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Wei R, Lu S, Lai S, Liang F, Zhang W, Jiang X, Zhen X, Yang R. A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. J Cancer Res Clin Oncol 2024; 150:73. [PMID: 38305926 PMCID: PMC10837235 DOI: 10.1007/s00432-023-05603-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/26/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.
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Affiliation(s)
- Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Songlin Lu
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Fangrong Liang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China.
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China.
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Mahmoudi K, Kim DH, Tavakkol E, Kihira S, Bauer A, Tsankova N, Khan F, Hormigo A, Yedavalli V, Nael K. Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma. Cancers (Basel) 2024; 16:589. [PMID: 38339340 PMCID: PMC10854536 DOI: 10.3390/cancers16030589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.
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Affiliation(s)
- Keon Mahmoudi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Daniel H. Kim
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Elham Tavakkol
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USA
| | - Nadejda Tsankova
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Fahad Khan
- Department of Pathology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Adilia Hormigo
- Department of Oncology, Montefiore Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Vivek Yedavalli
- Department of Radiology and Radiological Science, Johns Hopkins Bayview Medical Center, Baltimore, MD 21224, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
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Shi J, Chen H, Wang X, Cao R, Chen Y, Cheng Y, Pang Z, Huang C. Using Radiomics to Differentiate Brain Metastases From Lung Cancer Versus Breast Cancer, Including Predicting Epidermal Growth Factor Receptor and human Epidermal Growth Factor Receptor 2 Status. J Comput Assist Tomogr 2023; 47:924-933. [PMID: 37948368 DOI: 10.1097/rct.0000000000001499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
OBJECTIVE We evaluated the feasibility of using multiregional radiomics to identify brain metastasis (BM) originating from lung adenocarcinoma (LA) and breast cancer (BC) and assess the epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) status. METHODS Our experiment included 160 patients with BM originating from LA (n = 70), BC (n = 67), and other tumor types (n = 23), between November 2017 and December 2021. All patients underwent contrast-enhanced T1- and T2-weighted magnetic resonance imaging (MRI) scans. A total of 1967 quantitative MRI features were calculated from the tumoral active area and peritumoral edema area and selected using least absolute shrinkage and selection operator regression with 5-fold cross-validation. We constructed radiomic signatures (RSs) based on the most predictive features for preoperative assessment of the metastatic origins, EGFR mutation, and HER2 status. Prediction performance of the constructed RSs was evaluated based on the receiver operating characteristic curve analysis. RESULTS The developed multiregion RSs generated good area under the receiver operating characteristic curve (AUC) for identifying the LA and BC origin in the training (AUCs, RS-LA vs RS-BC, 0.767 vs 0.898) and validation (AUCs, RS-LA vs RS-BC, 0.778 and 0.843) cohort and for predicting the EGFR and HER2 status in the training (AUCs, RS-EGFR vs RS-HER2, 0.837 vs 0.894) and validation (AUCs, RS-EGFR vs RS-HER2, 0.729 vs 0.784) cohorts. CONCLUSIONS Our results revealed associations between brain MRI-based radiomics and their metastatic origins, EGFR mutations, and HER2 status. The developed multiregion combined RSs may be considered noninvasive predictive markers for planning early treatment for BM patients.
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Affiliation(s)
- Jiaxin Shi
- From the School of Intelligent Medicine, China Medical University
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, People's Republic of China
| | - Ran Cao
- From the School of Intelligent Medicine, China Medical University
| | - Yu Chen
- From the School of Intelligent Medicine, China Medical University
| | - Yuan Cheng
- From the School of Intelligent Medicine, China Medical University
| | - Ziyan Pang
- From the School of Intelligent Medicine, China Medical University
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Lee J, Narang S, Martinez J, Rao G, Rao A. Association of graph-based spatial features with overall survival status of glioblastoma patients. Sci Rep 2023; 13:17046. [PMID: 37813981 PMCID: PMC10562480 DOI: 10.1038/s41598-023-44353-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 10/06/2023] [Indexed: 10/11/2023] Open
Abstract
Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.
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Affiliation(s)
- Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Shivali Narang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Juan Martinez
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ganesh Rao
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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Bailo M, Pecco N, Callea M, Scifo P, Gagliardi F, Presotto L, Bettinardi V, Fallanca F, Mapelli P, Gianolli L, Doglioni C, Anzalone N, Picchio M, Mortini P, Falini A, Castellano A. Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study. Front Neurosci 2022; 16:885291. [PMID: 35911979 PMCID: PMC9326318 DOI: 10.3389/fnins.2022.885291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundTumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging.ObjectivesThis study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity.Materials and MethodsSeventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed.ResultsA highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of the most aggressive clusters in WHO (World Health Organization) grade IV compared to WHO III was observed.ConclusionPreliminary findings demonstrated high reproducibility of the PET and MRI hypoxia, perfusion, and tissue diffusion spatial habitat maps and correlation with disease-specific histopathological features.
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Affiliation(s)
- Michele Bailo
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Nicolò Pecco
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Paola Scifo
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Filippo Gagliardi
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luca Presotto
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Federico Fallanca
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luigi Gianolli
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Nicoletta Anzalone
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Pietro Mortini
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Antonella Castellano
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
- *Correspondence: Antonella Castellano,
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Arthur A, Johnston EW, Winfield JM, Blackledge MD, Jones RL, Huang PH, Messiou C. Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We? Front Oncol 2022; 12:892620. [PMID: 35847882 PMCID: PMC9286756 DOI: 10.3389/fonc.2022.892620] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.
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Affiliation(s)
- Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Edward W. Johnston
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Robin L. Jones
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Division of Clinical Studies, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
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Habitat Analysis of Breast Cancer-Enhanced MRI Reflects BRCA1 Mutation Determined by Immunohistochemistry. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9623173. [PMID: 35402620 PMCID: PMC8986384 DOI: 10.1155/2022/9623173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 03/15/2022] [Indexed: 11/18/2022]
Abstract
Objective To use habitat analysis (also termed habitat imaging) for classifying untreated breast cancer-enhanced magnetic resonance imaging (MRI) in women. Moreover, we intended to obtain clustering parameters to predict the BReast CAncer gene 1 (BRCA1) gene mutation and to determine the use of MRI as a noninvasive examination tool. Methods We obtained enhanced MRI data of patients with breast cancer before treatment and selected some sequences as the source of habitat imaging. We used the k-means clustering to classify these images. According to the formed subregions, we calculated several parameters to evaluate the clustering. We used immunohistochemistry to detect BRCA1 mutations. Moreover, we separately determined the ability of these parameters through independent modeling or multiple parameter joint modeling to predict these mutations. Results Of all extracted values, separation (SP) demonstrated the best prediction performance for a single parameter (area under the receiver operating characteristic curve (AUC), 0.647; 95% confidence interval (CI), 0.557–0.731). Simultaneously, models based on the Calinski-Harabasz Index and sum of square error performed better in the training (AUC, 0.903; 95% CI, 0.831–0.96) and verification (AUC, 0.845; 95% CI, 0.723–0.942) sets for multiparameter joint modeling. Conclusion Based on the enhanced MRI of breast tumors and the subregions generated according to the habitat imaging theory, the parameters extracted to describe the clustering effect could reflect the BRCA1 status. Differences between clusters, including the general differences of cluster centers and clusters and the similarity of samples within clusters, were the embodiment of this mutation. We propose an algorithm to predict the BRCA1 mutation of a patient according to the enhanced MRI of the breast tumor.
