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Wu J, Li X, Teng X, Rubin DL, Napel S, Daniel BL, Li R. Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. Breast Cancer Res 2018; 20:101. [PMID: 30176944 PMCID: PMC6122724 DOI: 10.1186/s13058-018-1039-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 08/08/2018] [Indexed: 02/08/2023] Open
Abstract
Background We sought to investigate associations between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features and tumor-infiltrating lymphocytes (TILs) in breast cancer, as well as to study if MRI features are complementary to molecular markers of TILs. Methods In this retrospective study, we extracted 17 computational DCE-MRI features to characterize tumor and parenchyma in The Cancer Genome Atlas cohort (n = 126). The percentage of stromal TILs was evaluated on H&E-stained histological whole-tumor sections. We first evaluated associations between individual imaging features and TILs. Multiple-hypothesis testing was corrected by the Benjamini-Hochberg method using false discovery rate (FDR). Second, we implemented LASSO (least absolute shrinkage and selection operator) and linear regression nested with tenfold cross-validation to develop an imaging signature for TILs. Next, we built a composite prediction model for TILs by combining imaging signature with molecular features. Finally, we tested the prognostic significance of the TIL model in an independent cohort (I-SPY 1; n = 106). Results Four imaging features were significantly associated with TILs (P < 0.05 and FDR < 0.2), including tumor volume, cluster shade of signal enhancement ratio (SER), mean SER of tumor-surrounding background parenchymal enhancement (BPE), and proportion of BPE. Among molecular and clinicopathological factors, only cytolytic score was correlated with TILs (ρ = 0.51; 95% CI, 0.36–0.63; P = 1.6E-9). An imaging signature that linearly combines five features showed correlation with TILs (ρ = 0.40; 95% CI, 0.24–0.54; P = 4.2E-6). A composite model combining the imaging signature and cytolytic score improved correlation with TILs (ρ = 0.62; 95% CI, 0.50–0.72; P = 9.7E-15). The composite model successfully distinguished low vs high, intermediate vs high, and low vs intermediate TIL groups, with AUCs of 0.94, 0.76, and 0.79, respectively. During validation (I-SPY 1), the predicted TILs from the imaging signature separated patients into two groups with distinct recurrence-free survival (RFS), with log-rank P = 0.042 among triple-negative breast cancer (TNBC). The composite model further improved stratification of patients with distinct RFS (log-rank P = 0.0008), where TNBC with no/minimal TILs had a worse prognosis. Conclusions Specific MRI features of tumor and parenchyma are associated with TILs in breast cancer, and imaging may play an important role in the evaluation of TILs by providing key complementary information in equivocal cases or situations that are prone to sampling bias. Electronic supplementary material The online version of this article (10.1186/s13058-018-1039-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, 1070 Arastradero Road, Stanford, CA, 94305, USA.
| | - Xuejie Li
- Department of Pathology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Xiaodong Teng
- Department of Pathology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Bruce L Daniel
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, 1070 Arastradero Road, Stanford, CA, 94305, USA
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Net JM, Whitman GJ, Morris E, Brandt KR, Burnside ES, Giger ML, Ganott M, Sutton EJ, Zuley ML, Rao A. Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype. Curr Probl Diagn Radiol 2018; 48:467-472. [PMID: 30270031 DOI: 10.1067/j.cpradiol.2018.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 08/16/2018] [Indexed: 12/14/2022]
Abstract
PURPOSE The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project. MATERIALS AND METHODS Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers. RESULTS ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER- tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema (P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8). CONCLUSIONS Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.
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Affiliation(s)
- Jose M Net
- Department of Radiology, University of Miami, Miller School of Medicine, Miami, FL.
| | - Gary J Whitman
- Department of Diagnostic Imaging, University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Elizabteh Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Marie Ganott
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA
| | - Elizabeth J Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Arvind Rao
- Department of Bioinformatics and Computational Biology, University of Texas, MD Anderson Cancer Center, Houston, TX
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Lung Cancer Radiogenomics: The Increasing Value of Imaging in Personalized Management of Lung Cancer Patients. J Thorac Imaging 2018; 33:17-25. [PMID: 29252899 DOI: 10.1097/rti.0000000000000312] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Radiogenomics provide a large-scale data analytical framework that aims to understand the broad multiscale relationships between the complex information encoded in medical images (including computational, quantitative, and semantic image features) and their underlying clinical, therapeutic, and biological associations. As such it is a powerful and increasingly important tool for both clinicians and researchers involved in the imaging, evaluation, understanding, and management of lung cancers. Herein we provide an overview of the growing field of lung cancer radiogenomics and its applications.
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Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 2018; 287:732-747. [PMID: 29782246 DOI: 10.1148/radiol.2018172171] [Citation(s) in RCA: 168] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. © RSNA, 2018.
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Affiliation(s)
- Katja Pinker
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Joanne Chin
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Amy N Melsaether
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Elizabeth A Morris
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Linda Moy
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
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Jamshidi N, Yamamoto S, Gornbein J, Kuo MD. Receptor-based Surrogate Subtypes and Discrepancies with Breast Cancer Intrinsic Subtypes: Implications for Image Biomarker Development. Radiology 2018; 289:210-217. [PMID: 30040052 DOI: 10.1148/radiol.2018171118] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Purpose To determine the concordance and accuracy of imaging surrogates of immunohistochemical (IHC) markers and the molecular classification of breast cancer. Materials and Methods A total of 3050 patients from 17 public breast cancer data sets containing IHC marker receptor status (estrogen receptor/progesterone receptor/human epidermal growth factor receptor 2 [HER2]) and their molecular classification (basal-like, HER2-enriched, luminal A or B) were analyzed. Diagnostic accuracy and concordance as measured with the κ statistic were calculated between the IHC and molecular classifications. Simulations were performed to assess the relationship between accuracy of imaging-based IHC markers to predict molecular classification. A simulation was performed to examine effects of misclassification of molecular type on patient survival. Results Accuracies of intrinsic subtypes based on IHC subtype were 71.7% (luminal A), 53.7% (luminal B), 64.8% (HER2-enriched), and 81.7% (basal-like). The κ agreement was fair (κ = 0.36) for luminal A and HER2-enriched subtypes, good (κ = 0.65) for the basal-like subtype, and poor (κ = 0.09) for the luminal B subtypes. Introduction of image misclassification by simulation lowered image-true subtype accuracies and κ values. Simulation analysis showed that misclassification caused survival differences between luminal A and basal-like subtypes to decrease. Conclusion There is poor concordance between triple-receptor status and intrinsic molecular subtype in breast cancer, arguing against their use in the design of prognostic genomic-based image biomarkers. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Neema Jamshidi
- From the Department of Radiology, University of California-Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, Calif (N.J., S.Y.); College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu Taiwan (S.Y.); Department of Biomathematics, University of California-Los Angeles, Los Angeles, Calif (J.G.); and Department of Diagnostic Radiology, University of Hong Kong, Room 406, Block K, Queen Mary Hospital, 102 Pok Fu Lam Rd, Hong Kong (M.D.K.)
| | - Shota Yamamoto
- From the Department of Radiology, University of California-Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, Calif (N.J., S.Y.); College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu Taiwan (S.Y.); Department of Biomathematics, University of California-Los Angeles, Los Angeles, Calif (J.G.); and Department of Diagnostic Radiology, University of Hong Kong, Room 406, Block K, Queen Mary Hospital, 102 Pok Fu Lam Rd, Hong Kong (M.D.K.)
| | - Jeffrey Gornbein
- From the Department of Radiology, University of California-Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, Calif (N.J., S.Y.); College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu Taiwan (S.Y.); Department of Biomathematics, University of California-Los Angeles, Los Angeles, Calif (J.G.); and Department of Diagnostic Radiology, University of Hong Kong, Room 406, Block K, Queen Mary Hospital, 102 Pok Fu Lam Rd, Hong Kong (M.D.K.)
| | - Michael D Kuo
- From the Department of Radiology, University of California-Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, Calif (N.J., S.Y.); College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu Taiwan (S.Y.); Department of Biomathematics, University of California-Los Angeles, Los Angeles, Calif (J.G.); and Department of Diagnostic Radiology, University of Hong Kong, Room 406, Block K, Queen Mary Hospital, 102 Pok Fu Lam Rd, Hong Kong (M.D.K.)
