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Lin D, Liu J, Ke C, Chen H, Li J, Xie Y, Ma J, Lv X, Feng Y. Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas. Clin Neuroradiol 2024:10.1007/s00062-024-01421-3. [PMID: 38858272 DOI: 10.1007/s00062-024-01421-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/03/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. METHODS Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. RESULTS Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). CONCLUSION Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.
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Affiliation(s)
- Danlin Lin
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jiehong Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chao Ke
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haolin Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
- Guangdong-Hong Kong-Macao Greater Bay Area Centre for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Guangzhou, China.
- Department of Radiology, The First People's Hospital of Shunde, Southern Medical University, Foshan, China.
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Caii W, Wu X, Guo K, Chen Y, Shi Y, Chen J. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. Cancer Immunol Immunother 2024; 73:153. [PMID: 38833187 DOI: 10.1007/s00262-024-03724-3] [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: 03/19/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). METHODS Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. RESULTS The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. CONCLUSION The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.
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Affiliation(s)
- Weimin Caii
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China
| | - Xiao Wu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yongxian Chen
- Department of Chest Cancer, Xiamen Second People's Hospital, Xiamen, 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou, 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China.
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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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Ghimire P, Kinnersley B, Karami G, Arumugam P, Houlston R, Ashkan K, Modat M, Booth TC. Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies. Neurooncol Adv 2024; 6:vdae055. [PMID: 38680991 PMCID: PMC11046988 DOI: 10.1093/noajnl/vdae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024] Open
Abstract
Background Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. Methods A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Conclusions Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.
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Affiliation(s)
- Prajwal Ghimire
- Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Ben Kinnersley
- Department of Oncology, University College London, London, UK
| | | | | | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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Rathore S, Iftikhar MA, Chaddad A, Singh A, Gillani Z, Abdulkadir A. Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107812. [PMID: 37757566 DOI: 10.1016/j.cmpb.2023.107812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 05/14/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy. PURPOSE To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients. MATERIALS AND METHODS Multi-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983-2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS. RESULTS The average age of patients was 51.2years (women: n = 77, age-range=18-84years; men: n = 83, age-range=21-80years). The median OS of the participants was 494.5 (range,3-4752), 481 (range,7-4752), and 524.5 days (range,3-2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87). CONCLUSION The combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.
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Affiliation(s)
- Saima Rathore
- AVID Radiopharmaceuticals, Philadelphia, PA, USA; Eli Lilly and Company, Indianapolis, IN, USA.
| | | | - Ahmad Chaddad
- School of Artificial Intelligence, GUET, Guilin, China
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Zeeshan Gillani
- Comsats University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Ahmed Abdulkadir
- Center for Research in Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Artificial Intelligence, Zurich University of Applied Sciences, Winterthur, ZH, Switzerland
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Wang S, Liu X, Wu Y, Jiang C, Luo Y, Tang X, Wang R, Zhang X, Gong J. Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study. Front Oncol 2023; 13:1252074. [PMID: 37954078 PMCID: PMC10637586 DOI: 10.3389/fonc.2023.1252074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/13/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Lymphovascular space invasion (LVSI) is associated with lymph node metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential of radiomics, derived from magnetic resonance (MR) images using habitat analysis, as a non-invasive surrogate biomarker for predicting LVSI in cervical cancer. Methods This retrospective study included 300 patients with cervical cancer who underwent surgical treatment at two centres (centre 1 = 198 and centre 2 = 102). Using the k-means clustering method, contrast-enhanced T1-weighted imaging (CE-T1WI) images were segmented based on voxel and entropy values, creating sub-regions within the volume ofinterest. Radiomics features were extracted from these sub-regions. Pearson correlation coefficient and least absolute shrinkage and selection operator LASSO) regression methods were used to select features associated with LVSI in cervical cancer. Support vector machine (SVM) model was developed based on the radiomics features extracted from each sub-region in the training cohort. Results The voxels and entropy values of the CE-T1WI images were clustered into three sub-regions. In the training cohort, the AUCs of the SVM models based on radiomics features derived from the whole tumour, habitat 1, habitat 2, and habitat 3 models were 0.805 (95% confidence interval [CI]: 0.745-0.864), 0.873(95% CI: 0.824-0.922), 0.869 (95% CI: 0.821-0.917), and 0.870 (95% CI: 0.821-0.920), respectively. Compared with whole tumour model, the predictive performances of habitat 3 model was the highest in the external test cohort (0.780 [95% CI: 0.692-0.869]). Conclusions The radiomics model based on the tumour sub-regional habitat demonstrated superior predictive performance for an LVSI in cervical cancer than that of radiomics model derived from the whole tumour.
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Affiliation(s)
- Shuxing Wang
- The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Xiaowen Liu
- The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Yu Wu
- Department of Radiology, Guangzhou Women and Children’s Medical Center, Guangzhou, China
| | - Changsi Jiang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Yan Luo
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Xue Tang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Rui Wang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Xiaochun Zhang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Jingshan Gong
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
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Ocaña-Tienda B, Pérez-Beteta J, Villanueva-García JD, Romero-Rosales JA, Molina-García D, Suter Y, Asenjo B, Albillo D, Ortiz de Mendivil A, Pérez-Romasanta LA, González-Del Portillo E, Llorente M, Carballo N, Nagib-Raya F, Vidal-Denis M, Luque B, Reyes M, Arana E, Pérez-García VM. A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data. Sci Data 2023; 10:208. [PMID: 37059722 PMCID: PMC10104872 DOI: 10.1038/s41597-023-02123-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 03/30/2023] [Indexed: 04/16/2023] Open
Abstract
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | | | - José A Romero-Rosales
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Yannick Suter
- Medical Image Analysis Group, ARTORG Research Center, Bern, Switzerland
| | - Beatriz Asenjo
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - David Albillo
- Radiology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | | | | | - Manuel Llorente
- Radiology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | - Fátima Nagib-Raya
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Maria Vidal-Denis
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Mauricio Reyes
- Medical Image Analysis Group, ARTORG Research Center, Bern, Switzerland
| | - Estanislao Arana
- Radiology Department, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
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Chiu FY, Yen Y. Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 2023; 11:35. [PMID: 36991494 DOI: 10.1186/s40364-023-00476-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.
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Affiliation(s)
- Fang-Ying Chiu
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Center for Brain and Neurobiology Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Teaching and Research Headquarters for Sustainable Development Goals, Tzu Chi University, Hualien City, 970374, Taiwan.
| | - Yun Yen
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Ph.D. Program for Cancer Biology and Drug Discovery, Taipei Medical University, Taipei City, 110301, Taiwan.
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei City, 110301, Taiwan.
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei City, 110301, Taiwan.
- Cancer Center, Taipei Municipal WanFang Hospital, Taipei City, 116081, Taiwan.
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Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas. Eur Radiol 2023; 33:3455-3466. [PMID: 36853347 DOI: 10.1007/s00330-023-09459-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/21/2022] [Accepted: 01/20/2023] [Indexed: 03/01/2023]
Abstract
OBJECTIVES To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. METHODS We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. RESULTS We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7 years; range, 19-77 years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes. CONCLUSIONS Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas. KEY POINTS • The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.
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Wang X, Dai Y, Lin H, Cheng J, Zhang Y, Cao M, Zhou Y. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol 2023; 33:3312-3321. [PMID: 36738323 DOI: 10.1007/s00330-023-09412-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/21/2022] [Accepted: 12/29/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model. METHODS Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation. RESULTS Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set. CONCLUSION The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas. KEY POINTS • Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.
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Affiliation(s)
- Xiaoqing Wang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Hai Lin
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jiahui Cheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yiming Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mengqiu Cao
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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11
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Long H, Zhang P, Bi Y, Yang C, Wu M, He D, Huang S, Yang K, Qi S, Wang J. MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme. Front Oncol 2023; 12:1042498. [PMID: 36686829 PMCID: PMC9845721 DOI: 10.3389/fonc.2022.1042498] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 01/05/2023] Open
Abstract
Background and purpose As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management. Materials and methods The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. Results Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. Conclusions Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.