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Antunes JT, Ismail M, Hossain I, Wang Z, Prasanna P, Madabhushi A, Tiwari P, Viswanath SE. RADIomic Spatial TexturAl descripTor (RADISTAT): Quantifying spatial organization of imaging heterogeneity associated with tumor response to treatment. IEEE J Biomed Health Inform 2022; 26:2627-2636. [PMID: 35085099 DOI: 10.1109/jbhi.2022.3146778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic expression maps, we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone.
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Radiomic Features Associated with Extent of Resection in Glioma Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:341-347. [PMID: 34862558 DOI: 10.1007/978-3-030-85292-4_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.
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Xu X, Samaras D, Prasanna P. Radiologically Defined Tumor-habitat Adjacency as a Prognostic Biomarker in Glioblastoma. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3998-4001. [PMID: 34892107 DOI: 10.1109/embc46164.2021.9629779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Intratumor heterogeneity in glioblastoma (GBM) has been linked to adverse clinical outcomes including poor survival and sub-optimal response to therapies. Different techniques, such as radiomics, have been used to characterize GBM phenotype. However, the spatial diversity and the interaction between different sub-regions within the tumor (habitats) and its microenvironment has been relatively unexplored. Besides, existing approaches have mainly focused on the radiomic analysis within globally defined regions without considering local heterogeneity. In this paper, we developed a 3D spatial co-localization descriptor based on the adjacency of "habitats" to quantify the diversity of physiologically similar sub-regions on multi-protocol magnetic resonance imaging. We demonstrated the utility of this spatial phenotype descriptor in predicting overall patient survival. Our experimental results on N=236 treatment-naïve MRI scans suggest that the co-localization features in conjunction with traditional clinical measures, such as age and tumor volume, outperform texture based radiomic features. The presented descriptor provides a tool for more complete characterization of intratumor heterogeneity in solid cancers.
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Permuth JB, Vyas S, Li J, Chen DT, Jeong D, Choi JW. Comparison of Radiomic Features in a Diverse Cohort of Patients With Pancreatic Ductal Adenocarcinomas. Front Oncol 2021; 11:712950. [PMID: 34367997 PMCID: PMC8339963 DOI: 10.3389/fonc.2021.712950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/05/2021] [Indexed: 12/20/2022] Open
Abstract
Background Significant racial disparities in pancreatic cancer incidence and mortality rates exist, with the highest rates in African Americans compared to Non-Hispanic Whites and Hispanic/Latinx populations. Computer-derived quantitative imaging or “radiomic” features may serve as non-invasive surrogates for underlying biological factors and heterogeneity that characterize pancreatic tumors from African Americans, yet studies are lacking in this area. The objective of this pilot study was to determine if the radiomic tumor profile extracted from pretreatment computed tomography (CT) images differs between African Americans, Non-Hispanic Whites, and Hispanic/Latinx with pancreatic cancer. Methods We evaluated a retrospective cohort of 71 pancreatic cancer cases (23 African American, 33 Non-Hispanic White, and 15 Hispanic/Latinx) who underwent pretreatment CT imaging at Moffitt Cancer Center and Research Institute. Whole lesion semi-automated segmentation was performed on each slice of the lesion on all pretreatment venous phase CT exams using Healthmyne Software (Healthmyne, Madison, WI, USA) to generate a volume of interest. To reduce feature dimensionality, 135 highly relevant non-texture and texture features were extracted from each segmented lesion and analyzed for each volume of interest. Results Thirty features were identified and significantly associated with race/ethnicity based on Kruskal-Wallis test. Ten of the radiomic features were highly associated with race/ethnicity independent of tumor grade, including sphericity, volumetric mean Hounsfield units (HU), minimum HU, coefficient of variation HU, four gray level texture features, and two wavelet texture features. A radiomic signature summarized by the first principal component partially differentiated African American from non-African American tumors (area underneath the curve = 0.80). Poorer survival among African Americans compared to Non-African Americans was observed for tumors with lower volumetric mean CT [HR: 3.90 (95% CI:1.19–12.78), p=0.024], lower GLCM Avg Column Mean [HR:4.75 (95% CI: 1.44,15.37), p=0.010], and higher GLCM Cluster Tendency [HR:3.36 (95% CI: 1.06–10.68), p=0.040], and associations persisted in volumetric mean CT and GLCM Avg Column after adjustment for key clinicopathologic factors. Conclusions This pilot study identified several textural radiomics features associated with poor overall survival among African Americans with PDAC, independent of other prognostic factors such as grade. Our findings suggest that CT radiomic features may serve as surrogates for underlying biological factors and add value in predicting clinical outcomes when integrated with other parameters in ongoing and future studies of cancer health disparities.
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Affiliation(s)
- Jennifer B Permuth
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Shraddha Vyas
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Jiannong Li
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Dung-Tsa Chen
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Daniel Jeong
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Jung W Choi
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
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Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 2021; 16:39. [PMID: 34078424 PMCID: PMC8173908 DOI: 10.1186/s13027-021-00379-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/12/2021] [Indexed: 02/06/2023] Open
Abstract
Background Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. Methods The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. Results We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. Conclusions In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Roberta Fusco
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy.
| | - Maria Luisa Barretta
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Carmine Picone
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Andrea Belli
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Renato Patrone
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Marilina Ferrante
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Diletta Cozzi
- Division of Radiology, "Azienda Ospedaliera Universitaria Careggi", Florence, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy.,Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
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Subregion Radiomics Analysis to Display Necrosis After Hepatic Microwave Ablation-A Proof of Concept Study. Invest Radiol 2021; 55:422-429. [PMID: 32028297 DOI: 10.1097/rli.0000000000000653] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVES The aim of this study was to improve the visualization of coagulation necrosis after computed tomography (CT)-guided microwave ablation (MWA) in routine postablational imaging. MATERIALS AND METHODS Ten MWAs were performed in 8 pigs under CT guidance. After each ablation, we obtained contrast-enhanced CT scans in venous phase. Ablations were then resected as a whole, and histologic slices were obtained orthogonally through the ablation center. Subsequently, a vital stain was applied to the sections for visualization of coagulation necrosis. Computed tomography images were reformatted to match the histologic slices. Afterwards, quantitative imaging features were extracted from the subregions of all images, and binary classifiers were used to predict the presence of coagulation necrosis for each subregion. From this, heatmaps could be created, which visually represented the extent of necrosis in each CT image. Two independent observers evaluated the extent of coagulative necrosis between the heat maps and histological sections. RESULTS We applied 4 different classifiers, including a generalized linear mixed model (GLMM), a stochastic gradient boosting classifier, a random forest classifier, and a k-nearest neighbor classifier, out of which the GLMM showed the best performance to display coagulation necrosis. The GLMM resulted in an area under the curve of 0.84 and a Jaccard index of 0.6 between the generated heat map and the histologic reference standard as well as a good interobserver agreement with a Jaccard index of 0.9. CONCLUSIONS Subregion radiomics analysis may improve visualization of coagulation necrosis after hepatic MWA in an in vivo porcine model.