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Shin SU, Cho N, Lee HB, Kim SY, Yi A, Kim SY, Lee SH, Chang JM, Moon WK. Neoadjuvant Chemotherapy and Surgery for Breast Cancer: Preoperative MRI Features Associated with Local Recurrence. Radiology 2018; 289:30-38. [PMID: 30040058 DOI: 10.1148/radiol.2018172888] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Purpose To investigate the MRI and clinical-pathologic features associated with local-regional recurrence (LRR) in patients who had undergone breast-conserving surgery (BCS) following neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective, single-institution study between October 2003 and September 2015, 548 consecutive women, consisting of 468 down-staged and 80 preplanned BCS patients (mean age, 45.7 years; range, 22-75 years), underwent preoperative MRI and BCS following NAC. The rate and site of LRR, preoperative MRI features including Breast Imaging Reporting and Data System lexicon, and clinical-pathologic features (age, stage, tumor subtype, histologic grade, lymphovascular invasion, adjuvant chemotherapy, and endocrine therapy) were analyzed with the Cox proportional hazards model to identify independent factors associated with LRR-free survival (LRFS). Results Of the 548 women, 23 (4.2%) had LRR at a median follow-up of 23.1 months. In Cox regression analysis, younger age (ie, ≤ 40 years) (hazard ratio = 2.932; 95% confidence interval [CI]: 1.233, 6.969; P = .015) or the presence of nonmass enhancement on preoperative MR images (hazard ratio = 3.220; 95% CI: 1.274, 8.140; P = .014) was associated with worse LRFS. LRR was more frequently observed in the same quadrant as the original tumor in the down-staged BCS group than in the preplanned BCS group (80.0% [16 of 20] vs 33.3% [one of three]; P = .021). Conclusion Age of 40 years or younger or the presence of nonmass enhancement on preoperative MR images tends to be associated with worse local-regional recurrence-free survival, and local-regional recurrence frequently occurs in the same quadrant as the original tumor in breast cancer patients who undergo breast-conserving surgery following neoadjuvant chemotherapy. © RSNA, 2018.
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Affiliation(s)
| | | | | | - Soo-Yeon Kim
- From the Departments of Radiology (S.U.S., N.C., S.Y.K. [1], S.Y.K. [2], S.H.L., J.M.C., W.K.M.) and Surgery (H.B.L.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.U.S., N.C., S.Y.K. [1], S.Y.K. [2], S.H.L., J.M.C., W.K.M.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.U.S., N.C., S.Y.K. [1], S.Y.K. [2], S.H.L., J.M.C., W.K.M.); and Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (S.U.S., A.Y.)
| | | | - Soo-Yeon Kim
- From the Departments of Radiology (S.U.S., N.C., S.Y.K. [1], S.Y.K. [2], S.H.L., J.M.C., W.K.M.) and Surgery (H.B.L.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.U.S., N.C., S.Y.K. [1], S.Y.K. [2], S.H.L., J.M.C., W.K.M.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.U.S., N.C., S.Y.K. [1], S.Y.K. [2], S.H.L., J.M.C., W.K.M.); and Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (S.U.S., A.Y.)
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Liu Y, Zhang Y, Cheng R, Liu S, Qu F, Yin X, Wang Q, Xiao B, Ye Z. Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation. J Magn Reson Imaging 2018; 49:280-290. [PMID: 29761595 DOI: 10.1002/jmri.26192] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 04/26/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The role of apparent diffusion coefficient (ADC)-based radiomics features in evaluating histopathological grade of cervical cancer is unresolved. PURPOSE To determine if there is a difference between radiomics features derived from center-slice 2D versus whole-tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications. STUDY TYPE Prospective. SUBJECTS In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix. FIELD STRENGTH/SEQUENCE Conventional and diffusion-weighted MR images (b values = 0, 800, 1000 s/mm2 ) were acquired on a 3.0T MR scanner. ASSESSMENT Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole-tumor segmentation. A total of 624 radiomics features were derived from T2 -weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis. STATISTICAL TESTS Parameters were compared using Wilcoxon signed rank test, Bland-Altman analysis, t-test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation. RESULTS In all, 95 radiomics features were insensitive to ROI variation among T2 images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center-slice and 3D whole-tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076). DATA CONCLUSION Several radiomics features extracted from T2 images and ADC maps were highly reproducible. Whole-tumor volumetric 3D radiomics analysis had a better performance than using the 2D center-slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm2 is suggested as the optimal parameter in pelvic DWI scans. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:280-290.
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Affiliation(s)
- Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Runfen Cheng
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Shichang Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Fangyuan Qu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xiaoyu Yin
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Qin Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Bohan Xiao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Jansen RW, van Amstel P, Martens RM, Kooi IE, Wesseling P, de Langen AJ, Menke-Van der Houven van Oordt CW, Jansen BHE, Moll AC, Dorsman JC, Castelijns JA, de Graaf P, de Jong MC. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 2018; 9:20134-20155. [PMID: 29732009 PMCID: PMC5929452 DOI: 10.18632/oncotarget.24893] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 02/26/2018] [Indexed: 12/12/2022] Open
Abstract
With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.
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Affiliation(s)
- Robin W Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Paul van Amstel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Irsan E Kooi
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Utrecht, The Netherlands
| | - Adrianus J de Langen
- Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Bernard H E Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Annette C Moll
- Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands
| | - Josephine C Dorsman
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcus C de Jong
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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Bae MS, Chang JM, Cho N, Han W, Ryu HS, Moon WK. Association of preoperative breast MRI features with locoregional recurrence after breast conservation therapy. Acta Radiol 2018; 59:409-417. [PMID: 28747131 DOI: 10.1177/0284185117723041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background Locoregional recurrence (LRR) following breast conservation therapy (BCT) is associated with an increased risk of distant metastasis and death in patients with breast cancer. Purpose To investigate whether preoperative breast magnetic resonance imaging (MRI) features are associated with the risk of LRR in patients undergoing BCT. Material and Methods A total of 3781 women with primary invasive breast cancer underwent preoperative MRI and BCT between 2003 and 2013. Forty-eight patients who developed LRR comprised the LRR cohort and one-to-one matching (age, tumor stage, grade, and axillary nodal status) of each patient to a control participant was performed in patients who did not develop recurrence. Three readers independently reviewed MR images of the index cancer and the presence of multifocal disease was assessed. Χ2 analysis was used to compare imaging and clinical features between LRR and control cohorts, with multivariate logistic regression analysis used to identify independent features. Results Significant differences were found in the proportion of multifocal disease ( P = 0.001), background parenchymal enhancement level ( P = 0.007), and breast cancer molecular subtype ( P = 0.01) between LRR and control cohorts. Multivariate analysis showed that multifocal disease (odds ratio [OR] = 11.9; 95% confidence interval [CI] = 1.4-102.5; P = 0.02) and human epidermal growth factor receptor 2-positive subtype (OR = 12.7; 95% CI = 1.3-127.6; P = 0.03) were both independently associated with LRR. Conclusion Multifocal disease on preoperative breast MRI may indicate an increased risk of LRR in patients treated with BCT.