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Affiliation(s)
- Hao Long
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Ping Zhang
- Department of oncology, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Yuewei Bi
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Chen Yang
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Manfeng Wu
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Dian He
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Shaozhuo Huang
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Kaijun Yang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Songtao Qi
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China,*Correspondence: Jun Wang,
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A Head-to-Head Comparison of 18F-Fluorocholine PET/CT and Conventional MRI as Predictors of Outcome in IDH Wild-Type High-Grade Gliomas. J Clin Med 2022; 11:jcm11206065. [PMID: 36294385 PMCID: PMC9605635 DOI: 10.3390/jcm11206065] [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: 08/22/2022] [Revised: 10/01/2022] [Accepted: 10/06/2022] [Indexed: 01/24/2023] Open
Abstract
(1) Aim: To study the associations between imaging parameters derived from contrast-enhanced MRI (CE-MRI) and 18F-fluorocholine PET/CT and their performance as prognostic predictors in isocitrate dehydrogenase wild-type (IDH-wt) high-grade gliomas. (2) Methods: A prospective, multicenter study (FuMeGA: Functional and Metabolic Glioma Analysis) including patients with baseline CE-MRI and 18F-fluorocholine PET/CT and IDH wild-type high-grade gliomas. Clinical variables such as performance status, extent of surgery and adjuvant treatments (Stupp protocol vs others) were obtained and used to discriminate overall survival (OS) and progression-free survival (PFS) as end points. Multilesionality was assessed on the visual analysis of PET/CT and CE-MRI images. After tumor segmentation, standardized uptake value (SUV)-based variables for PET/CT and volume-based and geometrical variables for PET/CT and CE-MRI were calculated. The relationships among imaging techniques variables and their association with prognosis were evaluated using Pearson’s chi-square test and the t-test. Receiver operator characteristic, Kaplan−Meier and Cox regression were used for the survival analysis. (3) Results: 54 patients were assessed. The median PFS and OS were 5 and 11 months, respectively. Significant strong relationships between volume-dependent variables obtained from PET/CT and CE-MRI were found (r > 0.750, p < 0.05). For OS, significant associations were found with SUVmax, SUVpeak, SUVmean and sphericity (HR: 1.17, p = 0.035; HR: 1.24, p = 0.042; HR: 1.62, p = 0.040 and HR: 0.8, p = 0.022, respectively). Among clinical variables, only Stupp protocol and age showed significant associations with OS and PFS. No CE-MRI derived variables showed significant association with prognosis. In multivariate analysis, age (HR: 1.04, p = 0.002), Stupp protocol (HR: 2.81, p = 0.001), multilesionality (HR: 2.20, p = 0.013) and sphericity (HR: 0.79, p = 0.027) derived from PET/CT showed independent associations with OS. For PFS, only age (HR: 1.03, p = 0.021) and treatment protocol (HR: 2.20, p = 0.008) were significant predictors. (4) Conclusions: 18F-fluorocholine PET/CT metabolic and radiomic variables were robust prognostic predictors in patients with IDH-wt high-grade gliomas, outperforming CE-MRI derived variables.
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Spatial heterogeneity of edema region uncovers survival-relevant habitat of Glioblastoma. Eur J Radiol 2022; 154:110423. [DOI: 10.1016/j.ejrad.2022.110423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/16/2022] [Accepted: 06/20/2022] [Indexed: 11/18/2022]
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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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Ismail M, Prasanna P, Bera K, Statsevych V, Hill V, Singh G, Partovi S, Beig N, McGarry S, Laviolette P, Ahluwalia M, Madabhushi A, Tiwari P. Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1764-1777. [PMID: 35108202 PMCID: PMC9575333 DOI: 10.1109/tmi.2022.3148780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.
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RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas. Cancers (Basel) 2022; 14:cancers14122818. [PMID: 35740484 PMCID: PMC9220978 DOI: 10.3390/cancers14122818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/29/2022] [Accepted: 06/01/2022] [Indexed: 12/07/2022] Open
Abstract
Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.
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Park H, Lee SY, Lee J, Pak J, Lee K, Lee SE, Jung JY. Detecting Multiple Myeloma Infiltration of the Bone Marrow on CT Scans in Patients with Osteopenia: Feasibility of Radiomics Analysis. Diagnostics (Basel) 2022; 12:diagnostics12040923. [PMID: 35453971 PMCID: PMC9025143 DOI: 10.3390/diagnostics12040923] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/30/2022] [Accepted: 04/04/2022] [Indexed: 02/04/2023] Open
Abstract
It is difficult to detect multiple myeloma (MM) infiltration of the bone marrow on computed tomography (CT) scans of patients with osteopenia. Our aim is to determine the feasibility of using radiomics analysis to detect MM infiltration of the bone marrow on CT scans of patients with osteopenia. The contrast-enhanced thoracic CT scans of 104 patients with MM and 104 age- and sex-matched controls were retrospectively evaluated. All individuals had decreased bone density on radiography. The study group was divided into development (n = 160) and temporal validation sets (n = 48). The radiomics model was developed using 805 texture features extracted from the bone marrow for a development set, using a Random Forest algorithm. The developed models were applied to evaluate a temporal validation set. For comparison, three radiologists evaluated the CTs for the possibility of MM infiltration in the bone marrow. The diagnostic performances were assessed and compared using an area under the receiver operating characteristic curve (AUC) analysis. The AUC of the radiomics model was not significantly different from those of the radiologists (p = 0.056–0.821). The radiomics analysis results showed potential for detecting MM infiltration in the bone marrow on CT scans of patients with osteopenia.
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Affiliation(s)
- Hyerim Park
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
- Department of Radiology, Soonchunhyang University Cheoan Hospital, Cheonan 31151, Korea;
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
- Correspondence: ; Tel.: +82-2-2258-6743
| | - Jooyeon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
- Department of Applied Statistics, Hanyang University, Seoul 04763, Korea
| | - Juyoung Pak
- Department of Radiology, Soonchunhyang University Cheoan Hospital, Cheonan 31151, Korea;
| | - Koeun Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
| | - Seung-Eun Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
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Cho HH, Kim H, Nam SY, Lee JE, Han BK, Ko EY, Choi JS, Park H, Ko ES. Measurement of Perfusion Heterogeneity within Tumor Habitats on Magnetic Resonance Imaging and Its Association with Prognosis in Breast Cancer Patients. Cancers (Basel) 2022; 14:cancers14081858. [PMID: 35454768 PMCID: PMC9025287 DOI: 10.3390/cancers14081858] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary A habitat analysis reflects intratumoral heterogeneity more accurately than does a whole-tumor analysis. Perfusional heterogeneity using a habitat analysis is a rarely explored option and can affect patient outcomes. From two hospitals, 308 and 147 patients with invasive breast cancer who underwent preoperative MRI were included as development and validation cohorts, respectively. In our study, five habitats with distinct perfusion patterns were identified based on early and delayed phases of dynamic contrast material-enhanced MR images. A habitat risk score (HRS) was an independent risk factor for predicting worse disease-free survival outcomes in the HRS-only risk model (hazard ratio = 3.274 [95% CI = 1.378–7.782]; p = 0.014) and combined habitat risk model (hazard ratio = 4.128 [95% CI = 1.744–9.769]; p = 0.003) in the validation cohort. Abstract The purpose of this study was to identify perfusional subregions sharing similar kinetic characteristics from dynamic contrast-enhanced magnetic resonance imaging (MRI) using data-driven clustering, and to evaluate the effect of perfusional heterogeneity based on those subregions on patients’ survival outcomes in various risk models. From two hospitals, 308 and 147 women with invasive breast cancer who underwent preoperative MRI between October 2011 and July 2012 were retrospectively enrolled as development and validation cohorts, respectively. Using the Cox-least absolute shrinkage and selection operator model, a habitat risk score (HRS) was constructed from the radiomics features from the derived habitat map. An HRS-only, clinical, combined habitat, and two conventional radiomics risk models to predict patients’ disease-free survival (DFS) were built. Patients were classified into low-risk or high-risk groups using the median cutoff values of each risk score. Five habitats with distinct perfusion patterns were identified. An HRS was an independent risk factor for predicting worse DFS outcomes in the HRS-only risk model (hazard ratio = 3.274 [95% CI = 1.378–7.782]; p = 0.014) and combined habitat risk model (hazard ratio = 4.128 [95% CI = 1.744–9.769]; p = 0.003) in the validation cohort. In the validation cohort, the combined habitat risk model (hazard ratio = 4.128, p = 0.003, C-index = 0.760) showed the best performance among five different risk models. The quantification of perfusion heterogeneity is a potential approach for predicting prognosis and may facilitate personalized, tailored treatment strategies for breast cancer.