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Álvarez-Torres MDM, Fuster-García E, Reynés G, Juan-Albarracín J, Chelebian E, Oleaga L, Pineda J, Auger C, Rovira A, Emblem KE, Filice S, Mollà-Olmos E, García-Gómez JM. Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma. NMR IN BIOMEDICINE 2021; 34:e4462. [PMID: 33470039 DOI: 10.1002/nbm.4462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/28/2020] [Indexed: 06/12/2023]
Abstract
INTRODUCTION IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. PURPOSE To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. MATERIALS AND METHODS Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS ≥ 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). RESULTS For LTSs, we found a significant association between a moderate value of rCBVmean and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBVmean and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. CONCLUSION We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBVmean at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.
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Affiliation(s)
| | | | - Gaspar Reynés
- Cancer Research Group, Health Research Institute Hospital La Fe, Valencia, Spain
| | | | | | | | - Jose Pineda
- Hospital Clinic de Barcelona, Barcelona, Spain
| | - Cristina Auger
- Magnetic Resonance Unit, Department of Radiology, Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alex Rovira
- Magnetic Resonance Unit, Department of Radiology, Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Kyrre E Emblem
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Silvano Filice
- Medical Physics, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Enrique Mollà-Olmos
- Departamento de Radiodiagnóstico, Hospital Universitario de la Ribera, Alzira, Spain
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Abstract
PURPOSE OF REVIEW Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications. RECENT FINDINGS The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.
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Raghunand N, Gatenby RA. Bridging Spatial Scales From Radiographic Images to Cellular and Molecular Properties in Cancers. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00053-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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18
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Blystad I, Warntjes JBM, Smedby Ö, Lundberg P, Larsson EM, Tisell A. Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema. Sci Rep 2020; 10:17986. [PMID: 33093605 PMCID: PMC7581520 DOI: 10.1038/s41598-020-75105-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 10/12/2020] [Indexed: 11/09/2022] Open
Abstract
Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.
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Affiliation(s)
- I Blystad
- Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden. .,Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - J B M Warntjes
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Ö Smedby
- Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - P Lundberg
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiation Physics and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - E-M Larsson
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - A Tisell
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiation Physics and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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20
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Syed AK, Whisenant JG, Barnes SL, Sorace AG, Yankeelov TE. Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer. Cancers (Basel) 2020; 12:cancers12061682. [PMID: 32599906 PMCID: PMC7352623 DOI: 10.3390/cancers12061682] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.
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Affiliation(s)
- Anum K Syed
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Jennifer G Whisenant
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Stephanie L Barnes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anna G Sorace
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
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Ahn SJ, Kwon H, Yang JJ, Park M, Cha YJ, Suh SH, Lee JM. Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer. Sci Rep 2020; 10:8905. [PMID: 32483122 PMCID: PMC7264319 DOI: 10.1038/s41598-020-65470-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/30/2020] [Indexed: 01/01/2023] Open
Abstract
Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.
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Affiliation(s)
- Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Hyeokjin Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Jin-Ju Yang
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
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Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. Neuroradiology 2020; 62:771-790. [DOI: 10.1007/s00234-020-02403-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 03/10/2020] [Indexed: 12/14/2022]
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Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas. Eur Radiol 2020; 30:3254-3265. [PMID: 32078014 DOI: 10.1007/s00330-020-06702-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/29/2019] [Accepted: 02/03/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVE The current study aimed to evaluate the clinical practice for hemodynamic tissue signature (HTS) method in IDH genotype prediction in three groups derived from high-grade gliomas. METHODS Preoperative MRI examinations of 44 patients with known grade and IDH genotype were assigned into three study groups: glioblastoma multiforme, grade III, and high-grade gliomas. Perfusion parameters were analyzed and were used to automatically draw the four reproducible habitats (high-angiogenic enhancing tumor habitats, low-angiogenic enhancing tumor habitats, infiltrated peripheral edema habitats, vasogenic peripheral edema habitats) related to vascular heterogeneity. These four habitats were then compared between inter-patient with IDH mutation and their wild-type counterparts at these three groups, respectively. The discriminating potential for HTS in assessing IDH mutation status prediction was assessed by ROC curves. RESULTS Compared with IDH wild type, IDH mutation had significantly decreased relative cerebral blood volume (rCBV) at the high-angiogenic enhancing tumor habitats and low-angiogenic enhancing tumor habitats. ROC analysis revealed that the rCBVs in habitats had great ability to discriminate IDH mutation from their wild type in all groups. In addition, the Kaplan-Meier survival analysis yielded significant differences for the survival times observed from the populations dichotomized by low (< 4.31) and high (> 4.31) rCBV in the low-angiogenic enhancing tumor habitat. CONCLUSIONS The HTS method has been proven to have high prediction capabilities for IDH mutation status in high-grade glioma patients, providing a set of quantifiable habitats associated with tumor vascular heterogeneity. KEY POINTS • The HTS method has a high accuracy for molecular stratification prediction for all subsets of HGG. • The HTS method can give IDH mutation-related hemodynamic information of tumor-infiltrated and vasogenic edema. • IDH-relevant rCBV difference in habitats will be a great prognosis factor in HGG.
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Stringfield O, Arrington JA, Johnston SK, Rognin NG, Peeri NC, Balagurunathan Y, Jackson PR, Clark-Swanson KR, Swanson KR, Egan KM, Gatenby RA, Raghunand N. Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme. ACTA ACUST UNITED AC 2020; 5:135-144. [PMID: 30854451 PMCID: PMC6403044 DOI: 10.18383/j.tom.2018.00052] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological "habitats" at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct "habitats" based on low- to medium- to high-contrast enhancement and low-high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.