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Affiliation(s)
- Min Sun Bae
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Wonshik Han
- Department of Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Han Suk Ryu
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
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60
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Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J 2018; 9:77-102. [PMID: 29515689 PMCID: PMC5833337 DOI: 10.1007/s13167-018-0128-8] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/29/2018] [Indexed: 02/06/2023]
Abstract
Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.
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Affiliation(s)
- Miaolong Lu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- The State Key Laboratory of Medical Genetics, Central South University, 88 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
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61
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Bickelhaupt S, Jaeger PF, Laun FB, Lederer W, Daniel H, Kuder TA, Wuesthof L, Paech D, Bonekamp D, Radbruch A, Delorme S, Schlemmer HP, Steudle FH, Maier-Hein KH. Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer. Radiology 2018; 287:761-770. [PMID: 29461172 DOI: 10.1148/radiol.2017170273] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0-1500 sec/mm2) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material-enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001). Conclusion A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Sebastian Bickelhaupt
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Paul Ferdinand Jaeger
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Frederik Bernd Laun
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Wolfgang Lederer
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Heidi Daniel
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Tristan Anselm Kuder
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Lorenz Wuesthof
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Daniel Paech
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - David Bonekamp
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Alexander Radbruch
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Stefan Delorme
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Heinz-Peter Schlemmer
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Franziska Hildegard Steudle
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
| | - Klaus Hermann Maier-Hein
- From the Department of Radiology (S.B., L.W., D.P., D.B., A.R., S.D., H.P.S., F.S.), Division of Medical Image Computing (P.F.J., K.H.M.H.), and Department of Medical Physics in Radiology (F.B.L., T.A.K.), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany (F.B.L.); Radiological Practice at the ATOS Clinic Heidelberg, Heidelberg, Germany (W.L.); and Radiology Center Mannheim, Mannheim, Germany (H.D.)
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Wang J, Ye C, Xiong H, Shen Y, Lu Y, Zhou J, Wang L. Dysregulation of long non-coding RNA in breast cancer: an overview of mechanism and clinical implication. Oncotarget 2018; 8:5508-5522. [PMID: 27732939 PMCID: PMC5354927 DOI: 10.18632/oncotarget.12537] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 10/03/2016] [Indexed: 01/16/2023] Open
Abstract
Long non-coding RNAs (lncRNAs), which occupy nearly 98% of genome, have crucial roles in cancer development, including breast cancer. Breast cancer is a disease with high incidence. Despite of recent progress in understanding the molecular mechanisms and combined therapy strategies, the functions and mechanisms of lncRNAs in breast cancer remains unclear. This review presents the currently basic knowledge and research approaches of lncRNAs. We also highlight the latest advances of seven classic lncRNAs and three novel lncRNAs in breast cancer, elucidating their mechanisms and possible therapeutic targets. Additionally, association between lncRNA and specific molecular subtype of breast cancer is reported. Lastly, we briefly delineate the potential roles of lncRNAs in clinical applications as biomarkers and treatment targets.
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Affiliation(s)
- Ji Wang
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.,Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chenyang Ye
- Cancer Institute (Key Laboratory of Cancer Prevention & Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hanchu Xiong
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.,Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yong Shen
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Lu
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.,Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jichun Zhou
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.,Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Linbo Wang
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.,Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, Zhejiang, China
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63
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Xia W, Chen Y, Zhang R, Yan Z, Zhou X, Zhang B, Gao X. Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data-a preliminary study. Phys Med Biol 2018; 63:035044. [PMID: 29311419 DOI: 10.1088/1361-6560/aaa609] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Our objective was to identify prognostic imaging biomarkers for hepatocellular carcinoma in contrast-enhanced computed tomography (CECT) with biological interpretations by associating imaging features and gene modules. We retrospectively analyzed 371 patients who had gene expression profiles. For the 38 patients with CECT imaging data, automatic intra-tumor partitioning was performed, resulting in three spatially distinct subregions. We extracted a total of 37 quantitative imaging features describing intensity, geometry, and texture from each subregion. Imaging features were selected after robustness and redundancy analysis. Gene modules acquired from clustering were chosen for their prognostic significance. By constructing an association map between imaging features and gene modules with Spearman rank correlations, the imaging features that significantly correlated with gene modules were obtained. These features were evaluated with Cox's proportional hazard models and Kaplan-Meier estimates to determine their prognostic capabilities for overall survival (OS). Eight imaging features were significantly correlated with prognostic gene modules, and two of them were associated with OS. Among these, the geometry feature volume fraction of the subregion, which was significantly correlated with all prognostic gene modules representing cancer-related interpretation, was predictive of OS (Cox p = 0.022, hazard ratio = 0.24). The texture feature cluster prominence in the subregion, which was correlated with the prognostic gene module representing lipid metabolism and complement activation, also had the ability to predict OS (Cox p = 0.021, hazard ratio = 0.17). Imaging features depicting the volume fraction and textural heterogeneity in subregions have the potential to be predictors of OS with interpretable biological meaning.
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Affiliation(s)
- Wei Xia
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Rd, Suzhou 215163, People's Republic of China
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64
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Acharya UR, Hagiwara Y, Sudarshan VK, Chan WY, Ng KH. Towards precision medicine: from quantitative imaging to radiomics. J Zhejiang Univ Sci B 2018; 19:6-24. [PMID: 29308604 PMCID: PMC5802973 DOI: 10.1631/jzus.b1700260] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/16/2017] [Indexed: 12/12/2022]
Abstract
Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.
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Affiliation(s)
- U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Vidya K. Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
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65
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Lehrer M, Bhadra A, Aithala S, Ravikumar V, Zheng Y, Dogan B, Bonaccio E, Burnside ES, Morris E, Sutton E, Whitman GJ, Net J, Brandt K, Ganott M, Zuley M, Rao A. High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma. Oncoscience 2018; 5:39-48. [PMID: 29556516 PMCID: PMC5854291 DOI: 10.18632/oncoscience.397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 12/15/2017] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Imaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor. METHODS Multiple-response regression of the image-derived, radiologist-scored features with reverse-phase protein array expression levels generated association coefficients for each combination of image-feature and protein in the RPPA dataset. Significantly-associated proteins for features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis determined which features were most strongly correlated with pathway activity and cellular functions. RESULTS Each of the twenty-nine imaging features was found to have a set of significantly correlated molecules, associated biological functions, and pathways. CONCLUSIONS We interrogated the pathway alterations represented by the protein expression associated with each imaging feature. Our study demonstrates the relationships between biological processes (via proteomic measurements) and MRI features within breast tumors.