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Affiliation(s)
- Hwan-ho Cho
- Department of Medical Artificial Intelligence, Konyang University, Daejon 32992, Korea;
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Sungkyunkwan University, Suwon 16419, Korea
| | - Haejung Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.K.); (B.-K.H.); (E.Y.K.); (J.S.C.)
| | - Sang Yu Nam
- Department of Radiology, Gil Hospital, Gachon University of Medicine and Science, Incheon 21565, Korea;
| | - Jeong Eon Lee
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Boo-Kyung Han
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.K.); (B.-K.H.); (E.Y.K.); (J.S.C.)
| | - Eun Young Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.K.); (B.-K.H.); (E.Y.K.); (J.S.C.)
| | - Ji Soo Choi
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.K.); (B.-K.H.); (E.Y.K.); (J.S.C.)
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Sungkyunkwan University, Suwon 16419, Korea
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea
- Correspondence: (H.P.); (E.S.K.)
| | - Eun Sook Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.K.); (B.-K.H.); (E.Y.K.); (J.S.C.)
- Correspondence: (H.P.); (E.S.K.)
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Cho HH, Kim CK, Park H. Overview of radiomics in prostate imaging and future directions. Br J Radiol 2022; 95:20210539. [PMID: 34797688 PMCID: PMC8978251 DOI: 10.1259/bjr.20210539] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.
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Affiliation(s)
- Hwan-Ho Cho
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
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Shi J, Cui L, Wang H, Dong Y, Yu T, Yang H, Wang X, Liu G, Jiang W, Luo Y, Yang Z, Jiang X. MRI-based intratumoral and peritumoral radiomics on prediction of lymph-vascular space invasion in cervical cancer: A multi-center study. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103373] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Jian A, Jang K, Russo C, Liu S, Di Ieva A. Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:183-193. [PMID: 34862542 DOI: 10.1007/978-3-030-85292-4_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.
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Affiliation(s)
- Anne Jian
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Kevin Jang
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Centre for Health Informatics, Macquarie University, Sydney, NSW, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
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Fan Y, Dong Y, Yang H, Chen H, Yu Y, Wang X, Wang X, Yu T, Luo Y, Jiang X. Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer. Phys Med Biol 2021; 66. [PMID: 34633298 DOI: 10.1088/1361-6560/ac2ea7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/11/2021] [Indexed: 01/20/2023]
Abstract
The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.
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Affiliation(s)
- Ying Fan
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Huazhe Yang
- Department of Biophysics, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China
| | - Yalian Yu
- Department of Otorhinolaryngology, the First Affiliated Hospital of China Medical University, Shenyang, 110122, People's Republic of China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xinling Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
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MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme. Magn Reson Imaging 2021; 85:222-227. [PMID: 34687850 DOI: 10.1016/j.mri.2021.10.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/16/2021] [Accepted: 10/17/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE Our current study aims to consider the image biomarkers extracted from the MRI images for exploring their effects on glioblastoma multiforme (GBM) patients' survival. Determining its biomarker helps better manage the disease and evaluate treatments. It has been proven that imaging features could be used as a biomarker. The purpose of this study is to investigate the features in MRI and clinical features as the biomarker association of survival of GBM. METHODS 55 patients were considered with five clinical features, 10 qualities pre-operative MRI image features, and six quantitative features obtained using BraTumIA software. It was run ANN, C5, Bayesian, and Cox models in two phases for determining important variables. In the first phase, we selected the quality features that occur at least in three models and quantitative in two models. In the second phase, models were run with the extracted features, and then the probability value of variables in each model was calculated. RESULTS The mean of accuracy, sensitivity, specificity, and area under curve (AUC) after running four machine learning techniques were 80.47, 82.54, 79.78, and 0.85, respectively. In the second step, the mean of accuracy, sensitivity, specificity, and AUC were 79.55, 78.71, 79.83, and 0.87, respectively. CONCLUSION We found the largest size of the width, the largest size of length, radiotherapy, volume of enhancement, volume of nCET, satellites, enhancing margin, and age feature are important features.
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Huang SG, Qian XS, Cheng Y, Guo WL, Zhou ZY, Dai YK. Machine learning-based quantitative analysis of barium enema and clinical features for early diagnosis of short-segment Hirschsprung disease in neonate. J Pediatr Surg 2021; 56:1711-1717. [PMID: 34120738 DOI: 10.1016/j.jpedsurg.2021.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 05/12/2021] [Accepted: 05/12/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To develop a mathematical model based on a combination of clinical and radiologic features (barium enema) for early diagnosis of short-segment Hirschsprung disease (SHSCR) in neonate. METHODS The analysis included 54 neonates with biopsy-confirmed SHSCR (the cases) and 59 neonates undergoing barium enema for abdominal symptoms but no Hirschsprung disease (the control). Colon shape features extracted from barium enema images and clinical features were used to develop diagnostic models using support vector machine (SVM) and L2-regularized logistic regression (LR). The training cohort included 32 cases and 37 controls; testing cohort consisted 22 cases and 22 controls. Results were compared to interpretation by 2 radiologists. RESULTS In the analysis by radiologists, 87 out of 113 cases were correctly classified. Six SHSCR cases were mis-classified into the non-HSCR group. In the remaining 20 cases, radiologists were unable to make a decision. Both the SVM and LR classifiers contained five clinical features and four shape features. The performance of the two classifiers was similar. The best model had 86.36% accuracy, 81.82% sensitivity, and 90.91% specificity. The AUC was 0.9132 for the best-performing SVM classifier and 0.9318 for the best-performing LR classifier. CONCLUSION A combination of clinical features and colon shape features extracted from barium enemas can be used to improve early diagnosis of SHSCR in neonate.
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Affiliation(s)
- Shun-Gen Huang
- Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025 China
| | - Xu-Sheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 215163 Suzhou, China
| | - Yuan Cheng
- Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025 China
| | - Wan-Liang Guo
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025 China
| | - Zhi-Yong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 China
| | - Ya-Kang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 China.
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Dapash M, Castro B, Hou D, Lee-Chang C. Current Immunotherapeutic Strategies for the Treatment of Glioblastoma. Cancers (Basel) 2021; 13:4548. [PMID: 34572775 PMCID: PMC8467991 DOI: 10.3390/cancers13184548] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/24/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022] Open
Abstract
Glioblastoma (GBM) is a lethal primary brain tumor. Despite extensive effort in basic, translational, and clinical research, the treatment outcomes for patients with GBM are virtually unchanged over the past 15 years. GBM is one of the most immunologically "cold" tumors, in which cytotoxic T-cell infiltration is minimal, and myeloid infiltration predominates. This is due to the profound immunosuppressive nature of GBM, a tumor microenvironment that is metabolically challenging for immune cells, and the low mutational burden of GBMs. Together, these GBM characteristics contribute to the poor results obtained from immunotherapy. However, as indicated by an ongoing and expanding number of clinical trials, and despite the mostly disappointing results to date, immunotherapy remains a conceptually attractive approach for treating GBM. Checkpoint inhibitors, various vaccination strategies, and CAR T-cell therapy serve as some of the most investigated immunotherapeutic strategies. This review article aims to provide a general overview of the current state of glioblastoma immunotherapy. Information was compiled through a literature search conducted on PubMed and clinical trials between 1961 to 2021.
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Affiliation(s)
- Mark Dapash
- Pritzker School of Medicine, University of Chicago, Chicago, IL 60637, USA;
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
| | - Brandyn Castro
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
- Department of Neurosurgery, University of Chicago, Chicago, IL 60637, USA
| | - David Hou
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
| | - Catalina Lee-Chang
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
- Northwestern Medicine Malnati Brain Tumor Institute, Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Lin M, Wynne JF, Zhou B, Wang T, Lei Y, Curran WJ, Liu T, Yang X. Artificial intelligence in tumor subregion analysis based on medical imaging: A review. J Appl Clin Med Phys 2021; 22:10-26. [PMID: 34164913 PMCID: PMC8292694 DOI: 10.1002/acm2.13321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/17/2021] [Accepted: 05/22/2021] [Indexed: 12/20/2022] Open
Abstract
Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed.