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Affiliation(s)
| | - John A Arrington
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Sandra K Johnston
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ.,Department of Radiology, University of Washington, Seattle, WA; and
| | | | - Noah C Peeri
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL
| | | | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kamala R Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kathleen M Egan
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Robert A Gatenby
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Natarajan Raghunand
- Cancer Physiology, and.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
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Virostko J. Quantitative Magnetic Resonance Imaging of the Pancreas of Individuals With Diabetes. Front Endocrinol (Lausanne) 2020; 11:592349. [PMID: 33343509 PMCID: PMC7747766 DOI: 10.3389/fendo.2020.592349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/28/2020] [Indexed: 12/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) has the potential to improve our understanding of diabetes and improve both diagnosis and monitoring of the disease. Although the spatial resolution of MRI is insufficient to directly image the endocrine pancreas in people, the increasing awareness that the exocrine pancreas is also involved in diabetes pathogenesis has spurred new MRI applications. These techniques build upon studies of exocrine pancreatic diseases, for which MRI has already developed into a routine clinical tool for diagnosis and monitoring of pancreatic cancer and pancreatitis. By adjusting the imaging contrast and carefully controlling image acquisition and processing, MRI can quantify a variety of tissue pathologies. This review introduces a number of quantitative MRI techniques that have been applied to study the diabetic pancreas, summarizes progress in validating and standardizing each technique, and discusses the need for image analyses that account for spatial heterogeneity in the pancreas.
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Affiliation(s)
- John Virostko
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, United States
- Department of Oncology, University of Texas at Austin, Austin, TX, United States
- *Correspondence: John Virostko,
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Stanková K, Brown JS, Dalton WS, Gatenby RA. Optimizing Cancer Treatment Using Game Theory: A Review. JAMA Oncol 2019; 5:96-103. [PMID: 30098166 DOI: 10.1001/jamaoncol.2018.3395] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Importance While systemic therapy for disseminated cancer is often initially successful, malignant cells, using diverse adaptive strategies encoded in the human genome, almost invariably evolve resistance, leading to treatment failure. Thus, the Darwinian dynamics of resistance are formidable barriers to all forms of systemic cancer treatment but rarely integrated into clinical trial design or included within precision oncology initiatives. Observations We investigate cancer treatment as a game theoretic contest between the physician's therapy and the cancer cells' resistance strategies. This game has 2 critical asymmetries: (1) Only the physician can play rationally. Cancer cells, like all evolving organisms, can only adapt to current conditions; they can neither anticipate nor evolve adaptations for treatments that the physician has not yet applied. (2) It has a distinctive leader-follower (or "Stackelberg") dynamics; the "leader" oncologist plays first and the "follower" cancer cells then respond and adapt to therapy. Current treatment protocols for metastatic cancer typically exploit neither asymmetry. By repeatedly administering the same drug(s) until disease progression, the physician "plays" a fixed strategy even as the opposing cancer cells continuously evolve successful adaptive responses. Furthermore, by changing treatment only when the tumor progresses, the physician cedes leadership to the cancer cells and treatment failure becomes nearly inevitable. Without fundamental changes in strategy, standard-of-care cancer therapy typically results in "Nash solutions" in which no unilateral change in treatment can favorably alter the outcome. Conclusions and Relevance Physicians can exploit the advantages inherent in the asymmetries of the cancer treatment game, and likely improve outcomes, by adopting more dynamic treatment protocols that integrate eco-evolutionary dynamics and modulate therapy accordingly. Implementing this approach will require new metrics of tumor response that incorporate both ecological (ie, size) and evolutionary (ie, molecular mechanisms of resistance and relative size of resistant population) changes.
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Affiliation(s)
- Katerina Stanková
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, the Netherlands.,Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
| | - Joel S Brown
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, Florida
| | - William S Dalton
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, Florida.,M2Gen Health Informatics Solutions, Tampa, Florida
| | - Robert A Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, Florida
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Zhang Y, Morris JS, Aerry SN, Rao AU, Baladandayuthapani V. RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA. Ann Appl Stat 2019; 13:1957-1988. [PMID: 33224404 PMCID: PMC7678720 DOI: 10.1214/19-aoas1238] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, wherein our goals are to simultaneously identify genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. For this task, we propose Radio-iBAG: Radiomics-based Integrative Bayesian Analysis of Multiplatform Genomic Data, a multi-scale Bayesian hierarchical model that involves several innovative strategies: it incorporates integrative analysis of multi-platform genomic data sets to capture fundamental biological relationships; explores the associations between radiomic markers accompanying genomic information with clinical outcomes; and detects genomic and radiomic markers associated with clinical prognosis. We also introduce the use of sparse Principal Component Analysis (sPCA) to extract a sparse set of approximately orthogonal meta-features each containing information from a set of related individual radiomic features, reducing dimensionality and combining like features. Our methods are motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, where-in we integrate magnetic resonance imaging-based biomarkers along with genomic, epigenomic and transcriptomic data. Our model identifies important magnetic resonance imaging features and the associated genomic platforms that are related with patient survival times.
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Affiliation(s)
- Youyi Zhang
- The University of Texas MD Anderson Cancer Center
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28
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Um H, Tixier F, Bermudez D, Deasy JO, Young RJ, Veeraraghavan H. Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets. ACTA ACUST UNITED AC 2019; 64:165011. [DOI: 10.1088/1361-6560/ab2f44] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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29
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Johnston SK, Whitmire P, Massey SC, Kumthekar P, Porter AB, Raghunand N, Gonzalez-Cuyar LF, Mrugala MM, Hawkins-Daarud A, Jackson PR, Hu LS, Sarkaria JN, Wang L, Gatenby RA, Egan KM, Canoll P, Swanson KR. ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: A Multidisciplinary Team-based, Multifactorial Analytical Approach. Am J Clin Oncol 2019; 42:655-661. [PMID: 31343422 PMCID: PMC7416695 DOI: 10.1097/coc.0000000000000564] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Although glioblastoma (GBM) is a fatal primary brain cancer with short median survival of 15 months, a small number of patients survive >5 years after diagnosis; they are known as extreme survivors (ES). Because of their rarity, very little is known about what differentiates these outliers from other patients with GBM. For the purpose of identifying unknown drivers of extreme survivorship in GBM, the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of GBM) was developed. This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histologic parameters. Leveraging our combined resources, the goals of the ENDURES consortium are 2-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with GBM; and (2) to leverage the ENDURES repository for new insights into tumor behavior and novel targets for prolonging survival for all patients with GBM. In this article, the authors review the available literature and discuss what is already known about ES. The authors then describe the creation of their consortium and some preliminary results.