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Affiliation(s)
- Michael Lehrer
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anindya Bhadra
- Department of Statistics, Purdue University, West Lafayette, IN, USA
| | - Sathvik Aithala
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Visweswaran Ravikumar
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Youyun Zheng
- Department of Biostatistics, Emory University, Atlanta, GA, USA
| | - Basak Dogan
- Department of Radiology, UT Southwestern, Dallas, TX, USA
| | - Emerlinda Bonaccio
- Department of Diagnostic Radiology, Roswell Park Cancer Institute, Buffalo, NY, USA
| | | | - Elizabeth Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gary J. Whitman
- Department of Radiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jose Net
- Department of Radiology, University of Miami Health System, Miami, FL, USA
| | - Kathy Brandt
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Marie Ganott
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Margarita Zuley
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Arvind Rao
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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66
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Zhang Z, Yang J, Ho A, Jiang W, Logan J, Wang X, Brown PD, McGovern SL, Guha-Thakurta N, Ferguson SD, Fave X, Zhang L, Mackin D, Court LE, Li J. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 2017; 28:2255-2263. [PMID: 29178031 DOI: 10.1007/s00330-017-5154-8] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 10/06/2017] [Accepted: 10/23/2017] [Indexed: 01/23/2023]
Abstract
OBJECTIVES To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery. METHODS We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions) and calculated 285 radiomic features from four MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at two follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions. RESULTS A combination of five radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation. CONCLUSIONS Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases. KEY POINTS • Some radiomic features showed better reproducibility for progressive lesions than necrotic ones • Delta radiomic features can help to distinguish radiation necrosis from tumour progression • Delta radiomic features had better predictive value than did traditional radiomic features.
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Affiliation(s)
- Zijian Zhang
- Central South University Xiangya Hospital, Changsha, Hunan, China.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
| | - Angela Ho
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.,University of Houston, Houston, TX, USA
| | - Wen Jiang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jennifer Logan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Xin Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Paul D Brown
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Susan L McGovern
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Nandita Guha-Thakurta
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Sherise D Ferguson
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Xenia Fave
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Dennis Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jing Li
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA
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Abstract
OBJECTIVE The goals of this review are to provide background information on the definitions and applications of the general term "biomarker" and to highlight the specific roles of breast imaging biomarkers in research and clinical breast cancer care. A search was conducted of the main electronic biomedical databases (PubMed, Cochrane, Embase, MEDLINE [Ovid], Scopus, and Web of Science). The search was focused on review literature in general radiology and biomedical sciences and on reviews and primary research articles on biomarkers in breast imaging over the 15 years ending in June 2017. The keywords included "biomarker," "trial endpoints," "breast imaging," "breast cancer," "radiomics," and "precision medicine" in the titles and abstracts of the papers. CONCLUSION Clinical breast care and breast cancer-related research rely on imaging biomarkers for decision support. In the era of precision medicine and big data, the practice of radiology is likely to change. A closer integration of breast imaging with related biomedical fields and the creation of large integrated and shareable databases of clinical, molecular, and imaging biomarkers should allow the field to continue guiding breast cancer care and research.
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Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, Hricak H, Sutton EJ, Morris EA. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2017; 47:604-620. [PMID: 29095543 DOI: 10.1002/jmri.25870] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 09/17/2017] [Accepted: 09/19/2017] [Indexed: 12/17/2022] Open
Abstract
With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors. LEVEL OF EVIDENCE 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.
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Affiliation(s)
- Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
| | - Fuki Shitano
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Evis Sala
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Richard K Do
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Young
- Department of Radiology, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Andreas G Wibmer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Elizabeth J Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Shin SU, Lee J, Kim JH, Kim WH, Song SE, Chu A, Kim HS, Han W, Ryu HS, Moon WK. Gene expression profiling of calcifications in breast cancer. Sci Rep 2017; 7:11427. [PMID: 28900139 PMCID: PMC5595962 DOI: 10.1038/s41598-017-11331-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 08/22/2017] [Indexed: 12/21/2022] Open
Abstract
We investigated the gene expression profiles of calcifications in breast cancer. Gene expression analysis of surgical specimen was performed using Affymetrix GeneChip® Human Gene 2.0 ST arrays in 168 breast cancer patients. The mammographic calcifications were reviewed by three radiologists and classified into three groups according to malignancy probability: breast cancers without suspicious calcifications; breast cancers with low-to-intermediate suspicious calcifications; and breast cancers with highly suspicious calcifications. To identify differentially expressed genes (DEGs) between these three groups, a one-way analysis of variance was performed with post hoc comparisons with Tukey's honest significant difference test. To explore the biological significance of DEGs, we used DAVID for gene ontology analysis and BioLattice for clustering analysis. A total of 2551 genes showed differential expression among the three groups. ERBB2 genes are up-regulated in breast cancers with highly suspicious calcifications (fold change 2.474, p < 0.001). Gene ontology analysis revealed that the immune, defense and inflammatory responses were decreased in breast cancers with highly suspicious calcifications compared to breast cancers without suspicious calcifications (p from 10-23 to 10-8). The clustering analysis also demonstrated that the immune system is associated with mammographic calcifications (p < 0.001). Our study showed calcifications in breast cancers are associated with high levels of mRNA expression of ERBB2 and decreased immune system activity.
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Affiliation(s)
- Sung Ui Shin
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Jeonghoon Lee
- Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea
| | - Ju Han Kim
- Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea
| | - Won Hwa Kim
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Sung Eun Song
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Ajung Chu
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Hoe Suk Kim
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Wonshik Han
- Department of Surgery, Seoul National University Hospital, Seoul, Korea
| | - Han Suk Ryu
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea.
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Zhou M, Leung A, Echegaray S, Gentles A, Shrager JB, Jensen KC, Berry GJ, Plevritis SK, Rubin DL, Napel S, Gevaert O. Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology 2017; 286:307-315. [PMID: 28727543 PMCID: PMC5749594 DOI: 10.1148/radiol.2017161845] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Materials and Methods A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. Conclusion Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article.
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Affiliation(s)
- Mu Zhou
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Ann Leung
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Sebastian Echegaray
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Andrew Gentles
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Joseph B Shrager
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Kristin C Jensen
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Gerald J Berry
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Sylvia K Plevritis
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Daniel L Rubin
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Sandy Napel
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Olivier Gevaert
- From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
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Wu J, Li B, Sun X, Cao G, Rubin DL, Napel S, Ikeda DM, Kurian AW, Li R. Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer. Radiology 2017; 285:401-413. [PMID: 28708462 DOI: 10.1148/radiol.2017162823] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R2 = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Jia Wu
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Bailiang Li
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Xiaoli Sun
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Guohong Cao
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Daniel L Rubin
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Sandy Napel
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Debra M Ikeda
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Allison W Kurian
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
| | - Ruijiang Li
- From the Department of Radiation Oncology (J.W., B.L., R.L.), Department of Radiology (D.L.R., S.N., D.M.I.), Department of Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Department of Medicine (A.W.K.), Department of Health Research and Policy (A.W.K.), and Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Stanford, CA 94305; Department of Radiotherapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.); and Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.)