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Affiliation(s)
- Mingquan Lin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jacob F. Wynne
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Boran Zhou
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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Rathore S, Chaddad A, Iftikhar MA, Bilello M, Abdulkadir A. Combining MRI and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma. Radiol Imaging Cancer 2021; 3:e200108. [PMID: 34296969 DOI: 10.1148/rycan.2021200108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Purpose To test the hypothesis that combined features from MR and digital histopathologic images more accurately predict overall survival (OS) in patients with glioma compared with MRI or histopathologic features alone. Materials and Methods Multiparametric MR and histopathologic images in patients with a diagnosis of glioma (high- or low-grade glioma [HGG or LGG]) were obtained from The Cancer Imaging Archive (original images acquired 1983-2008). An extensive set of engineered features such as intensity, histogram, and texture were extracted from delineated tumor regions in MR and histopathologic images. Cox proportional hazard regression and support vector machine classification (SVC) models were applied to (a) MRI features only (MRIcox/svc), histopathologic features only (HistoPathcox/svc), and (c) combined MRI and histopathologic features (MRI+HistoPathcox/svc) and evaluated in a split train-test configuration. Results A total of 171 patients (mean age, 51 years ± 15; 91 men) were included with HGG (n = 75) and LGG (n = 96). Median OS was 467 days (range, 3-4752 days) for all patients, 350 days (range, 15-1561 days) for HGG, and 595 days (range, 3-4752 days) for LGG. The MRI+HistoPathcox model demonstrated higher concordance index (C-index) compared with MRIcox and HistoPathcox models on all patients (C-index, 0.79 vs 0.70 [P = .02; MRIcox] and 0.67 [P = .01; HistoPathcox]), patients with HGG (C-index, 0.78 vs 0.68 [P = .03; MRIcox] and 0.64 [P = .01; HistoPathcox]), and patients with LGG (C-index, 0.88 vs 0.62 [P = .008; MRIcox] and 0.62 [P = .006; HistoPathcox]). In binary classification, the MRI+HistoPathsvc model (area under the receiver operating characteristic curve [AUC], 0.86 [95% CI: 0.80, 0.95]) had higher performance than the MRIsvc model (AUC, 0.68 [95% CI: 0.50, 0.81]; P = .01) and the HistoPathsvc model (AUC, 0.72 [95% CI: 0.60, 0.85]; P = .04). Conclusion The model combining features from MR and histopathologic images had higher accuracy in predicting OS compared with the models with MR or histopathologic images alone. Keywords: Survival Prediction, Gliomas, Digital Pathology Imaging, MR Imaging, Machine Learning Supplemental material is available for this article.
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Affiliation(s)
- Saima Rathore
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Ahmad Chaddad
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Muhammad A Iftikhar
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Michel Bilello
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Ahmed Abdulkadir
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
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Zhang H, Xu L, Zhong Z, Liu Y, Long Y, Zhou S. Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features. Acad Radiol 2021; 28:e199-e208. [PMID: 32241714 DOI: 10.1016/j.acra.2020.02.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/20/2020] [Accepted: 02/20/2020] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES To explore associations between MR imaging features, DNA methylation subtyping, and survival in lower-grade gliomas (LGG). MATERIALS AND METHODS The MR data from 170 patients generated with the Cancer Imaging Archive were reviewed. The correlation was evaluated by Fisher's Exact Test, Pearson Chi-Square and binary regression analysis. Survival analysis was conducted by using time-dependent ROC analysis and the Kaplan-Meier method (the worst prognosis subgroup). RESULTS Identified were 9 (5.3%) M1-subtype, 18 (10.6%) M2-subtype, 48 (28.2%) M3-subtype, 31 (18.2%) M4-subtype and 64 (37.6%) M5-subtype. Patients with M4-subtype had the shortest median OS (49.3 vs. 28.4) months(p < 0.05). The time-dependent ROC for the M4-subtype was 0.83 (95% confidence interval 0.72-0.95) for survival at 12 months, 0.82 (95% confidence interval 0.70-0.94) for survival at 24 months, and 0.74 (95% confidence interval 0.62-0.86) for survival at 36 months. After uni- and multivariate analysis, a nomogram was built based on proportion contrast-enhanced (CE) tumor, extranodular growth, volume_cutoff_median, and location. For the prediction of M4-subtype, the nomogram showed good discrimination, with an area under the curve (AUC) of 0.886 (95% CI: 0.820-952) and was well calibrated. On multivariate logistic regression analysis, volume ≥60cm3 (OR: 0.200; p < 0.001; 95%CI: 0.048-0.834) was associated with M1-subtype (AUC: 0.690). Hemorrhage (OR: 5.443; p = 0.002; 95%CI: 1.844-16.069) and volume > median (OR: 3.256; p = 0.05; 95%CI: 0.992-10.686) were associated with M2-subtype (AUC: 0.733). Proportion CE tumor<=5% (OR: 3.968; P=0.002; 95%CI: 1.634-9.635) was associated with M3-subtype (AUC: 0.632). Poorly-defined (OR: 2.258; p = 0.05; 95%CI: 1.000-5.101) and volume > median (OR: 2.447; p = 0.01; 95%CI: 1.244-4.813) were associated with M5-subtype (AUC: 0.645). Decision curve analysis indicated predictions for all models were clinically useful. CONCLUSION This preliminary radiogenomics analysis of lower-grade gliomas demonstrated associations between MR features and DNA methylation subtyping. The shortest survival was observed in patients with M4-subtype. And we have constructed nomogram that enables more accurate predictions of M4-subtype.
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A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning. J Clin Med 2021; 10:jcm10092030. [PMID: 34068528 PMCID: PMC8126019 DOI: 10.3390/jcm10092030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/29/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022] Open
Abstract
Glioblastoma multiforme (GBM) carries a poor prognosis and usually presents with heterogenous regions of a necrotic core, solid part, peritumoral tissue, and peritumoral edema. Accurate demarcation on magnetic resonance imaging (MRI) between the active tumor region and perifocal edematous extension is essential for planning stereotactic biopsy, GBM resection, and radiotherapy. We established a set of radiomics features to efficiently classify patients with GBM and retrieved cerebral multiparametric MRI, including contrast-enhanced T1-weighted (T1-CE), T2-weighted, T2-weighted fluid-attenuated inversion recovery, and apparent diffusion coefficient images from local patients with GBM. A total of 1316 features on the raw MR images were selected for each annotated area. A leave-one-out cross-validation was performed on the whole dataset, the different machine learning and deep learning techniques tested; random forest achieved the best performance (average accuracy: 93.6% necrosis, 90.4% solid part, 95.8% peritumoral tissue, and 90.4% peritumoral edema). The features from the enhancing tumor and the tumor shape elongation of peritumoral edema region for high-risk groups from T1-CE. The multiparametric MRI-based radiomics model showed the efficient classification of tumor subregions of GBM and suggests that prognostic radiomic features from a routine MRI exam may also be significantly associated with key biological processes that affect the response to chemotherapy in GBM.
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Vils A, Bogowicz M, Tanadini-Lang S, Vuong D, Saltybaeva N, Kraft J, Wirsching HG, Gramatzki D, Wick W, Rushing E, Reifenberger G, Guckenberger M, Weller M, Andratschke N. Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial. Front Oncol 2021; 11:636672. [PMID: 33937035 PMCID: PMC8079773 DOI: 10.3389/fonc.2021.636672] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/17/2021] [Indexed: 12/21/2022] Open
Abstract
Background Based on promising results from radiomic approaches to predict O6-methylguanine DNA methyltransferase promoter methylation status (MGMT status) and clinical outcome in patients with newly diagnosed glioblastoma, the current study aimed to evaluate radiomics in recurrent glioblastoma patients. Methods Pre-treatment MR-imaging data of 69 patients enrolled into the DIRECTOR trial in recurrent glioblastoma served as a training cohort, and 49 independent patients formed an external validation cohort. Contrast-enhancing tumor and peritumoral volumes were segmented on MR images. 180 radiomic features were extracted after application of two MR intensity normalization techniques: fixed number of bins and linear rescaling. Radiomic feature selection was performed via principal component analysis, and multivariable models were trained to predict MGMT status, progression-free survival from first salvage therapy, referred to herein as PFS2, and overall survival (OS). The prognostic power of models was quantified with concordance index (CI) for survival data and area under receiver operating characteristic curve (AUC) for the MGMT status. Results We established and validated a radiomic model to predict MGMT status using linear intensity interpolation and considering features extracted from gadolinium-enhanced T1-weighted MRI (training AUC = 0.670, validation AUC = 0.673). Additionally, models predicting PFS2 and OS were found for the training cohort but were not confirmed in our validation cohort. Conclusions A radiomic model for prediction of MGMT promoter methylation status from tumor texture features in patients with recurrent glioblastoma was successfully established, providing a non-invasive approach to anticipate patient's response to chemotherapy if biopsy cannot be performed. The radiomic approach to predict PFS2 and OS failed.
