexed: 01/30/2023] Open
Abstract
Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer.
Collapse
|
41
|
Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:6978650. [PMID: 31827586 PMCID: PMC6885255 DOI: 10.1155/2019/6978650] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 10/10/2019] [Indexed: 12/28/2022]
Abstract
Background and Objective Breast cancer is a major cause of mortality among women if not treated in early stages. Recognizing molecular markers from DCE-MRI directly to distinguish the four molecular subtypes without invasive biopsy is helpful for guiding treatment plans for breast cancer, which provides a fast way to consequential treatment plan decision in early time and best opportunity for patients. Methods This study presents an approach of molecular subtypes recognition from breast cancer image phenotypes by radiomics. An improved region growth algorithm with dynamic threshold without user interaction is proposed for cancer lesion segmentation, which gives the precise border of lesion other than area with background. The lesions are extracted automatically based on radiologists' annotation which guarantees the lesion is segmented correctly. Various features are extracted on lesions data including texture, morphology, dynamic kinetics, and statistics features carried out on a large patient cohort, which are used to validate the relationship between image phenotypes and the molecular subtypes. A new algorithm of multimodel-based recursive feature elimination is applied on the radiomics data generated by the feature extraction process. This method obtains the feature subset with stable performance for different classification models, and the gradient boosting decision tree model gets the best results of both classification performance and imbalance performance on molecular subtypes. Result From the experimental results, 69 optimal features from 143 original features are found by the multimodel-based recursive feature elimination algorithms and the gradient boosting decision tree classifier obtains a good performance with accuracy 0.87, precise 0.88, recall 0.87, and F1-score 0.87. The dataset with 637 patients in this paper has serious imbalance problem on different molecular subtypes, and the the robust features that are generated by multimodel-based recursive feature eliminiation algorithm make the gradient boosting decision tree classifier have good behaviors. The recognition precision for the four molecular subtypes of luminal A, luminal B, HER-2, and basal-like are 0.91, 0.89, 0.83, and 0.87, respectively. Conclusions The improved lesion segmentation method gives more precise lesion edge, which not only saves the time of automatic extraction of lesion region of interest without threshold setting for each case, but also prevents the segmentation error by manual and prejudice from different radiologists. The feature selection algorithm of multimodel-based recursive feature elimination has the ability to find robust and optimal features that distinguish the four molecular subtypes from image phenotypes. The gradient boosting decision tree classifier rather plays a main role in recognition than other models used in this paper.
Collapse
|
42
|
Fan M, Xia P, Liu B, Zhang L, Wang Y, Gao X, Li L. Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast Cancer Res 2019; 21:112. [PMID: 31623683 PMCID: PMC6798414 DOI: 10.1186/s13058-019-1199-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 09/13/2019] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Heterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore their biological importance. METHODS Imaging features (n = 14), such as texture, histogram distribution and morphological features, were extracted to determine their associations with recurrence-free survival (RFS) in patients in the training cohort (n = 61) from The Cancer Imaging Archive (TCIA). The prognostic value of the features was evaluated in an independent dataset of 173 patients (i.e. the reproducibility cohort) from the TCIA I-SPY 1 TRIAL dataset. Radiogenomic analysis was performed in an additional cohort, the radiogenomic cohort (n = 87), using DCE-MRI from TCGA-BRCA and corresponding gene expression data from The Cancer Genome Atlas (TCGA). The MRI tumour area was decomposed by convex analysis of mixtures (CAM), resulting in 3 components that represent plasma input, fast-flow kinetics and slow-flow kinetics. The prognostic MRI features were associated with the gene expression module in which the pathway was analysed. Furthermore, a multigene signature for each prognostic imaging feature was built, and the prognostic value for RFS and overall survival (OS) was confirmed in an additional cohort from TCGA. RESULTS Three image features (i.e. the maximum probability from the precontrast MR series, the median value from the second postcontrast series and the overall tumour volume) were independently correlated with RFS (p values of 0.0018, 0.0036 and 0.0032, respectively). The maximum probability feature from the fast-flow kinetics subregion was also significantly associated with RFS and OS in the reproducibility cohort. Additionally, this feature had a high correlation with the gene expression module (r = 0.59), and the pathway analysis showed that Ras signalling, a breast cancer-related pathway, was significantly enriched (corrected p value = 0.0044). Gene signatures (n = 43) associated with the maximum probability feature were assessed for associations with RFS (p = 0.035) and OS (p = 0.027) in an independent dataset containing 1010 gene expression samples. Among the 43 gene signatures, Ras signalling was also significantly enriched. CONCLUSIONS Dynamic pattern deconvolution revealed that tumour heterogeneity was associated with poor survival and cancer-related pathways in breast cancer.