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Wu J, Cui Y, Sun X, Cao G, Li B, Ikeda DM, Kurian AW, Li R. Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways. Clin Cancer Res 2017; 23:3334-3342. [PMID: 28073839 PMCID: PMC5496801 DOI: 10.1158/1078-0432.ccr-16-2415] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 12/29/2016] [Accepted: 01/03/2017] [Indexed: 01/28/2023]
Abstract
Purpose: To identify novel breast cancer subtypes by extracting quantitative imaging phenotypes of the tumor and surrounding parenchyma and to elucidate the underlying biologic underpinnings and evaluate the prognostic capacity for predicting recurrence-free survival (RFS).Experimental Design: We retrospectively analyzed dynamic contrast-enhanced MRI data of patients from a single-center discovery cohort (n = 60) and an independent multicenter validation cohort (n = 96). Quantitative image features were extracted to characterize tumor morphology, intratumor heterogeneity of contrast agent wash-in/wash-out patterns, and tumor-surrounding parenchyma enhancement. On the basis of these image features, we used unsupervised consensus clustering to identify robust imaging subtypes and evaluated their clinical and biologic relevance. We built a gene expression-based classifier of imaging subtypes and tested their prognostic significance in five additional cohorts with publically available gene expression data but without imaging data (n = 1,160).Results: Three distinct imaging subtypes, that is, homogeneous intratumoral enhancing, minimal parenchymal enhancing, and prominent parenchymal enhancing, were identified and validated. In the discovery cohort, imaging subtypes stratified patients with significantly different 5-year RFS rates of 79.6%, 65.2%, 52.5% (log-rank P = 0.025) and remained as an independent predictor after adjusting for clinicopathologic factors (HR, 2.79; P = 0.016). The prognostic value of imaging subtypes was further validated in five independent gene expression cohorts, with average 5-year RFS rates of 88.1%, 74.0%, 59.5% (log-rank P from <0.0001 to 0.008). Each imaging subtype was associated with specific dysregulated molecular pathways that can be therapeutically targeted.Conclusions: Imaging subtypes provide complimentary value to established histopathologic or molecular subtypes and may help stratify patients with breast cancer. Clin Cancer Res; 23(13); 3334-42. ©2017 AACR.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Yi Cui
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
- Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan
| | - Xiaoli Sun
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
- Radiotherapy Department, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China
| | - Guohong Cao
- Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China
| | - Bailiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Debra M Ikeda
- Department of Radiology, Stanford University School of Medicine, Advanced Medicine Center, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Allison W Kurian
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
- Department of Medicine, Stanford University School of Medicine, Stanford, California
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
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Kim GR, Ku YJ, Cho SG, Kim SJ, Min BS. Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon. Ann Surg Treat Res 2017; 93:18-26. [PMID: 28706887 PMCID: PMC5507787 DOI: 10.4174/astr.2017.93.1.18] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 02/10/2017] [Accepted: 02/13/2017] [Indexed: 11/30/2022] Open
Abstract
Purpose To evaluate whether the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon could reflect the genomic information of breast cancers and to suggest intuitive imaging features as biomarkers. Methods Matched breast MRI data from The Cancer Imaging Archive and gene expression profile from The Cancer Genome Atlas of 70 invasive breast cancers were analyzed. Magnetic resonance images were reviewed according to the BI-RADS MRI lexicon of mass morphology. The cancers were divided into 2 groups of gene clustering by gene set enrichment an alysis. Clinicopathologic and imaging characteristics were compared between the 2 groups. Results The luminal subtype was predominant in the group 1 gene set and the triple-negative subtype was predominant in the group 2 gene set (55 of 56, 98.2% vs. 9 of 14, 64.3%). Internal enhancement descriptors were different between the 2 groups; heterogeneity was most frequent in group 1 (27 of 56, 48.2%) and rim enhancement was dominant in group 2 (10 of 14, 71.4%). In group 1, the gene sets related to mammary gland development were overexpressed whereas the gene sets related to mitotic cell division were overexpressed in group 2. Conclusion We identified intuitive imaging features of breast MRI associated with distinct gene expression profiles using the standard imaging variables of BI-RADS. The internal enhancement pattern on MRI might reflect specific gene expression profiles of breast cancers, which can be recognized by visual distinction.
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Affiliation(s)
- Ga Ram Kim
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - You Jin Ku
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - Soon Gu Cho
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - Sei Joong Kim
- Department of Surgery, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - Byung Soh Min
- Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Trout AT, Batie MR, Gupta A, Sheridan RM, Tiao GM, Towbin AJ. 3D printed pathological sectioning boxes to facilitate radiological–pathological correlation in hepatectomy cases. J Clin Pathol 2017; 70:984-987. [DOI: 10.1136/jclinpath-2016-204293] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 03/17/2017] [Accepted: 04/15/2017] [Indexed: 12/14/2022]
Abstract
Radiogenomics promises to identify tumour imaging features indicative of genomic or proteomic aberrations that can be therapeutically targeted allowing precision personalised therapy. An accurate radiological–pathological correlation is critical to the process of radiogenomic characterisation of tumours. An accurate correlation, however, is difficult to achieve with current pathological sectioning techniques which result in sectioning in non-standard planes. The purpose of this work is to present a technique to standardise hepatic sectioning to facilitateradiological–pathological correlation. We describe a process in which three-dimensional (3D)-printed specimen boxes based on preoperative cross-sectional imaging (CT and MRI) can be used to facilitate pathological sectioning in standard planes immediately on hepatic resection enabling improved tumour mapping. We have applied this process in 13 patients undergoing hepatectomy and have observed close correlation between imaging and gross pathology in patients with both unifocal and multifocal tumours.
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Kim Y, Furlan A, Borhani AA, Bae KT. Computer-aided diagnosis program for classifying the risk of hepatocellular carcinoma on MR images following liver imaging reporting and data system (LI-RADS). J Magn Reson Imaging 2017; 47:710-722. [PMID: 28556283 DOI: 10.1002/jmri.25772] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 05/08/2017] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To develop and evaluate a computer-aided diagnosis (CAD) program for liver lesions on magnetic resonance (MR) images for classification of the risk of hepatocellular carcinoma (HCC) following the liver imaging reporting and data system (LI-RADS). MATERIALS AND METHODS Liver MR images from 41 patients with hyperenhancing liver lesions categorized as LR 3, 4, and 5 were evaluated by two radiologists. The major LI-RADS features of each index liver lesion were recorded, including size (maximum transverse diameter), presence of hyperenhancement, washout appearance, and capsule appearance. A CAD program was implemented to register MR images at different contrast-enhancement phases, segment liver lesions, extract lesion features, and classify lesions according to LI-RADS. The LI-RADS features quantified by CAD were compared with those assessed by radiologists using the intraclass correlation coefficient (ICC) and receiver operator curve (ROC) analyses. The LI-RADS categorization between CAD and radiologists was evaluated using the weighted Cohen's kappa coefficient. RESULTS The mean and standard deviation of the lesion diameters were 21 ± 11 mm (range, 7-70 mm) by radiologists and 22 ± 11 mm (range, 8-72 mm) by CAD (ICC, 0.96-0.97). The area under the curve (AUC) for the washout assessment by CAD was 0.79-0.93 with sensitivity 0.69-0.82 and specificity 0.79-1. The AUC for the capsule assessment by CAD was 0.79-0.9 with sensitivity 0.75-0.9 and specificity 0.82-0.96. The classifications by the radiologists and CAD coincided in 76-83% lesions (k = 0.57-0.71), while the agreements between radiologists were in 78% lesions (k = 0.59). CONCLUSION We developed a CAD program for liver lesions on MR images and showed a substantial agreement in the LI-RADS-based classification of the risk of HCCs between the CAD and radiologists. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:710-722.