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Affiliation(s)
- Alex Vils
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | | | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Natalia Saltybaeva
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Johannes Kraft
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | | | - Dorothee Gramatzki
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Wolfgang Wick
- Neurology Clinic, University Heidelberg Medical School, Heidelberg, Germany
| | - Elisabeth Rushing
- Department of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Guido Reifenberger
- Department of Neuropathology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Michael Weller
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
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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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Xu Y, He X, Li Y, Pang P, Shu Z, Gong X. The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study. J Magn Reson Imaging 2021; 54:571-583. [PMID: 33559302 DOI: 10.1002/jmri.27536] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/15/2021] [Accepted: 01/16/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique. A pragmatic and objective method in the clinic to assess OS is strongly in need. PURPOSE To construct a radiomics nomogram to stratify GBM patients into long- vs. short-term survival. STUDY TYPE Retrospective. POPULATION One-hundred and fifty-eight GBM patients from Brain Tumor Segmentation Challenge 2018 (BRATS2018) were for model construction and 32 GBM patients from the local hospital for external validation. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T MRI Scanners, T1 WI, T2 WI, T2 FLAIR, and contrast-enhanced T1 WI sequences ASSESSMENT: All patients were divided into long-term or short-term based on a survival of greater or fewer than 12 months. All BRATS2018 subjects were divided into training and test sets, and images were assessed for ependymal and pia mater involvement (EPI) and multifocality by three experienced neuroradiologists. All tumor tissues from multiparametric MRI were fully automatically segmented into three subregions to calculate the radiomic features. Based on the training set, the most powerful radiomic features were selected to constitute radiomic signature. STATISTICAL TESTS Receiver operating characteristic (ROC) curve, sensitivity, specificity, and the Hosmer-Lemeshow test. RESULTS The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set. The ROC curve showed the accuracy of the nomogram, radiomic signature, age, and EPI for external validation set were 0.858, 0.826, 0.664, and 0.66 in the validate set, respectively. DATA CONCLUSION Radiomics nomogram integrated with radiomic signature, EPI, and age was found to be robust for the stratification of GBM patients into long- vs. short-term survival. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Yuyun Xu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Xiaodong He
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yumei Li
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | | | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Xiangyang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
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Ratnam NM, Frederico SC, Gonzalez JA, Gilbert MR. Clinical correlates for immune checkpoint therapy: significance for CNS malignancies. Neurooncol Adv 2021; 3:vdaa161. [PMID: 33506203 PMCID: PMC7813206 DOI: 10.1093/noajnl/vdaa161] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized the field of cancer immunotherapy. Most commonly, inhibitors of PD-1 and CTLA4 are used having received approval for the treatment of many cancers like melanoma, non-small-cell lung carcinoma, and leukemia. In contrast, to date, clinical studies conducted in patients with CNS malignancies have not demonstrated promising results. However, patients with CNS malignancies have several underlying factors such as treatment with supportive medications like corticosteroids and cancer therapies including radiation and chemotherapy that may negatively impact response to ICIs. Although many clinical trials have been conducted with ICIs, measures that reproducibly and reliably indicate that treatment has evoked an effective immune response have not been fully developed. In this article, we will review the history of ICI therapy and the correlative biology that has been performed in the clinical trials testing these therapies in different cancers. It is our aim to help provide an overview of the assays that may be used to gauge immunologic response. This may be particularly germane for CNS tumors, where there is currently a great need for predictive biomarkers that will allow for the selection of patients with the highest likelihood of responding.
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Affiliation(s)
- Nivedita M Ratnam
- Neuro-Oncology Branch, CCR, NCI, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephen C Frederico
- Neuro-Oncology Branch, CCR, NCI, National Institutes of Health, Bethesda, Maryland, USA
| | - Javier A Gonzalez
- Neuro-Oncology Branch, CCR, NCI, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, CCR, NCI, National Institutes of Health, Bethesda, Maryland, USA
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Verma R, Correa R, Hill VB, Statsevych V, Bera K, Beig N, Mahammedi A, Madabhushi A, Ahluwalia M, Tiwari P. Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study. Radiol Artif Intell 2020; 2:e190168. [PMID: 33330847 DOI: 10.1148/ryai.2020190168] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 07/09/2020] [Accepted: 07/23/2020] [Indexed: 12/16/2022]
Abstract
Purpose To identify radiomic features extracted from the tumor habitat on routine MR images that are prognostic for progression-free survival (PFS) and to assess their morphologic basis with corresponding histopathologic attributes in glioblastoma (GBM). Materials and Methods In this retrospective study, 156 pretreatment GBM MR images (gadolinium-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery [FLAIR] images) were curated. Of these 156 images, 122 were used for training (90 from The Cancer Imaging Archive and 32 from the Cleveland Clinic, acquired between December 1, 2011, and May 1, 2018) and 34 were used for validation. The validation set was obtained from the Ivy Glioblastoma Atlas Project database, for which the percentage extent of 11 histologic attributes was available on corresponding histopathologic specimens of the resected tumor. Following expert annotations of the tumor habitat (necrotic core, enhancing tumor, and FLAIR-hyperintense subcompartments), 1008 radiomic descriptors (eg, Haralick texture features, Laws energy features, co-occurrence of local anisotropic gradient orientations [CoLIAGe]) were extracted from the three MRI sequences. The top radiomic features were obtained from each subcompartment in the training set on the basis of their ability to risk-stratify patients according to PFS. These features were then concatenated to create a radiomics risk score (RRS). The RRS was independently validated on a holdout set. In addition, correlations (P < .05) of RRS features were computed, with the percentage extent of the 11 histopathologic attributes, using Spearman correlation analysis. Results RRS yielded a concordance index of 0.80 on the validation set and constituted radiomic features, including Laws (capture edges, waves, ripple patterns) and CoLIAGe (capture disease heterogeneity) from enhancing tumor and FLAIR hyperintensity. These radiomic features were correlated with histopathologic attributes associated with disease aggressiveness in GBM, particularly tumor infiltration (P = .0044) and hyperplastic blood vessels (P = .0005). Conclusion Preliminary findings demonstrated significant associations of prognostic radiomic features with disease-specific histologic attributes, with implications for risk-stratifying patients with GBM for personalized treatment decisions. Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- Ruchika Verma
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Ramon Correa
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Virginia B Hill
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Volodymyr Statsevych
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Abdelkader Mahammedi
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Manmeet Ahluwalia
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH 44106 (R.V., R.C., K.B., N.B., A. Madabhushi, P.T.); Department of Neuroradiology, Feinberg School of Medicine, Northwestern University, Chicago, Ill (V.B.H.); Brain Tumor and Neuro-Oncology Center (V.S., M.A.), and Department of Diagnostic Radiology (A. Mahammedi), Cleveland Clinic, Cleveland, Ohio; and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A. Madabhushi)
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Shaikh F, Dupont-Roettger D, Dehmeshki J, Awan O, Kubassova O, Bisdas S. The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Front Oncol 2020; 10:559946. [PMID: 33072586 PMCID: PMC7539039 DOI: 10.3389/fonc.2020.559946] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/13/2020] [Indexed: 01/22/2023] Open
Affiliation(s)
- Faiq Shaikh
- Image Analysis Group, Philadelphia, PA, United States
| | | | - Jamshid Dehmeshki
- Image Analysis Group, Philadelphia, PA, United States.,Department of Computer Science, Kingston University, Kingston-upon-Thames, United Kingdom
| | - Omer Awan
- Department of Radiology, University of Maryland Medical Center, Baltimore, MD, United States
| | | | - Sotirios Bisdas
- Department of Neuroradiology, University College London, London, United Kingdom
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Oltra-Sastre M, Fuster-Garcia E, Juan-Albarracin J, Sáez C, Perez-Girbes A, Sanz-Requena R, Revert-Ventura A, Mocholi A, Urchueguia J, Hervas A, Reynes G, Font-de-Mora J, Muñoz-Langa J, Botella C, Aparici F, Marti-Bonmati L, Garcia-Gomez JM. Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Curr Med Imaging 2020; 15:933-947. [PMID: 32008521 DOI: 10.2174/1573405615666190109100503] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 11/27/2018] [Accepted: 12/13/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE To systematically review evidence regarding the association of multiparametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. MATERIALS AND METHODS Scopus database was searched for original journal papers from January 1st, 2007 to February 20th, 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. RESULTS It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and highrisk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, α=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. CONCLUSION Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.