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Affiliation(s)
- Sandra K. Johnston
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
- Department of Radiology, University of Washington, Seattle, WA
| | - Paula Whitmire
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Susan Christine Massey
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Priya Kumthekar
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | | | | | - Luis F. Gonzalez-Cuyar
- Department of Pathology, Neuropathology Division, University of Washington Medical Center, Seattle, WA
| | | | - Andrea Hawkins-Daarud
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Pamela R. Jackson
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ
| | | | - Lei Wang
- Departments of Radiology & Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Robert A. Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL
| | | | - Peter Canoll
- Division of Neuropathology, Department of Pathology and Cell Biology, Columbia University School of Medicine, New York, NY
| | - Kristin R. Swanson
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
- Department of Neurosurgery, Mayo Clinic, Phoenix, AZ
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ
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Li C, Yan JL, Torheim T, McLean MA, Boonzaier NR, Zou J, Huang Y, Yuan J, van Dijken BRJ, Matys T, Markowetz F, Price SJ. Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival. Radiother Oncol 2019; 134:17-24. [PMID: 31005212 PMCID: PMC6486398 DOI: 10.1016/j.radonc.2019.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND PURPOSE Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management. MATERIALS AND METHODS A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival. RESULTS Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005). CONCLUSIONS Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK.
| | - Jiun-Lin Yan
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan; Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Mary A McLean
- Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Natalie R Boonzaier
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, UK
| | - Jingjing Zou
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK
| | - Yuan Huang
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK; Department of Radiology, University of Cambridge, UK
| | - Jianmin Yuan
- Department of Radiology, University of Cambridge, UK
| | - Bart R J van Dijken
- Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Tomasz Matys
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK; Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, University of Cambridge, UK
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Pérez-Beteta J, Martínez-González A, Pérez-García VM. A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours. J R Soc Interface 2018; 15:20180503. [PMID: 30958226 PMCID: PMC6303800 DOI: 10.1098/rsif.2018.0503] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 11/05/2018] [Indexed: 12/30/2022] Open
Abstract
Glioblastoma (GBM) is the most frequent and aggressive type of primary brain tumour. The development of image-based biomarkers from magnetic resonance images (MRIs) has been a topic of recent interest. GBMs on pre-treatment post-contrast T1-weighted (w) MRIs often appear as rim-shaped regions. In this research, we wanted to define rim-shape complexity (RSC) descriptors and study their value as indicators of the tumour's biological aggressiveness. We constructed a set of widths characterizing the rim-shaped contrast-enhancing areas in T1w MRIs, defined measures of the RSC and computed them for 311 GBM patients. Survival analysis, correlations and sensitivity studies were performed to assess the prognostic value of the measurements. All measures obtained from the histograms were found to depend on the class width to some extent. Several measures (FWHM and βR) had high prognostic value. Some histogram-independent measures were predictors of survival: maximum rim width, mean rim width and spherically averaged rim width. The later quantity allowed patients to be classified into subgroups with different rates of survival (mean difference 6.28 months, p = 0.006). In conclusion, some of the morphological quantifiers obtained from pre-treatment T1w MRIs provided information on the biological aggressiveness of GBMs. The results can be used to define prognostic measurements of clinical applicability.
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Affiliation(s)
- J. Pérez-Beteta
- Department of Mathematics, Mathematical Oncology Laboratory (MôLAB), University of Castilla-La Mancha, 13071 Ciudad Real, Spain
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Napel S, Mu W, Jardim‐Perassi BV, Aerts HJWL, Gillies RJ. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer 2018; 124:4633-4649. [PMID: 30383900 PMCID: PMC6482447 DOI: 10.1002/cncr.31630] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 07/11/2018] [Accepted: 07/17/2018] [Indexed: 11/07/2022]
Abstract
Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.
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Affiliation(s)
- Sandy Napel
- Department of RadiologyStanford UniversityStanfordCalifornia
| | - Wei Mu
- Department of Cancer PhysiologyH. Lee Moffitt Cancer CenterTampaFlorida
| | | | - Hugo J. W. L. Aerts
- Dana‐Farber Cancer Institute, Department of Radiology, Brigham and Women’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Robert J. Gillies
- Department of Cancer PhysiologyH. Lee Moffitt Cancer CenterTampaFlorida
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Naghavi AO, Yang GQ, Latifi K, Gillies R, McLeod H, Harrison LB. The Future of Radiation Oncology in Soft Tissue Sarcoma. Cancer Control 2018. [PMCID: PMC6291881 DOI: 10.1177/1073274818815504] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy (RT) is an important component of the treatment of soft tissue sarcomas (STS) and has been traditionally incorporated with a homogenous approach despite the reality that STS displays a known heterogeneity in clinicopathologic features and treatment outcomes. In this article, we explore the principle components of personalized medicine, including genomics, radiomics, and treatment response, along with their impact on the future of radiation therapy for STS. We propose a shift in the treatment paradigm for STS from a one-size-fits-all technique to one that implements the tenets of personalized medicine and includes the framework for a potential clinical trial technique in this heterogeneous disease.
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Affiliation(s)
- Arash O. Naghavi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- These authors contributed equally to this work
| | - George Q. Yang
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- These authors contributed equally to this work
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Robert Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Howard McLeod
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Louis B. Harrison
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol 2018; 140:297-306. [PMID: 30097822 DOI: 10.1007/s11060-018-2953-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/18/2018] [Indexed: 01/08/2023]
Abstract
PURPOSE To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas. METHODS This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93). RESULTS The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction. CONCLUSION Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.
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Wei J, Yang G, Hao X, Gu D, Tan Y, Wang X, Dong D, Zhang S, Wang L, Zhang H, Tian J. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 2018; 29:877-888. [PMID: 30039219 PMCID: PMC6302873 DOI: 10.1007/s00330-018-5575-z] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 04/30/2018] [Accepted: 05/29/2018] [Indexed: 01/23/2023]
Abstract
Objectives Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. Methods We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. Results The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). Conclusions The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. Key Points • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas. • Habitat-based radiomics can improve efficacy in predicting MGMT methylation status. • Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect. Electronic supplementary material The online version of this article (10.1007/s00330-018-5575-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xiaohan Hao
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xiaochun Wang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Di Dong
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuaitong Zhang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Le Wang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Hui Zhang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China.
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
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Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Med Biol Eng Comput 2018; 56:2287-2300. [DOI: 10.1007/s11517-018-1858-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 05/27/2018] [Indexed: 12/19/2022]
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37
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Liu J, Chen F, Pan C, Zhu M, Zhang X, Zhang L, Liao H. A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas. IEEE Trans Biomed Eng 2018; 65:1943-1952. [PMID: 29993462 DOI: 10.1109/tbme.2018.2845706] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
GOAL Automatic segmentation of brainstem gliomas and prediction of genotype (H3 K27M) mutation status based on magnetic resonance (MR) images are crucial but challenging tasks for computer-aided diagnosis in neurosurgery. In this paper, we present a novel cascaded deep convolutional neural network (CNN) to address these two challenging tasks simultaneously. METHODS Our novel segmentation task contains two feature-fusion modules: the Gaussian-pyramid multiscale input features-fusion technique and the brainstem-region feature enhancement. The aim is to resolve very difficult problems in brainstem glioma segmentation. Our prediction model combines CNN features and support-vector-machine classifier to automatically predict genotypes without region-of-interest labeled-MR images and is learned jointly with the segmentation task. First, Gaussian-pyramid multiscale input feature fusion is added to our glioma-segmentation task to solve the problems of size variety and weak brainstem-gliomas boundaries. Second, the two feature-fusion modules provide both local and global contexts to retain higher frequency details for sharper tumor boundaries, handling the problem of the large variation of tumor shape, and volume resolution. RESULTS AND CONCLUSION Experiments demonstrate that our cascaded CNN method achieves not only a good tumor segmentation result with a high Dice similarity coefficient of 77.03%, but also a competitive genotype prediction result with an average accuracy of 94.85% upon fivefold cross-validation.