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Affiliation(s)
- Youngwoo Kim
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Alessandro Furlan
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Amir A Borhani
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Kyongtae T Bae
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
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Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data. J Neurooncol 2017; 133:27-35. [DOI: 10.1007/s11060-017-2420-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 04/09/2017] [Indexed: 01/15/2023]
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Cox VL, Bhosale P, Varadhachary GR, Wagner-Bartak N, Glitza IC, Gold KA, Atkins JT, Soliman PT, Hong DS, Qayyum A. Cancer Genomics and Important Oncologic Mutations: A Contemporary Guide for Body Imagers. Radiology 2017; 283:314-340. [DOI: 10.1148/radiol.2017152224] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Veronica L. Cox
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Priya Bhosale
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Gauri R. Varadhachary
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Nicolaus Wagner-Bartak
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Isabella C. Glitza
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Kathryn A. Gold
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Johnique T. Atkins
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Pamela T. Soliman
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - David S. Hong
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
| | - Aliya Qayyum
- From the Department of Radiology, Abdominal Imaging Section (V.L.C., P.B., N.W.B., A.Q.), Department of Gastrointestinal Medical Oncology (G.R.V.), Department of Melanoma Medical Oncology (I.C.G.), Department of Thoracic and Head & Neck Medical Oncology (K.A.G.), Department of Gynecologic Oncology (P.T.S.), Department of Investigational Cancer Therapeutics (J.T.A., D.S.H.), University of Texas MD
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Jamshidi N, Margolis DJ, Raman S, Huang J, Reiter RE, Kuo MD. Multiregional Radiogenomic Assessment of Prostate Microenvironments with Multiparametric MR Imaging and DNA Whole-Exome Sequencing of Prostate Glands with Adenocarcinoma. Radiology 2017; 284:109-119. [PMID: 28453432 DOI: 10.1148/radiol.2017162827] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Purpose To assess the underlying genomic variation of prostate gland microenvironments of patients with prostate adenocarcinoma in the context of colocalized multiparametric magnetic resonance (MR) imaging and histopathologic assessment of normal and abnormal regions by using whole-exome sequencing. Materials and Methods Six patients with prostate adenocarcinoma who underwent robotic prostatectomy with whole-mount preservation of the prostate were identified, which enabled spatial mapping between preoperative multiparametric MR imaging and the gland. Four regions of interest were identified within each gland, including regions found to be normal and abnormal via histopathologic analysis. Whole-exome DNA sequencing (>50 times coverage) was performed on each of these spatially targeted regions. Radiogenomic analysis of imaging and mutation data were performed with hierarchical clustering, phylogenetic analysis, and principal component analysis. Results Radiogenomic multiparametric MR imaging and whole-exome spatial characterization in six patients with prostate adenocarcinoma (three patients, Gleason score of 3 + 4; and three patients, Gleason score of 4 + 5) was performed across 23 spatially distinct regions. Hierarchical clustering separated histopathologic analysis-proven high-grade lesions from the normal regions, and this reflected concordance between multiparametric MR imaging and resultant histopathologic analysis in all patients. Seventy-seven mutations involving 29 cancer-associated genes across the 23 spatially distinct prostate samples were identified. There was no significant difference in mutation load in cancer-associated genes between regions that were proven to be normal via histopathologic analysis (34 mutations per sample ± 19), mildly suspicious via multiparametric MR imaging (37 mutations per sample ± 21), intermediately suspicious via multiparametric MR imaging (31 mutations per sample ± 15), and high-grade cancer (33 mutations per sample ± 18) (P = .30). Principal component analysis resolved samples from different patients and further classified samples (regardless of histopathologic status) from prostate glands with Gleason score 3 + 4 versus 4 + 5 samples. Conclusion Multiregion spatial multiparametric MR imaging and whole-exome radiogenomic analysis of prostate glands with adenocarcinoma shows a continuum of mutations across regions that were found via histologic analysis to be high grade and normal. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Neema Jamshidi
- From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.)
| | - Daniel J Margolis
- From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.)
| | - Steven Raman
- From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.)
| | - Jiaoti Huang
- From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.)
| | - Robert E Reiter
- From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.)
| | - Michael D Kuo
- From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.)
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Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, Monti S, Salvatore M. Radiogenomic Analysis of Oncological Data: A Technical Survey. Int J Mol Sci 2017; 18:ijms18040805. [PMID: 28417933 PMCID: PMC5412389 DOI: 10.3390/ijms18040805] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 04/06/2017] [Accepted: 04/08/2017] [Indexed: 12/18/2022] Open
Abstract
In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.
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Affiliation(s)
| | - Marco Aiello
- IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy.
| | | | | | | | | | - Serena Monti
- IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy.
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lncRNA MEG3 had anti-cancer effects to suppress pancreatic cancer activity. Biomed Pharmacother 2017; 89:1269-1276. [PMID: 28320094 DOI: 10.1016/j.biopha.2017.02.041] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 02/12/2017] [Accepted: 02/13/2017] [Indexed: 01/30/2023] Open
Abstract
AIM The aim of this study was to explain the mechanism of lncRNA MEG 3 in pancreatic cancer. METHODS We were collecting 30 pancreatic cancer patients, taking the sample from these patients. We measured the PI3K protein expressions from 30 patients by IHC and WB methods and MEG 3 expression by RT-PCR, and analyzed the relationship between PI3K protein expression and pancreatic cancer patients' clinical pathology and the correlation between lncRNA MEG 3 and PI3K. In the cell experiment, PANC-1 cells were divided into three groups: NC, BL and lncRNA groups, after treatment,we measured cell proliferation rate of 3 groups by MTT methods, evaluated cell apoptosis and cell cycle using flow cytometry, tested the invasion cells and migrate rate of 3 groups by transwell and wound healing assays. RESULTS Compared with carcinoma adjacent tissue, The PI3K protein expression of pancreatic cancer tissue were significantly up-regulation (P>0.05). MEG 3 gene expression was negatively correlated with PI3K expression. The MEG 3 was negatively correlated with tumor size, Metastasis and Vascular invasion in pancreatic cancer (P<0.05, respectively). In the cell experiment, The cell proliferation and apoptosis rates of lncRNA group were significantly difference compared with NC group (P<0.05, respectively), and the G1 phase rate of lncRNA group was higher than NC group (P<0.05). The invasion cells and wound healing rate were significantly reduced in lncRNA group than those in NC group (P<0.05, respectively). CONCLUSION MEG 3 over-expressing had anti-cancer effects to suppress pancreatic cancer activity by regulation PI3K/AKT/Bcl-2/Bax/Cyclin D1/P53 and PI3K/AKT/MMP-2/MMP-9 signaling pathways.
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MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case–control study. Breast Cancer Res Treat 2017; 162:559-569. [DOI: 10.1007/s10549-017-4143-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 02/06/2017] [Indexed: 01/15/2023]
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Wu J, Sun X, Wang J, Cui Y, Kato F, Shirato H, Ikeda DM, Li R. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. J Magn Reson Imaging 2017; 46:1017-1027. [PMID: 28177554 DOI: 10.1002/jmri.25661] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 01/24/2017] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer. MATERIALS AND METHODS In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. RESULTS On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. CONCLUSION DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Xiaoli Sun
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.,Radiotherapy Department, First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, P.R. China
| | - Jeff Wang
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan.,Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan
| | - Yi Cui
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.,Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan
| | - Fumi Kato
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Hiroki Shirato
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan.,Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan
| | - Debra M Ikeda
- Department of Radiology, Stanford University School of Medicine, Advanced Medicine Center, Stanford, California, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
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Bickelhaupt S, Paech D, Kickingereder P, Steudle F, Lederer W, Daniel H, Götz M, Gählert N, Tichy D, Wiesenfarth M, Laun FB, Maier-Hein KH, Schlemmer HP, Bonekamp D. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017; 46:604-616. [PMID: 28152264 DOI: 10.1002/jmri.25606] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 12/07/2016] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences. MATERIALS AND METHODS From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC. RESULTS The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI. CONCLUSION In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.