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Affiliation(s)
- Miquel Oltra-Sastre
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Elies Fuster-Garcia
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Javier Juan-Albarracin
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Carlos Sáez
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Alexandre Perez-Girbes
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | | | | | - Antonio Mocholi
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Javier Urchueguia
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Antonio Hervas
- Instituto de Matematica Multidisciplinar (IMM), Universitat Politecnica de Valencia, Valencia, Spain
| | - Gaspar Reynes
- Grupo de Investigacion Clinica y Traslacional del Cancer, Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Jaime Font-de-Mora
- Grupo de Investigacion Clinica y Traslacional del Cancer, Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Jose Muñoz-Langa
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Carlos Botella
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Fernando Aparici
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Luis Marti-Bonmati
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Juan M Garcia-Gomez
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
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Qiu Q, Duan J, Deng H, Han Z, Gu J, Yue NJ, Yin Y. Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery. Front Oncol 2020; 10:1398. [PMID: 32850451 PMCID: PMC7431604 DOI: 10.3389/fonc.2020.01398] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/02/2020] [Indexed: 12/24/2022] Open
Abstract
Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinghao Duan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hongbin Deng
- Department of Medical Imaging Ultrasonography, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhujun Han
- Department of Radiation Oncology, Yantai Yuhuangding Hospital, Yantai, China
| | - Jiabing Gu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ning J Yue
- Department of Radiation Oncology, The Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Survival-relevant high-risk subregion identification for glioblastoma patients: the MRI-based multiple instance learning approach. Eur Radiol 2020; 30:5602-5610. [DOI: 10.1007/s00330-020-06912-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/07/2020] [Accepted: 04/23/2020] [Indexed: 12/26/2022]
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Kunz M, Albert NL, Unterrainer M, la Fougere C, Egensperger R, Schüller U, Lutz J, Kreth S, Tonn JC, Kreth FW, Thon N. Dynamic 18F-FET PET is a powerful imaging biomarker in gadolinium-negative gliomas. Neuro Oncol 2020; 21:274-284. [PMID: 29893965 DOI: 10.1093/neuonc/noy098] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND We aimed to elucidate the place of dynamic O-(2-[18F]-fluoroethyl)-L-tyrosine (18F-FET) PET in prognostic models of gadolinium (Gd)-negative gliomas. METHODS In 98 patients with Gd-negative gliomas undergoing 18F-FET PET guided biopsy, time activity curves (TACs) of each tumor were qualitatively categorized as either increasing or decreasing. Additionally, post-hoc quantitative analyses were done using minimal time-to-peak (TTPmin) measurements. Prognostic factors were obtained from multivariate hazards models. The fit of the biospecimen- and imaging-derived models was compared. RESULTS A homogeneous increasing, mixed, and homogeneous decreasing TAC pattern was seen in 51, 19, and 28 tumors, respectively. Mixed TAC tumors exhibited both increasing and decreasing TACs. Corresponding adjusted 5-year survival was 85%, 47%, and 19%, respectively (P < 0.001). Qualitative and quantitative TAC measurements were highly intercorrelated (P < 0.0001). TTPmin was longest (shortest) in the homogeneous increasing (decreasing) TAC group and in between in the mixed TAC group. TTPmin was longer in isocitrate dehydrogenase (IDH)-mutant tumors (P < 0.001). Outcome was similarly precisely predicted by biospecimen- and imaging-derived models. In the biospecimen model, World Health Organization (WHO) grade (P < 0.0001) and IDH status (P < 0.001) were predictors for survival. Outcome of homogeneous increasing (homogeneous decreasing) TAC tumors was nearly identical, with both TTPmin > 25 min (TTPmin ≤ 12.5 min) tumors and IDH-mutant grade II (IDH-wildtype) gliomas. Outcome of mixed TAC tumors matched that of both intermediate TTPmin (>12.5 min and ≤25 min) and IDH-mutant, grade III gliomas. Each of the 3 prognostic clusters differed significantly from the other ones of the respective models (P < 0.001). CONCLUSION TAC measurements constitute a powerful biomarker independent from tumor grade and IDH status.
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Affiliation(s)
- Mathias Kunz
- Department of Neurosurgery, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Nathalie Lisa Albert
- Department of Nuclear Medicine, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Marcus Unterrainer
- Department of Nuclear Medicine, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Christian la Fougere
- Department of Nuclear Medicine, University of Munich, Munich, Germany.,Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Tübingen, Tübingen, Germany
| | - Rupert Egensperger
- Center for Neuropathology, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Ulrich Schüller
- Center for Neuropathology, University of Munich, Munich, Germany.,Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Juergen Lutz
- Department of Clinical Radiology, University of Munich, Munich, Germany
| | - Simone Kreth
- Department of Anaesthesiology, University of Munich, Munich, Germany
| | - Jörg-Christian Tonn
- Department of Neurosurgery, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Friedrich-Wilhelm Kreth
- Department of Neurosurgery, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Niklas Thon
- Department of Neurosurgery, University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
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40
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Jiang Z, Yin J. Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE-MRI in classifying benign from malignant lesions. J Surg Oncol 2020; 121:1181-1190. [PMID: 32167588 DOI: 10.1002/jso.25901] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/02/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND OBJECTIVES To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors. METHODS A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SIslope ), initial percentage of peak enhancement (Einitial ), percentage of peak enhancement (Epeak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out cross-validation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. RESULTS The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SIslope , 0.756 for Einitial , 0.923 for Epeak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between Einitial vs MSI, Einitial vs Epeak and Einitial vs SEP (P < .05). There were no significant differences in other pairwise comparisons. CONCLUSION Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis.
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Affiliation(s)
- Zejun Jiang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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41
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Radiomics prognostication model in glioblastoma using diffusion- and perfusion-weighted MRI. Sci Rep 2020; 10:4250. [PMID: 32144360 PMCID: PMC7060336 DOI: 10.1038/s41598-020-61178-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 02/19/2020] [Indexed: 01/30/2023] Open
Abstract
We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P < 0.0001) or clinical predictors (C-index, 0.66; P < 0.0001). The multiparametric MR prognostic model also showed robustness in external validation (C-index, 0.70). Our results indicate that the incorporation of diffusion- and perfusion-weighted MR imaging into an MR radiomics model to improve prognostication in glioblastoma patients improved its performance over that achievable using clinical predictors alone.
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42
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Wu J, Gensheimer MF, Zhang N, Guo M, Liang R, Zhang C, Fischbein N, Pollom EL, Beadle B, Le QT, Li R. Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer. J Nucl Med 2020; 61:327-336. [PMID: 31420498 PMCID: PMC7067523 DOI: 10.2967/jnumed.119.230037] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 07/29/2019] [Indexed: 12/19/2022] Open
Abstract
The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on 18F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4-13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7-12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Michael F Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Nasha Zhang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Meiying Guo
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Rachel Liang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Carrie Zhang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Nancy Fischbein
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Erqi L Pollom
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Beth Beadle
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
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Senders JT, Staples P, Mehrtash A, Cote DJ, Taphoorn MJB, Reardon DA, Gormley WB, Smith TR, Broekman ML, Arnaout O. An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning. Neurosurgery 2020; 86:E184-E192. [PMID: 31586211 PMCID: PMC7061165 DOI: 10.1093/neuros/nyz403] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 07/18/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata. OBJECTIVE To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator. METHODS Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves. RESULTS In total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/). CONCLUSION The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.