Collapse
Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Pingping Xia
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Bin Liu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Lin Zhang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China.
| |
Collapse
|
43
|
Leithner D, Horvat JV, Ochoa-Albiztegui RE, Thakur S, Wengert G, Morris EA, Helbich TH, Pinker K. Imaging and the completion of the omics paradigm in breast cancer. Radiologe 2019; 58:7-13. [PMID: 29947931 PMCID: PMC6244523 DOI: 10.1007/s00117-018-0409-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Within the field of oncology, “omics” strategies—genomics, transcriptomics, proteomics, metabolomics—have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with “omics” data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology–pathology correlation from the anatomical–histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.
Collapse
Affiliation(s)
- D Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - J V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA
| | - R E Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA
| | - S Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA
| | - G Wengert
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - E A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA
| | - T H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - K Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria.
| |
Collapse
|
44
|
Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019; 52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022] Open
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
Collapse
Affiliation(s)
- Beatriu Reig
- The Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krzysztof J Geras
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
| |
Collapse
|
45
|
Antonelli L, Guarracino MR, Maddalena L, Sangiovanni M. Integrating imaging and omics data: A review. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.032] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
46
|
Korn RL, Rahmanuddin S, Borazanci E. Use of Precision Imaging in the Evaluation of Pancreas Cancer. Cancer Treat Res 2019; 178:209-236. [PMID: 31209847 DOI: 10.1007/978-3-030-16391-4_8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Pancreas cancer is an aggressive and fatal disease that will become one of the leading causes of cancer mortality by 2030. An all-out effort is underway to better understand the basic biologic mechanisms of this disease ranging from early development to metastatic disease. In order to change the course of this disease, diagnostic radiology imaging may play a vital role in providing a precise, noninvasive method for early diagnosis and assessment of treatment response. Recent progress in combining medical imaging, advanced image analysis and artificial intelligence, termed radiomics, can offer an innovate approach in detecting the earliest changes of tumor development as well as a rapid method for the detection of response. In this chapter, we introduce the principles of radiomics and demonstrate how it can provide additional information into tumor biology, early detection, and response assessments advancing the goals of precision imaging to deliver the right treatment to the right person at the right time.
Collapse
Affiliation(s)
- Ronald L Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA. .,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA. .,Imaging Endpoints Core Lab, Scottsdale, AZ, USA.
| | | | - Erkut Borazanci
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA.,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA
| |
Collapse
|
47
|
Woolf DK, Li SP, Detre S, Liu A, Gogbashian A, Simcock IC, Stirling J, Kosmin M, Cook GJ, Siddique M, Dowsett M, Makris A, Goh V. Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry. BIOMARKERS IN CANCER 2019; 11:1179299X19851513. [PMID: 31210736 PMCID: PMC6552350 DOI: 10.1177/1179299x19851513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/23/2019] [Indexed: 11/17/2022]
Abstract
BACKGROUND Tumour heterogeneity is considered an important mechanism of treatment failure. Imaging-based assessment of tumour heterogeneity is showing promise but the relationship between these mathematically derived measures and accepted 'gold standards' of tumour biology such as immunohistochemical measures is not established. METHODS A total of 20 women with primary breast cancer underwent a research dynamic contrast-enhanced computed tomography prior to treatment with data being available for 15 of these. Texture analysis was performed of the primary tumours to extract 13 locoregional and global parameters. Immunohistochemical analysis associations were assessed by the Spearman rank correlation. RESULTS Hypoxia-inducible factor-1α was correlated with first-order kurtosis (r = -0.533, P = .041) and higher order neighbourhood grey-tone difference matrix coarseness (r = 0.54, P = .038). Vascular maturity-related smooth muscle actin was correlated with higher order grey-level run-length long-run emphasis (r = -0.52, P = .047), fractal dimension (r = 0.613, P = .015), and lacunarity (r = -0.634, P = .011). Micro-vessel density, reflecting angiogenesis, was also associated with lacunarity (r = 0.547, P = .035). CONCLUSIONS The associations suggest a biological basis for these image-based heterogeneity features and support the use of imaging, already part of standard care, for assessing intratumoural heterogeneity.