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Wu J, Cao G, Sun X, Lee J, Rubin DL, Napel S, Kurian AW, Daniel BL, Li R. Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology 2018; 288:26-35. [PMID: 29714680 DOI: 10.1148/radiol.2018172462] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Purpose To characterize intratumoral spatial heterogeneity at perfusion magnetic resonance (MR) imaging and investigate intratumoral heterogeneity as a predictor of recurrence-free survival (RFS) in breast cancer. Materials and Methods In this retrospective study, a discovery cohort (n = 60) and a multicenter validation cohort (n = 186) were analyzed. Each tumor was divided into multiple spatially segregated, phenotypically consistent subregions on the basis of perfusion MR imaging parameters. The authors first defined a multiregional spatial interaction (MSI) matrix and then, based on this matrix, calculated 22 image features. A network strategy was used to integrate all image features and classify patients into different risk groups. The prognostic value of imaging-based stratification was evaluated in relation to clinical-pathologic factors with multivariable Cox regression. Results Three intratumoral subregions with high, intermediate, and low MR perfusion were identified and showed high consistency between the two cohorts. Patients in both cohorts were stratified according to network analysis of multiregional image features regarding RFS (log-rank test, P = .002 for both). Aggressive tumors were associated with a larger volume of the poorly perfused subregion as well as interaction between poorly and moderately perfused subregions and surrounding parenchyma. At multivariable analysis, the proposed MSI-based marker was independently associated with RFS (hazard ratio: 3.42; 95% confidence interval: 1.55, 7.57; P = .002) adjusting for age, estrogen receptor (ER) status, progesterone receptor status, human epidermal growth factor receptor type 2 (HER2) status, tumor volume, and pathologic complete response (pCR). Furthermore, imaging helped stratify patients for RFS within the ER-positive and HER2-positive subgroups (log-rank test, P = .007 and .004) and among patients without pCR after neoadjuvant chemotherapy (log-rank test, P = .003). Conclusion Breast cancer consists of multiple spatially distinct subregions. Imaging heterogeneity is an independent prognostic factor beyond traditional risk predictors.
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Affiliation(s)
- Jia Wu
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Guohong Cao
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Xiaoli Sun
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Juheon Lee
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Daniel L Rubin
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Sandy Napel
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Allison W Kurian
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Bruce L Daniel
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
| | - Ruijiang Li
- From the Departments of Radiation Oncology (J.W., J.L., R.L.), Radiology (D.L.R., S.N., B.L.D.), Biomedical Data Science and Medicine (Biomedical Informatics Research) (D.L.R.), Medicine (A.W.K.), and Health Research and Policy (A.W.K.) and the Stanford Cancer Institute (A.W.K., R.L.), Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304; Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China (G.C.); and Department of Radiation Therapy, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China (X.S.)
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Shen C, Liu Z, Wang Z, Guo J, Zhang H, Wang Y, Qin J, Li H, Fang M, Tang Z, Li Y, Qu J, Tian J. Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction. Transl Oncol 2018; 11:815-824. [PMID: 29727831 PMCID: PMC6154864 DOI: 10.1016/j.tranon.2018.04.005] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Revised: 04/10/2018] [Accepted: 04/10/2018] [Indexed: 11/17/2022] Open
Abstract
PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.
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Affiliation(s)
- Chen Shen
- School of Life Science and Technology, XIDIAN University, Xi'an, Shaanxi, 710126, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Zhaoqi Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China
| | - Jia Guo
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China
| | - Hongkai Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China
| | - Yingshu Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China
| | - Jianjun Qin
- Department of Thoracic Surgery, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China
| | - Hailiang Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080,China
| | - Zhenchao Tang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China; School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong Province, 264209, China
| | - Yin Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China.
| | - Jinrong Qu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, 450003, China.
| | - Jie Tian
- School of Life Science and Technology, XIDIAN University, Xi'an, Shaanxi, 710126, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080,China.
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Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. JOURNAL OF RADIATION RESEARCH 2018; 59:i25-i31. [PMID: 29385618 PMCID: PMC5868194 DOI: 10.1093/jrr/rrx102] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 12/14/2017] [Indexed: 06/07/2023]
Abstract
Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, USA
| | - Khin Khin Tha
- Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, USA
- Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, USA
- Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
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Permuth JB, Choi J, Balarunathan Y, Kim J, Chen DT, Chen L, Orcutt S, Doepker MP, Gage K, Zhang G, Latifi K, Hoffe S, Jiang K, Coppola D, Centeno BA, Magliocco A, Li Q, Trevino J, Merchant N, Gillies R, Malafa M. Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget 2018; 7:85785-85797. [PMID: 27589689 PMCID: PMC5349874 DOI: 10.18632/oncotarget.11768] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 07/14/2016] [Indexed: 12/21/2022] Open
Abstract
Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors incidentally discovered by cross-sectional imaging. Consensus guidelines for IPMN management rely on standard radiologic features to predict pathology, but they lack accuracy. Using a retrospective cohort of 38 surgically-resected, pathologically-confirmed IPMNs (20 benign; 18 malignant) with preoperative computed tomography (CT) images and matched plasma-based ‘miRNA genomic classifier (MGC)’ data, we determined whether quantitative ‘radiomic’ CT features (+/- the MGC) can more accurately predict IPMN pathology than standard radiologic features ‘high-risk’ or ‘worrisome’ for malignancy. Logistic regression, principal component analyses, and cross-validation were used to examine associations. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated. The MGC, ‘high-risk,’ and ‘worrisome’ radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Fourteen radiomic features differentiated malignant from benign IPMNs (p<0.05) and collectively had an AUC=0.77. Combining radiomic features with the MGC revealed an AUC=0.92 and superior sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) than other models. Evaluation of uncertainty by 10-fold cross-validation retained an AUC>0.80 (0.87 (95% CI:0.84-0.89)). This proof-of-concept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than ‘worrisome’ radiologic features considered in consensus guidelines.