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Affiliation(s)
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Franziska Steudle
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Lederer
- Radiological Clinic at the ATOS Clinic Heidelberg, Heidelberg, Germany
| | - Heidi Daniel
- Radiology Center Mannheim (RZM), Mannheim, Germany
| | - Michael Götz
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nils Gählert
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Diana Tichy
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik B Laun
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus H Maier-Hein
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Gevaert O, Echegaray S, Khuong A, Hoang CD, Shrager JB, Jensen KC, Berry GJ, Guo HH, Lau C, Plevritis SK, Rubin DL, Napel S, Leung AN. Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep 2017; 7:41674. [PMID: 28139704 PMCID: PMC5282551 DOI: 10.1038/srep41674] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 12/21/2016] [Indexed: 11/18/2022] Open
Abstract
Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient’s tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.
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Affiliation(s)
- Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine &Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Amanda Khuong
- Thoracic and GI Oncology Branch, CCR, National Institutes of Health, National Cancer Institute, Bethesda, MD, USA
| | - Chuong D Hoang
- Thoracic and GI Oncology Branch, CCR, National Institutes of Health, National Cancer Institute, Bethesda, MD, USA
| | - Joseph B Shrager
- Thoracic and GI Oncology Branch, CCR, National Institutes of Health, National Cancer Institute, Bethesda, MD, USA
| | - Kirstin C Jensen
- Department of Pathology, Stanford University Medical Center, Stanford, CA, USA.,Pathology and Laboratory Service of Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Gerald J Berry
- Department of Pathology, Stanford University Medical Center, Stanford, CA, USA
| | - H Henry Guo
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Charles Lau
- Department of Radiology, Stanford University, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | | | - Daniel L Rubin
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Ann N Leung
- Department of Radiology, Stanford University, Stanford, CA, USA
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Kickingereder P, Burth S, Wick A, Götz M, Eidel O, Schlemmer HP, Maier-Hein KH, Wick W, Bendszus M, Radbruch A, Bonekamp D. Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology 2016; 280:880-9. [PMID: 27326665 DOI: 10.1148/radiol.2016160845] [Citation(s) in RCA: 277] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters. Results SPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637). Conclusion An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Philipp Kickingereder
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sina Burth
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Antje Wick
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Götz
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Eidel
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus H Maier-Hein
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Bendszus
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Radbruch
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Bonekamp
- From the Department of Neuroradiology (P.K., S.B., O.E., M.B., A.R., D.B.) and Neurology Clinic (A.W., W.W.), University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Medical Image Computing, Medical and Biological Informatics Division (M.G., K.H.M.H.), Department of Radiology (H.P.S., A.R., D.B.), and Clinical Neuro-oncology Cooperation Unit, German Cancer Consortium (DKTK) (W.W.), German Cancer Research Center (DKFZ), Heidelberg, Germany
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Trecate G, Sinues PML, Orlandi R. Noninvasive strategies for breast cancer early detection. Future Oncol 2016; 12:1395-411. [DOI: 10.2217/fon-2015-0071] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.
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Affiliation(s)
- Giovanna Trecate
- Department of Imaging Diagnosis & Radiotherapy, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Rosaria Orlandi
- Molecular Targeting Unit, Department of Experimental Oncology & Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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87
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Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, Fan C, Conzen SD, Zuley M, Net JM, Sutton E, Whitman GJ, Morris E, Perou CM, Ji Y, Giger ML. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer 2016; 2. [PMID: 27853751 PMCID: PMC5108580 DOI: 10.1038/npjbcancer.2016.12] [Citation(s) in RCA: 230] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.
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Affiliation(s)
- Hui Li
- Department of Radiology, The University of Chicago, Chicago, IL, USA
| | - Yitan Zhu
- Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL, USA
| | | | - Erich Huang
- National Cancer Institute, Cancer Imaging Program, Bethesda, MA, USA
| | - Karen Drukker
- Department of Radiology, The University of Chicago, Chicago, IL, USA
| | - Katherine A Hoadley
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Cheng Fan
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Suzanne D Conzen
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Margarita Zuley
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jose M Net
- Department of Radiology, University of Miami Health System, Miami, FL, USA
| | - Elizabeth Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gary J Whitman
- Department of Radiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Elizabeth Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Charles M Perou
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Yuan Ji
- Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL, USA; Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Maryellen L Giger
- Department of Radiology, The University of Chicago, Chicago, IL, USA
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88
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Yamamoto S, Huang D, Du L, Korn RL, Jamshidi N, Burnette BL, Kuo MD. Radiogenomic Analysis Demonstrates Associations between (18)F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer. Radiology 2016; 280:261-70. [PMID: 27082783 DOI: 10.1148/radiol.2016160259] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Purpose To investigate whether non-small cell lung cancer (NSCLC) tumors that express high normalized maximum standardized uptake value (SUVmax) are associated with a more epithelial-mesenchymal transition (EMT)-like phenotype. Materials and Methods In this institutional review board-approved study, a public NSCLC data set that contained fluorine 18 ((18)F) fluoro-2-deoxyglucose positron emission tomography (PET) and messenger RNA expression profile data (n = 26) was obtained, and patients were categorized on the basis of measured normalized SUVmax values. Significance analysis of microarrays was then used to create a radiogenomic signature. The prognostic ability of this signature was assessed in a second independent data set that consisted of clinical and messenger RNA expression data (n = 166). Signature concordance with EMT was evaluated by means of validation in a publicly available cell line data set. Finally, by establishing an in vitro EMT lung cancer cell line model, an attempt was made to substantiate the radiogenomic signature with quantitative polymerase chain reaction, and functional assays were performed, including Western blot, cell migration, glucose transporter, and hexokinase assays (paired t test), as well as pharmacologic assays against chemotherapeutic agents (half-maximal effective concentration). Results Differential expression analysis yielded a 14-gene radiogenomic signature (P < .05, false discovery rate [FDR] < 0.20), which was confirmed to have differences in disease-specific survival (log-rank test, P = .01). This signature also significantly overlapped with published EMT cell line gene expression data (P < .05, FDR < 0.20). Finally, an EMT cell line model was established, and cells that had undergone EMT differentially expressed this signature and had significantly different EMT protein expression (P < .05, FDR < 0.20), cell migration, glucose uptake, and hexokinase activity (paired t test, P < .05). Cells that had undergone EMT also had enhanced chemotherapeutic resistance, with a higher half-maximal effective concentration than that of cells that had not undergone EMT (P < .05). Conclusion Integrative radiogenomic analysis demonstrates an association between increased normalized (18)F fluoro-2-deoxyglucose PET SUVmax, outcome, and EMT in NSCLC. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Shota Yamamoto
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
| | - Danshan Huang
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
| | - Liutao Du
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
| | - Ronald L Korn
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
| | - Neema Jamshidi
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
| | - Barry L Burnette
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
| | - Michael D Kuo
- From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.)
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Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, Diskin SJ. Imaging genomics in cancer research: limitations and promises. Br J Radiol 2016; 89:20151030. [PMID: 26864054 DOI: 10.1259/bjr.20151030] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Recently, radiogenomics or imaging genomics has emerged as a novel high-throughput method of associating imaging features with genomic data. Radiogenomics has the potential to provide comprehensive intratumour, intertumour and peritumour information non-invasively. This review article summarizes the current state of radiogenomic research in tumour characterization, discusses some of its limitations and promises and projects its future directions. Semi-radiogenomic studies that relate specific gene expressions to imaging features will also be briefly reviewed.