Collapse
Affiliation(s)
- David K Woolf
- Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Sonia P Li
- Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK
| | - Simone Detre
- Ralph Lauren Centre for Breast Cancer Research, Royal Marsden Hospital, London, UK
| | - Alison Liu
- Division of Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Andrew Gogbashian
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK
| | - Ian C Simcock
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK
| | - James Stirling
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK
| | - Michael Kosmin
- Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK
| | - Gary J Cook
- Division of Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Muhammad Siddique
- Division of Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Mitch Dowsett
- Ralph Lauren Centre for Breast Cancer Research, Royal Marsden Hospital, London, UK
| | - Andreas Makris
- Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK
| | - Vicky Goh
- Division of Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK
| |
Collapse
|
48
|
Erb-Eigner K, Asbach P, Ro SR, Haas M, Bertelmann E, Pietsch H, Schwenke C, Taupitz M, Denecke T, Hamm B, Lawaczeck R. DCE-MR imaging of orbital lesions: diagnostic performance of the tumor flow residence time τ calculated by a multi-compartmental pharmacokinetic tumor model based on individual factors. Acta Radiol 2019; 60:643-652. [PMID: 30114927 DOI: 10.1177/0284185118795324] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Differentiating benign from malignant orbital lesions by imaging and clinical presentation can be challenging. PURPOSE To differentiate benign from malignant orbital masses using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on tumor flow residence time τ calculated with the aid of a pharmacokinetic tumor model. MATERIAL AND METHODS Sixty patients with orbital masses were investigated by 3-T MRI including dynamic sequences. The signal intensity-time curve after i.v. contrast medium administration within lesions was approximated by Gd-concentration profiles on the basis of model calculations where the tumor is embedded in a whole-body kinetic model. One output of the model was tumor flow residence time τ, defined as the ratio of the tumor volume and the tumor blood flow rate. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance of τ. The results were compared with those of Ktrans, kep, ve, iAUC, and ADC. RESULTS Thirty-one benign and 29 malignant orbital masses were identified (reference standard: histopathology, clinical characteristics). Mean τ was significantly longer for benign masses (94 ± 48 s) than for malignant masses (21 ± 19 s, P < 0.001). ROC analysis revealed the highest area under the curve (AUC = 0.94) for τ in orbital masses compared to standard methods. CONCLUSION Tumor flow residence times τ of benign and malignant orbital masses are valuable in the diagnostic work-up of orbital tumors. Measures of diagnostic accuracy were superior for τ compared to ADC, Ktrans, ve, and iAUC.
Collapse
Affiliation(s)
| | - Patrick Asbach
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Sa-Ra Ro
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Matthias Haas
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Eckart Bertelmann
- Department of Ophthalmology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Hubertus Pietsch
- MR and CT Contrast Media Research, Bayer Pharma AG, Berlin, Germany
| | | | - Matthias Taupitz
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Timm Denecke
- Department of Radiology, Charité – Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- MR and CT Contrast Media Research, Bayer Pharma AG, Berlin, Germany
| | | |
Collapse
|
49
|
Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw Open 2019; 2:e192561. [PMID: 31002322 PMCID: PMC6481453 DOI: 10.1001/jamanetworkopen.2019.2561] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer. OBJECTIVE To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy. DESIGN, SETTING, AND PARTICIPANTS In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019. MAIN OUTCOMES AND MEASURES Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting. RESULTS In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002). CONCLUSIONS AND RELEVANCE A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.
Collapse
Affiliation(s)
- Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Salendra Singh
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Maryam Etesami
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gallagher
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - B. Nicolas Bloch
- Department of Radiology, Boston Medical Center, Boston, Massachusetts
- Department of Radiology, Boston University School of Medicine, Boston, Massachusetts
| | - Manasa Vulchi
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - Paulette Turk
- Department of Diagnostic Radiology, The Cleveland Clinic, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jame Abraham
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - William M. Sikov
- Program in Women’s Oncology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - George Somlo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
| | - Lyndsay N. Harris
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Hannah Gilmore
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Donna Plecha
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Vinay Varadan
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
| |
Collapse
|
50
|
Tian Y, Feng Y. Up-regulation of long noncoding RNA uc.338 predicts poor survival in non-small cell lung cancer. Cancer Biomark 2018; 22:781-785. [PMID: 29843223 DOI: 10.3233/cbm-181331] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Long noncoding RNA ultraconserved element 338 (uc.338) is a long non-coding RNA reported to function as a promoter in non-small cell lung cancer (NSCLC). However, the function and potential mechanism of uc.338 in NSCLC is still unclear. OBJECTIVE The aim of the present study was to assess the effect of uc.338 on the prognosis of patients with NSCLC. METHODS The expression levels of uc.338 in NSCLC tissues and matched normal lung tissues were examined by real-time quantitative PCR. Then the association between uc.338 levels with clinical variables as well as survival time was investigated. RESULTS We found that uc.338 expression levels were significantly upregulated in NSCLC compared with the matched noncancerous lung tissues (P< 0.01). In addition, increased uc.338 expression was significantly associated with TNM stage (P< 0.003), lymph node metastasis (P< 0.006) and distant metastasis (P< 0.002). More importantly, Kaplan-Meier survival analysis demonstrated that higher uc.338 expression levels were associated with a shorter overall survival (P< 0.0016) and disease-free survival (p< 0.0001) in NSCLC patients. Finally, univariate and multivariate Cox regression analyses revealed that uc.338 was an independent risk factor for overall survival and disease-free survival. CONCLUSIONS Our results show that uc.338 may play an important role in tumorigenesis and progression and could serve as a potential independent prognostic biomarker for patients with NSCLC.
Collapse
|