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Affiliation(s)
- Jennifer B Permuth
- Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.,Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Jung Choi
- Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Yoganand Balarunathan
- Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Jongphil Kim
- Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Dung-Tsa Chen
- Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Lu Chen
- Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Sonia Orcutt
- Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Matthew P Doepker
- Department of Clinical Surgery/Surgical Oncology, Palmetto Health/USC School of Medicine, Columbia, South Carolina, USA
| | - Kenneth Gage
- Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Geoffrey Zhang
- Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.,Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Kujtim Latifi
- Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.,Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Sarah Hoffe
- Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.,Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Kun Jiang
- Anatomic Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Domenico Coppola
- Anatomic Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Barbara A Centeno
- Anatomic Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Anthony Magliocco
- Anatomic Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Qian Li
- Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.,Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jose Trevino
- Department of Surgery, Division of General Surgery, University of Florida Health Sciences Center, Gainesville, Florida, USA
| | - Nipun Merchant
- Department of Surgery, Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Robert Gillies
- Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Mokenge Malafa
- Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
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Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 2018; 39:208-216. [PMID: 28982791 PMCID: PMC5812810 DOI: 10.3174/ajnr.a5391] [Citation(s) in RCA: 208] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.
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Affiliation(s)
- M Zhou
- From the Stanford Center for Biomedical Informatic Research (M.Z., O.G.)
| | - J Scott
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - B Chaudhury
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - L Hall
- Department of Computer Science and Engineering (L.H., D.G.), University of South Florida, Tampa, Florida
| | - D Goldgof
- Department of Computer Science and Engineering (L.H., D.G.), University of South Florida, Tampa, Florida
| | - K W Yeom
- Department of Radiology (K.W.Y., M.I.), Stanford University, Stanford, California
| | - M Iv
- Department of Radiology (K.W.Y., M.I.), Stanford University, Stanford, California
| | - Y Ou
- Department of Radiology (Y.O., J.K.-C.), Massachusetts General Hospital, Boston, Massachusetts
| | - J Kalpathy-Cramer
- Department of Radiology (Y.O., J.K.-C.), Massachusetts General Hospital, Boston, Massachusetts
| | - S Napel
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - R Gillies
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - O Gevaert
- From the Stanford Center for Biomedical Informatic Research (M.Z., O.G.)
| | - R Gatenby
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
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Stoyanova R, Pollack A, Takhar M, Lynne C, Parra N, Lam LLC, Alshalalfa M, Buerki C, Castillo R, Jorda M, Ashab HAD, Kryvenko ON, Punnen S, Parekh DJ, Abramowitz MC, Gillies RJ, Davicioni E, Erho N, Ishkanian A. Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget 2018; 7:53362-53376. [PMID: 27438142 PMCID: PMC5288193 DOI: 10.18632/oncotarget.10523] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 06/30/2016] [Indexed: 01/06/2023] Open
Abstract
Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.
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Affiliation(s)
- Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mandeep Takhar
- Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada
| | - Charles Lynne
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nestor Parra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lucia L C Lam
- Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada
| | | | - Christine Buerki
- Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada
| | - Rosa Castillo
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Merce Jorda
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA.,Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Oleksandr N Kryvenko
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA.,Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sanoj Punnen
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Dipen J Parekh
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Robert J Gillies
- Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, USA
| | - Elai Davicioni
- Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada
| | - Nicholas Erho
- Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada
| | - Adrian Ishkanian
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
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Dextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A. Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget 2017; 8:112992-113001. [PMID: 29348883 PMCID: PMC5762568 DOI: 10.18632/oncotarget.22947] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 11/20/2017] [Indexed: 12/28/2022] Open
Abstract
Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.
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Affiliation(s)
- Katherine Dextraze
- Department of Medical Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Abhijoy Saha
- Department of Statistics, The Ohio State University, Columbus, OH, USA
| | - Donnie Kim
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shivali Narang
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Lehrer
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita Rao
- Texas Academy of Math and Science, Denton, TX, USA.,School of Engineering and Applied Sciences, Columbia University, New York City, NY, USA
| | - Saphal Narang
- Debakey High School for Health Professions, Houston, TX, USA
| | - Dinesh Rao
- Radiology, University of Florida, College of Medicine, Jacksonville, FL, USA
| | - Salmaan Ahmed
- Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Clifton David Fuller
- Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michelle M Kim
- Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - Sunil Krishnan
- Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ganesh Rao
- Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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45
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Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, Janiszewska M, Silva AS, Gerlinger M, Yuan Y, Pienta KJ, Anderson KS, Gatenby R, Swanton C, Posada D, Wu CI, Schiffman JD, Hwang ES, Polyak K, Anderson ARA, Brown JS, Greaves M, Shibata D. Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 2017; 17:605-619. [PMID: 28912577 PMCID: PMC5811185 DOI: 10.1038/nrc.2017.69] [Citation(s) in RCA: 243] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.
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Affiliation(s)
- Carlo C Maley
- Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, 1001 S. McAllister Ave, Tempe, Arizona 85287, USA
| | - Athena Aktipis
- Department of Psychology, Center for Evolution and Medicine, Arizona State University, 651 E. University Drive, Tempe, Arizona 85287, USA
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
| | - Amy M Boddy
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - Michalina Janiszewska
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue D740C, Boston, Massachusetts 02215, USA
| | - Ariosto S Silva
- Department of Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Marco Gerlinger
- Centre for Evolution and Cancer, Division of Molecular Pathology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
| | - Kenneth J Pienta
- Brady Urological Institute, The Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, Maryland 21287, USA
| | - Karen S Anderson
- Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, 1001 S. McAllister Ave, Tempe, Arizona 85287, USA
| | - Robert Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O'Gorman Building, 72 Huntley Street, London WC1E 6BT, UK
| | - David Posada
- Department of Biochemistry, Genetics and Immunology and Biomedical Research Center (CINBIO), University of Vigo, Spain; Galicia Sur Health Research Institute, Vigo, 36310, Spain
| | - Chung-I Wu
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637, USA
| | - Joshua D Schiffman
- Departments of Pediatrics and Oncological Sciences, Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope, Salt Lake City, Utah 84108, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University and Duke Cancer Institute, 465 Seeley Mudd Building, Durham, North Carolina 27710, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue D740C, Boston, Massachusetts 02215, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Joel S Brown
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Mel Greaves
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
| | - Darryl Shibata
- Department of Pathology, Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Avenue, NOR2424, Los Angeles, California 90033, USA
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46
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Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol 2017; 27:4188-4197. [PMID: 27778090 PMCID: PMC5403632 DOI: 10.1007/s00330-016-4637-3] [Citation(s) in RCA: 176] [Impact Index Per Article: 25.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 09/28/2016] [Accepted: 10/05/2016] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM. METHODS Sixty-five patient examinations (29 short-term, 36 long-term) with gadolinium-contrast T1w, FLAIR and T2w sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ and tumour necrosis. 402 radiomic features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. Evaluation was performed using threefold cross-validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival. RESULTS A subset of ten radiomic 'peritumoral' MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 × 10-5) as compared to features from enhancing tumour, necrotic regions and known clinical factors. CONCLUSION Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long- versus short-term survival in GBM. KEY POINTS • Radiomic features from peritumoral regions can capture glioblastoma heterogeneity to predict outcome. • Peritumoral radiomics along with clinical factors are highly predictive of glioblastoma outcome. • Identifying prognostic markers can assist in making personalized therapy decisions in glioblastoma.