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Affiliation(s)
- Joeky T Senders
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
| | - Patrick Staples
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Alireza Mehrtash
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
- Department of Radiology, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
| | - David J Cote
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
| | - Martin J B Taphoorn
- Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands
| | - David A Reardon
- Department of Neurology, Dana-Farber Cancer Institute, School of Medicine, Harvard University, Boston, Massachusetts
| | - William B Gormley
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
| | - Timothy R Smith
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
| | - Marike L Broekman
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
- Department of Neurosurgery, Haaglanden Medical Center, The Hague, The Netherlands
| | - Omar Arnaout
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, School of Medicine, Harvard University, Boston, Massachusetts
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Liu L, Zhang H, Wu J, Yu Z, Chen X, Rekik I, Wang Q, Lu J, Shen D. Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks. Brain Imaging Behav 2020; 13:1333-1351. [PMID: 30155788 DOI: 10.1007/s11682-018-9949-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.
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Affiliation(s)
- Luyan Liu
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jinsong Wu
- Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China.,Shanghai Key Lab of Medical Image Computing and Computer-Assisted Intervention, Shanghai, 200040, China.,Neurosurgery Department of Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Zhengda Yu
- Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,BASIRA Lab, CVIP Group, School of Science and Engineering, Computing, University of Dundee, Dundee, UK
| | - Qian Wang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
| | - Junfeng Lu
- Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China. .,Shanghai Key Lab of Medical Image Computing and Computer-Assisted Intervention, Shanghai, 200040, China. .,Neurosurgery Department of Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai, 200040, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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18F-Fluorocholine PET/CT in the Prediction of Molecular Subtypes and Prognosis for Gliomas. Clin Nucl Med 2019; 44:e548-e558. [PMID: 31306196 DOI: 10.1097/rlu.0000000000002715] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
AIM To study the association of metabolic features of F-fluorocholine in gliomas with histopathological and molecular parameters, progression-free survival (PFS) and overall survival (OS). METHODS Prospective multicenter and nonrandomized study (Functional and Metabolic Glioma Analysis). Patients underwent a basal F-fluorocholine PET/CT and were included after histological confirmation of glioma. Histological and molecular profile was assessed: grade, Ki-67, isocitrate dehydrogenase status and 1p/19q codeletion. Patients underwent standard treatment after surgery or biopsy, depending on their clinical situation. Overall survival and PFS were obtained after follow-up. After tumor segmentation of PET images, SUV and volume-based variables, sphericity, surface, coefficient of variation, and multilesionality were obtained. Relations of metabolic variables with histological, molecular profile and prognosis were evaluated using Pearson χ and t test. Receiver operator caracteristic curves were used to obtain the cutoff of PET variables. Survival analysis was performed using Kaplan-Meier and Cox regression analysis. RESULTS Forty-five patients were assessed; 38 were diagnosed as having high-grade gliomas. Significant differences of SUV-based variables with isocitrate dehydrogenase status, tumor grade, and Ki-67 were found. Tumor grade, Ki-67, SUVmax, and SUVmean were related to progression. Kaplan-Meier analysis revealed significant associations of SUVmax, SUVmean, and multilesionaly with OS and PFS. SUVmean, sphericity, and multilesionality were independent predictors of OS and PFS in Cox regression analysis. CONCLUSIONS Metabolic information obtained from F-fluorocholine PET of patients with glioma may be useful in the prediction of tumor biology and patient prognosis.
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Álvarez‐Torres M, Juan‐Albarracín J, Fuster‐Garcia E, Bellvís‐Bataller F, Lorente D, Reynés G, Font de Mora J, Aparici‐Robles F, Botella C, Muñoz‐Langa J, Faubel R, Asensio‐Cuesta S, García‐Ferrando GA, Chelebian E, Auger C, Pineda J, Rovira A, Oleaga L, Mollà‐Olmos E, Revert AJ, Tshibanda L, Crisi G, Emblem KE, Martin D, Due‐Tønnessen P, Meling TR, Filice S, Sáez C, García‐Gómez JM. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J Magn Reson Imaging 2019; 51:1478-1486. [DOI: 10.1002/jmri.26958] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 09/19/2019] [Indexed: 02/03/2023] Open
Affiliation(s)
- María Álvarez‐Torres
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Javier Juan‐Albarracín
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | | | - Fuensanta Bellvís‐Bataller
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - David Lorente
- Hospital Provincial de Castellón, Department of Medical Oncology Castellón de la Plana Castellón de la Plana Spain
| | - Gaspar Reynés
- Health Research Institute Hospital La FeCancer Research Group Valencia Spain
| | - Jaime Font de Mora
- Instituto de Investigación Sanitaria La Fe, Laboratory of Cellular and Molecular Biology Valencia Spain
| | | | - Carlos Botella
- Hospital Universitari i Politècnic La Fe, Área Clínica de Neurociencias Valencia Spain
| | - Jose Muñoz‐Langa
- Health Research Institute Hospital La FeCancer Research Group Valencia Spain
| | - Raquel Faubel
- Universitat de València, Departament de Fisioteràpia Valencia Spain
| | - Sabina Asensio‐Cuesta
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Germán A. García‐Ferrando
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Eduard Chelebian
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Cristina Auger
- Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Magnetic Resonance Unit, Department of Radiology Barcelona Spain
| | - Jose Pineda
- Hospital Clinic de Barcelona Barcelona Spain
| | - Alex Rovira
- Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Magnetic Resonance Unit, Department of Radiology Barcelona Spain
| | | | - Enrique Mollà‐Olmos
- Hospital Universitario de la Ribera, Departamento de Radiodiagnóstico Alzira Valencia Spain
| | | | - Luaba Tshibanda
- Centre Hospitalier Universitaire de Liège, Service médical de Radiodiagnostic Liège Belgium
| | - Girolamo Crisi
- Azienda Ospedaliero‐Universitaria di Parma, Neuroradiology Parma Italy
| | - Kyrre E. Emblem
- Oslo University Hospital, Department of Diagnostic Physics Oslo Norway
| | - Didier Martin
- Centre Hospitalier Universitaire de Liege, Service de Neurochirurugie Liège Belgium
| | | | - Torstein R. Meling
- Oslo University Hospital, Department of Neurosurgery Oslo Norway
- Geneva University Hospital, Department of Neurosurgery Geneva Switzerland
| | - Silvano Filice
- Azienda Ospedaliero‐Universitaria di Parma, Medical Physics Parma Italy
| | - Carlos Sáez
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Juan M. García‐Gómez
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
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Danieli L, Riccitelli GC, Distefano D, Prodi E, Ventura E, Cianfoni A, Kaelin-Lang A, Reinert M, Pravatà E. Brain Tumor-Enhancement Visualization and Morphometric Assessment: A Comparison of MPRAGE, SPACE, and VIBE MRI Techniques. AJNR Am J Neuroradiol 2019; 40:1140-1148. [PMID: 31221635 DOI: 10.3174/ajnr.a6096] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 05/08/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND AND PURPOSE Postgadolinium MR imaging is crucial for brain tumor diagnosis and morphometric assessment. We compared brain tumor enhancement visualization and the "target" object morphometry obtained with the most commonly used 3D MR imaging technique, MPRAGE, with 2 other routinely available techniques: sampling perfection with application-optimized contrasts by using different flip angle evolutions (SPACE) and volumetric interpolated brain examination (VIBE). MATERIALS AND METHODS Fifty-four contrast-enhancing tumors (38 gliomas and 16 metastases) were assessed using MPRAGE, VIBE, and SPACE techniques randomly acquired after gadolinium-based contrast agent administration on a 3T scanner. Enhancement conspicuity was assessed quantitatively by calculating the contrast rate and contrast-to-noise ratio, and qualitatively, by consensus visual comparative ratings. The total enhancing tumor volume and between-sequence discrepancy in the margin delineation were assessed on the corresponding 3D target objects contoured with a computer-assisted software for neuronavigation. The Wilcoxon signed rank and Pearson χ2 nonparametric tests were used to investigate between-sequence discrepancies in the contrast rate, contrast-to-noise ratio, visual conspicuity ratings, tumor volume, and margin delineation estimates. Differences were also tested for 1D (Response Evaluation Criteria in Solid Tumors) and 2D (Response Assessment in Neuro-Oncology) measurements. RESULTS Compared with MPRAGE, both SPACE and VIBE obtained higher contrast rate, contrast-to-noise ratio, and visual conspicuity ratings in both gliomas and metastases (P range, <.001-.001). The between-sequence 3D target object margin discrepancy ranged between 3% and 19.9% of lesion tumor volume. Larger tumor volumes, 1D and 2D measurements were obtained with SPACE (P range, <.01-.007). CONCLUSIONS Superior conspicuity for brain tumor enhancement can be achieved using SPACE and VIBE techniques, compared with MPRAGE. Discrepancies were also detected when assessing target object size and morphology, with SPACE providing more accurate estimates.