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Affiliation(s)
- Harrison X Bai
- 1 Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Ashley M Lee
- 1 Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Li Yang
- 2 Department of Neurology, The Second Xiangya Hospital, Changsha, Hunan, China
| | - Paul Zhang
- 3 Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- 1 Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - John M Maris
- 4 Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,5 Abramson Family Cancer Research Institute, PerelmanSchool of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,6 Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon J Diskin
- 4 Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,5 Abramson Family Cancer Research Institute, PerelmanSchool of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,6 Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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90
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Grimm LJ. Breast MRI radiogenomics: Current status and research implications. J Magn Reson Imaging 2015; 43:1269-78. [PMID: 26663695 DOI: 10.1002/jmri.25116] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2015] [Accepted: 11/24/2015] [Indexed: 11/09/2022] Open
Abstract
Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278.
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Affiliation(s)
- Lars J Grimm
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
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91
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Burnside ES, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton EJ, Brandt KR, Whitman GJ, Conzen SD, Lan L, Ji Y, Zhu Y, Jaffe CC, Huang EP, Freymann JB, Kirby JS, Morris EA, Giger ML. Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer 2015; 122:748-57. [PMID: 26619259 DOI: 10.1002/cncr.29791] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 10/08/2015] [Accepted: 10/13/2015] [Indexed: 12/13/2022]
Abstract
BACKGROUND The objective of this study was to demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage. METHODS The authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy-proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist-measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave-1-out, cross-validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest. RESULTS Tumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor "homogeneity" significantly improved discrimination (AUC = 0.62; P = .003) compared with chance. CONCLUSIONS The current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748-757. © 2015 American Cancer Society.
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Affiliation(s)
- Elizabeth S Burnside
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Hui Li
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Ermelinda Bonaccio
- Department of Radiology, Roswell Park Cancer Institute, Buffalo, New York
| | - Margarita Zuley
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Marie Ganott
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jose M Net
- University of Miami, Miller School of Medicine, Miami, Florida
| | - Elizabeth J Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kathleen R Brandt
- Department of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota
| | - Gary J Whitman
- Department of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Suzanne D Conzen
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Li Lan
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Yuan Ji
- Department of Health Studies, University of Chicago, Chicago, Illinois.,Program of Computational Genomics and Medicine, NorthShore University HealthSystem, Evanston, Illinois
| | - Yitan Zhu
- Program of Computational Genomics and Medicine, NorthShore University HealthSystem, Evanston, Illinois
| | - Carl C Jaffe
- National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Erich P Huang
- National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - John B Freymann
- National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Justin S Kirby
- National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
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Tam AL, Lim HJ, Wistuba II, Tamrazi A, Kuo MD, Ziv E, Wong S, Shih AJ, Webster RJ, Fischer GS, Nagrath S, Davis SE, White SB, Ahrar K. Image-Guided Biopsy in the Era of Personalized Cancer Care: Proceedings from the Society of Interventional Radiology Research Consensus Panel. J Vasc Interv Radiol 2015; 27:8-19. [PMID: 26626860 DOI: 10.1016/j.jvir.2015.10.019] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 10/23/2015] [Accepted: 10/23/2015] [Indexed: 02/07/2023] Open
Affiliation(s)
- Alda L Tam
- Departments of Interventional Radiology, Houston, Texas.
| | - Howard J Lim
- Division of Medical Oncology, University of British Columbia, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | | | - Anobel Tamrazi
- Division of Vascular and Interventional Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Michael D Kuo
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Etay Ziv
- Departments of Interventional Radiology and Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Stephen Wong
- Department of Systems Medicine & Bioengineering, Houston Methodist Research Institute, Houston, Texas
| | - Albert J Shih
- Departments of Mechanical and Biomechanical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Robert J Webster
- Department of Mechanical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Gregory S Fischer
- Automation and Interventional Medicine Robotics Lab, Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts
| | - Sunitha Nagrath
- Chemical and Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Suzanne E Davis
- Division of Cancer Medicine, Research Planning and Development, University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Sarah B White
- Department of Systems Medicine & Bioengineering, Houston Methodist Research Institute, Houston, Texas; Departments of Radiology, Neuroscience, Pathology & Laboratory Medicine, Weill Cornell Medical College of Cornell University, New York, New York; Division of Vascular and Interventional Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kamran Ahrar
- Departments of Interventional Radiology, Houston, Texas
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Jamshidi N, Jonasch E, Zapala M, Korn RL, Brooks JD, Ljungberg B, Kuo MD. The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial. Eur Radiol 2015; 26:2798-807. [PMID: 26560727 DOI: 10.1007/s00330-015-4082-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Revised: 09/30/2015] [Accepted: 10/23/2015] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To characterize a radiogenomic risk score (RRS), a previously defined biomarker, and to evaluate its potential for stratifying radiological progression-free survival (rPFS) in patients with metastatic renal cell carcinoma (mRCC) undergoing pre-surgical treatment with bevacizumab. METHODOLOGY In this IRB-approved study, prospective imaging analysis of the RRS was performed on phase II clinical trial data of mRCC patients (n = 41) evaluating whether patient stratification according to the RRS resulted in groups more or less likely to have a rPFS to pre-surgical bevacizumab prior to cytoreductive nephrectomy. Survival times of RRS subgroups were analyzed using Kaplan-Meier survival analysis. RESULTS The RRS is enriched in diverse molecular processes including drug response, stress response, protein kinase regulation, and signal transduction pathways (P < 0.05). The RRS successfully stratified rPFS to bevacizumab based on pre-treatment computed tomography imaging with a median progression-free survival of 6 versus >25 months (P = 0.005) and overall survival of 25 versus >37 months in the high and low RRS groups (P = 0.03), respectively. Conventional prognostic predictors including the Motzer and Heng criteria were not predictive in this cohort (P > 0.05). CONCLUSIONS The RRS stratifies rPFS to bevacizumab in patients from a phase II clinical trial with mRCC undergoing cytoreductive nephrectomy and pre-surgical bevacizumab. KEY POINTS • The RRS SOMA stratifies patient outcomes in a phase II clinical trial. • RRS stratifies subjects into prognostic groups in a discrete or continuous fashion. • RRS is biologically enriched in diverse processes including drug response programs.
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Affiliation(s)
- Neema Jamshidi
- Department of Radiological Sciences, University of California-Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Eric Jonasch
- Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Matthew Zapala
- Department of Radiological Sciences, University of California-Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Radiology, University of California-San Diego, San Diego, CA, USA
| | | | - James D Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Borje Ljungberg
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden
| | - Michael D Kuo
- Department of Radiological Sciences, University of California-Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA.
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Jamshidi N, Jonasch E, Zapala M, Korn RL, Aganovic L, Zhao H, Tumkur Sitaram R, Tibshirani RJ, Banerjee S, Brooks JD, Ljungberg B, Kuo MD. The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma. Radiology 2015; 277:114-23. [PMID: 26402495 DOI: 10.1148/radiol.2015150800] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image. MATERIALS AND METHODS In this institutional review board approved study, gene expression profile data and contrast material-enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression-based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis. RESULTS Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57, P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001). CONCLUSION A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible.
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Affiliation(s)
- Neema Jamshidi
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Eric Jonasch
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Matthew Zapala
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Ronald L Korn
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Lejla Aganovic
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Hongjuan Zhao
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Raviprakash Tumkur Sitaram
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Robert J Tibshirani
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Sudeep Banerjee
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - James D Brooks
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Borje Ljungberg
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
| | - Michael D Kuo
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.)
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