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Affiliation(s)
- Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Jay Patel
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Sasan Partovi
- Case Western Reserve School of Medicine, University Hospitals Case Medical Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
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47
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Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma. Oncotarget 2017; 8:101244-101254. [PMID: 29254160 PMCID: PMC5731870 DOI: 10.18632/oncotarget.20643] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 08/07/2017] [Indexed: 01/22/2023] Open
Abstract
This study analyzed magnetic resonance imaging (MRI) scans of Glioblastoma (GB) patients to develop an imaging-derived predictive model for assessing the extent of intratumoral CD3 T-cell infiltration. Pre-surgical T1-weighted post-contrast and T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) MRI scans, with corresponding mRNA expression of CD3D/E/G were obtained through The Cancer Genome Atlas (TCGA) for 79 GB patients. The tumor region was contoured and 86 image-derived features were extracted across the T1-post contrast and FLAIR images. Six imaging features—kurtosis, contrast, small zone size emphasis, low gray level zone size emphasis, high gray level zone size emphasis, small zone high gray level emphasis—were found associated with CD3 activity and used to build a predictive model for CD3 infiltration in an independent data set of 69 GB patients (using a 50-50 split for training and testing). For the training set, the image-based prediction model for CD3 infiltration achieved accuracy of 97.1% and area under the curve (AUC) of 0.993. For the test set, the model achieved accuracy of 76.5% and AUC of 0.847. This suggests a relationship between image-derived textural features and CD3 T-cell infiltration enabling the non-invasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.
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48
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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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49
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Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, Weigelt B, Vargas HA. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol 2017; 72:3-10. [PMID: 27742105 PMCID: PMC5503113 DOI: 10.1016/j.crad.2016.09.013] [Citation(s) in RCA: 203] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 09/06/2016] [Accepted: 09/12/2016] [Indexed: 12/18/2022]
Abstract
Tumour heterogeneity in cancers has been observed at the histological and genetic levels, and increased levels of intra-tumour genetic heterogeneity have been reported to be associated with adverse clinical outcomes. This review provides an overview of radiomics, radiogenomics, and habitat imaging, and examines the use of these newly emergent fields in assessing tumour heterogeneity and its implications. It reviews the potential value of radiomics and radiogenomics in assisting in the diagnosis of cancer disease and determining cancer aggressiveness. This review discusses how radiogenomic analysis can be further used to guide treatment therapy for individual tumours by predicting drug response and potential therapy resistance and examines its role in developing radiomics as biomarkers of oncological outcomes. Lastly, it provides an overview of the obstacles in these emergent fields today including reproducibility, need for validation, imaging analysis standardisation, data sharing and clinical translatability and offers potential solutions to these challenges towards the realisation of precision oncology.
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Affiliation(s)
- E Sala
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - E Mema
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology, New York Presbyterian/Columbia University Medical Center, 622 W 168th St., New York, NY 10032, USA
| | - Y Himoto
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - H Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - J D Brenton
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - A Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - B Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - H A Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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50
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Abstract
Solid tumors are multiscale, open, complex, dynamic systems: complex because they have many interacting components, dynamic because both the components and their interactions can change with time, and open because the tumor freely communicates with surrounding and even distant host tissue. Thus, it is not surprising that striking intratumoral variations are commonly observed in clinical imaging such as MRI and CT and that several recent studies found striking regional variations in the molecular properties of cancer cells from the same tumor. Interestingly, this spatial heterogeneity in molecular properties of tumor cells is typically ascribed to branching clonal evolution due to accumulating mutations while macroscopic variations observed in, for example, clinical MRI scans are usually viewed as functions of blood flow. The clinical significance of spatial heterogeneity has not been fully determined but there is a general consensus that the varying intratumoral landscape along with patient factors such as age, morbidity and lifestyle, contributes significantly to the often unpredictable response of individual patients within a disease cohort treated with the same standard-of-care therapy.Here we investigate the potential link between macroscopic tumor heterogeneity observed by clinical imaging and spatial variations in the observed molecular properties of cancer cells. We build on techniques developed in landscape ecology to link regional variations in the distribution of species with local environmental conditions that define their habitat. That is, we view each region of the tumor as a local ecosystem consisting of environmental conditions such as access to nutrients, oxygen, and means of waste clearance related to blood flow and the local population of tumor cells that both adapt to these conditions and, to some extent, change them through, for example, production of angiogenic factors. Furthermore, interactions among neighboring habitats can produce broader regional dynamics so that the internal diversity of tumors is the net result of complex multiscale somatic Darwinian interactions.Methods in landscape ecology harness Darwinian dynamics to link the environmental properties of a given region to the local populations which are assumed to represent maximally fit phenotypes within those conditions. Consider a common task of a landscape ecologist: defining the spatial distribution of species in a large region, e.g., in a satellite image. Clearly the most accurate approach requires a meter by meter survey of the multiple square kilometers in the region of interest. However, this is both impractical and potentially destructive. Instead, landscape ecology breaks the task into component parts relying on the Darwinian interdependence of environmental properties and fitness of specific species' phenotypic and genotypic properties. First, the satellite map is carefully analyzed to define the number and distribution of habitats. Then the species distribution in a representative sampling of each habitat is empirically determined. Ultimately, this permits sufficient bridging of spatial scales to accurately predict spatial distribution of plant and animal species within large regions.Currently, identifying intratumoral subpopulations requires detailed histological and molecular studies that are expensive and time consuming. Furthermore, this method is subject to sampling bias, is invasive for vital organs such as the brain, and inherently destructive precluding repeated assessments for monitoring post-treatment response and proteogenomic evolution. In contrast, modern cross-sectional imaging can interrogate the entire tumor noninvasively, allowing repeated analysis without disrupting the region of interest. In particular, magnetic resonance imaging (MRI) provides exceptional spatial resolution and generates signals that are unique to the molecular constituents of tissue. Here we propose that MRI scans may be the equivalent of satellite images in landscape ecology and, with appropriate application of Darwinian first principles and sophisticated image analytic methods, can be used to estimate regional variations in the molecular properties of cancer cells.We have initially examined this technique in glioblastoma, a malignant brain neoplasm which is morphologically complex and notorious for a fast progression from diagnosis to recurrence and death, making a suitable subject of noninvasive, rapidly repeated assessment of intratumoral evolution. Quantitative imaging analysis of routine clinical MRIs from glioblastoma has identified macroscopic morphologic characteristics which correlate with proteogenomics and prognosis. The key to the accurate detection and forecasting of intratumoral evolution using quantitative imaging analysis is likely to be in the understanding of the synergistic interactions between observable intratumoral subregions and the resulting tumor behavior.
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Affiliation(s)
- Joo Yeun Kim
- Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center, Tampa, FL, 33612, USA
- Department of Integrative Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Robert A Gatenby
- Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center, Tampa, FL, 33612, USA.
- Department of Integrative Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, 33612, USA.
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