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Affiliation(s)
- L Danieli
- From the Departments of Neuroradiology (L.D., D.D., E.P., E.V., A.C., E.P.)
| | - G C Riccitelli
- Neurology (G.C.R., A.K.-L.).,Neuroimaging Research Unit (G.C.R.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - D Distefano
- From the Departments of Neuroradiology (L.D., D.D., E.P., E.V., A.C., E.P.)
| | - E Prodi
- From the Departments of Neuroradiology (L.D., D.D., E.P., E.V., A.C., E.P.)
| | - E Ventura
- From the Departments of Neuroradiology (L.D., D.D., E.P., E.V., A.C., E.P.)
| | - A Cianfoni
- From the Departments of Neuroradiology (L.D., D.D., E.P., E.V., A.C., E.P.).,Departments of Neuroradiology (A.C.)
| | - A Kaelin-Lang
- Neurology (G.C.R., A.K.-L.).,Neurology (A.K.-L.), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Faculty of Biomedical Sciences (A.K.-L., M.R.), Università della Svizzera Italiana, Lugano, Switzerland
| | - M Reinert
- Neurosurgery (M.R.), Neurocenter of Southern Switzerland, Lugano, Switzerland.,Faculty of Biomedical Sciences (A.K.-L., M.R.), Università della Svizzera Italiana, Lugano, Switzerland
| | - E Pravatà
- From the Departments of Neuroradiology (L.D., D.D., E.P., E.V., A.C., E.P.)
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48
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Gandía-González ML, Cerdán S, Barrios L, López-Larrubia P, Feijoó PG, Palpan A, Roda JM, Solivera J. Assessment of Overall Survival in Glioma Patients as Predicted by Metabolomic Criteria. Front Oncol 2019; 9:328. [PMID: 31134147 PMCID: PMC6524167 DOI: 10.3389/fonc.2019.00328] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 04/11/2019] [Indexed: 11/17/2022] Open
Abstract
Objective: We assess the efficacy of the metabolomic profile from glioma biopsies in providing estimates of postsurgical Overall Survival in glioma patients. Methods: Tumor biopsies from 46 patients bearing gliomas, obtained neurosurgically in the period 1992–1998, were analyzed by high resolution 1H magnetic resonance spectroscopy (HR- 1H MRS), following retrospectively individual postsurgical Overall Survival up to 720 weeks. Results: The Overall Survival profile could be resolved in three groups; Short (shorter than 52 weeks, n = 19), Intermediate (between 53 and 364 weeks, n = 19) or Long (longer than 365 weeks, n = 8), respectively. Classical histopathological analysis assigned WHO grades II–IV to every biopsy but notably, some patients with low grade glioma depicted unexpectedly Short Overall Survival, while some patients with high grade glioma, presented unpredictably Long Overall Survival. To explore the reasons underlying these different responses, we analyzed HR-1H MRS spectra from acid extracts of the same biopsies, to characterize the metabolite patterns associated to OS predictions. Poor prognosis was found in biopsies with higher contents of alanine, acetate, glutamate, total choline, phosphorylcholine, and glycine, while more favorable prognosis was achieved in biopsies with larger contents of total creatine, glycerol-phosphorylcholine, and myo-inositol. We then implemented a multivariate analysis to identify hierarchically the influence of metabolomic biomarkers on OS predictions, using a Classification Regression Tree (CRT) approach. The CRT based in metabolomic biomarkers grew up to three branches and split into eight nodes, predicting correctly the outcome of 94.7% of the patients in the Short Overall Survival group, 78.9% of the patients in the Intermediate Overall Survival group, and 75% of the patients in the Long Overall Survival group, respectively. Conclusion: Present results indicate that metabolic profiling by HR-1H MRS improves the Overall Survival predictions derived exclusively from classical histopathological gradings, thus favoring more precise therapeutic decisions.
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Affiliation(s)
| | - Sebastián Cerdán
- Institute of Biomedical Research "Alberto Sols" CSIC/UAM, Madrid, Spain
| | | | | | - Pablo G Feijoó
- Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain
| | - Alexis Palpan
- Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain
| | - José M Roda
- Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain
| | - Juan Solivera
- Department of Neurosurgery, University Hospital Reina Sofía, Córdoba, Spain
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49
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Molina-García D, Vera-Ramírez L, Pérez-Beteta J, Arana E, Pérez-García VM. Prognostic models based on imaging findings in glioblastoma: Human versus Machine. Sci Rep 2019; 9:5982. [PMID: 30979965 PMCID: PMC6461644 DOI: 10.1038/s41598-019-42326-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 03/26/2019] [Indexed: 12/30/2022] Open
Abstract
Many studies have built machine-learning (ML)-based prognostic models for glioblastoma (GBM) based on radiological features. We wished to compare the predictive performance of these methods to human knowledge-based approaches. 404 GBM patients were included (311 discovery and 93 validation). 16 morphological and 28 textural descriptors were obtained from pretreatment volumetric postcontrast T1-weighted magnetic resonance images. Different prognostic ML methods were developed. An optimized linear prognostic model (OLPM) was also built using the four significant non-correlated parameters with individual prognosis value. OLPM achieved high prognostic value (validation c-index = 0.817) and outperformed ML models based on either the same parameter set or on the full set of 44 attributes considered. Neural networks with cross-validation-optimized attribute selection achieved comparable results (validation c-index = 0.825). ML models using only the four outstanding parameters obtained better results than their counterparts based on all the attributes, which presented overfitting. In conclusion, OLPM and ML methods studied here provided the most accurate survival predictors for glioblastoma to date, due to a combination of the strength of the methodology, the quality and volume of the data used and the careful attribute selection. The ML methods studied suffered overfitting and lost prognostic value when the number of parameters was increased.
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Affiliation(s)
- David Molina-García
- Mathematics Department, Mathematical Oncology Laboratory (MôLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain.
| | | | - Julián Pérez-Beteta
- Mathematics Department, Mathematical Oncology Laboratory (MôLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Estanislao Arana
- Radiology Department, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematics Department, Mathematical Oncology Laboratory (MôLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
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50
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Conventional Magnetic Resonance Features for Predicting 1p19q Codeletion Status of World Health Organization Grade II and III Diffuse Gliomas. J Comput Assist Tomogr 2019; 43:269-276. [PMID: 30371623 DOI: 10.1097/rct.0000000000000816] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE The conventional magnetic resonance features of World Health Organization (WHO) grade II and III diffuse gliomas in relation to chromosome 1p and 19q deletions (1p19q codeletion) were analyzed. METHODS We identified 147 cases of WHO grade II and III diffuse gliomas (1p/19q codeletion, 36 cases; no 1p/19q codeletion, 111 cases). χ Test and univariate and multivariate binary logistic regression analyses were conducted to evaluate the association between the imaging features and 1p19q codeletion status of WHO grade II and III diffuse gliomas in the discovery group, including the WHO grade II and III subgroups. RESULTS (1) In the entire population, multivariate regression demonstrated that proportion contrast-enhanced tumor (>5% vs ≤5%; odds ratio [OR], 0.169; P = 0.009), enhancing margin (poorly vs well defined; OR, 12.435; P = 0.002), and hemorrhage (yes vs no; OR, 21.082; P < 0.001) were associated with a higher incidence of 1p19q codeletion status. The nomogram showed good discrimination (area under the curve [AUC], 0.803) and calibration. (2) For grade II tumors, subgroup analysis found that enhancing margin (poorly vs well defined; OR, 0.308; P = 0.007) and subventricular zone (presence vs absence-; OR, 0.137; P < 0.001) were associated with a higher incidence of 1p19q codeletion status (AUC, 0.779). (3) For grade III tumors, subgroup analysis found that age (≥40 years vs <40 years; OR, 5.977; P = 0.03) and hemorrhage (yes vs no; OR, 18.051; P < 0.001) were associated with a higher incidence of 1p19q codeletion status (AUC, 0.816). CONCLUSIONS Conventional magnetic resonance features can be conveniently used to facilitate the preoperative prediction of 1p19q codeletion status of WHO grade II and III diffuse gliomas. Decision curve analysis demonstrated that the nomogram was clinically useful.
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