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Herr J, Stoyanova R, Mellon EA. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Crit Rev Oncog 2024; 29:33-65. [PMID: 38683153 DOI: 10.1615/critrevoncog.2023050852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
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Affiliation(s)
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| | - Eric Albert Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
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Bathla G, Soni N, Ward C, Pillenahalli Maheshwarappa R, Agarwal A, Priya S. Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging. J Comput Assist Tomogr 2023; 47:919-923. [PMID: 37948367 DOI: 10.1097/rct.0000000000001493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
INTRODUCTION Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.
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Affiliation(s)
- Girish Bathla
- From the Department of Radiology, Mayo Clinic, Rochester, MN
| | - Neetu Soni
- Department of Radiology, University of Rochester Medical Center, Rochester, NY
| | - Caitlin Ward
- Division of Biostatistics, School of Public Health, University of Minnesota, MN
| | | | - Amit Agarwal
- Department of Radiology, Mayo Clinic, Jacksonville, FL
| | - Sarv Priya
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA
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Salome P, Sforazzini F, Grugnara G, Kudak A, Dostal M, Herold-Mende C, Heiland S, Debus J, Abdollahi A, Knoll M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers (Basel) 2023; 15:cancers15030965. [PMID: 36765922 PMCID: PMC9913466 DOI: 10.3390/cancers15030965] [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: 01/07/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
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Affiliation(s)
- Patrick Salome
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
| | - Francesco Sforazzini
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
| | - Gianluca Grugnara
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Andreas Kudak
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Matthias Dostal
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Brain Tumour Group, European Organization for Research and Treatment of Cancer, 1200 Brussels, Belgium
- Division of Neurosurgical Research, Department of Neurosurgery, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jürgen Debus
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Amir Abdollahi
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
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Alleman K, Knecht E, Huang J, Zhang L, Lam S, DeCuypere M. Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review. Cancers (Basel) 2023; 15:cancers15020545. [PMID: 36672494 PMCID: PMC9856816 DOI: 10.3390/cancers15020545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/05/2023] [Accepted: 01/08/2023] [Indexed: 01/18/2023] Open
Abstract
Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. A systematic review of the DL-based prognostication of gliomas was performed using the Embase (Elsevier), PubMed MEDLINE (National library of Medicine), and Scopus (Elsevier) databases, in accordance with PRISMA guidelines. All included studies focused on the prognostication of gliomas, and predicted overall survival (13 studies, 81%), overall survival as well as genotype (2 studies, 12.5%), and response to immunotherapy (1 study, 6.2%). Multimodal analyses were varied, with 6 studies (37.5%) combining MRI with clinical data; 6 studies (37.5%) integrating MRI with histologic, clinical, and biomarker data; 3 studies (18.8%) combining MRI with genomic data; and 1 study (6.2%) combining histologic imaging with clinical data. Studies that compared multimodal models to unimodal-only models demonstrated improved predictive performance. The risk of bias was mixed, most commonly due to inconsistent methodological reporting. Overall, the use of multimodal data in DL assessments of gliomas leads to a more accurate overall survival prediction. However, due to data limitations and a lack of transparency in model and code reporting, the full extent of multimodal DL as a resource for brain tumor patients has not yet been realized.
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Affiliation(s)
- Kaitlyn Alleman
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA
| | - Erik Knecht
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA
| | - Jonathan Huang
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Lu Zhang
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Sandi Lam
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Michael DeCuypere
- Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Correspondence:
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Ouyang G, Chen Z, Dou M, Luo X, Wen H, Deng X, Meng W, Yu Y, Wu B, Jiang D, Wang Z, Yao Y, Wang X. Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data. Technol Cancer Res Treat 2023; 22:15330338231186467. [PMID: 37431270 PMCID: PMC10338728 DOI: 10.1177/15330338231186467] [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: 02/08/2023] [Revised: 05/15/2023] [Accepted: 05/24/2023] [Indexed: 07/12/2023] Open
Abstract
PURPOSE To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods. METHODS Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2). We extracted and selected clinical and radiomic features on baseline T2WI. Then we built LR models and DL models. Receiver operating characteristic (ROC) curves analysis was performed to assess predictive performance of models. RESULTS Eighty-nine patients were assigned to the training cohort, and 29 patients were assigned to the testing cohort. The area under receiver operating characteristics curve (AUC) of LR models, which were predictive of high sensitivity and pCR, were 0.853 and 0.866, respectively. Whereas the AUCs of DL models were 0.829 and 0.838, respectively. After 10 rounds of cross validation, the accuracy of the models in Group 1 is higher than in Group 2. CONCLUSION There was no significant difference between LR model and DL model. Artificial Intelligence-based radiomics biomarkers may have potential clinical implications for adaptive and personalized therapy.
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Affiliation(s)
- Ganlu Ouyang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhebin Chen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Meng Dou
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xu Luo
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Han Wen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiangbing Deng
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wenjian Meng
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yongyang Yu
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bing Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Jiang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqiang Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Yao
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xin Wang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121746. [PMID: 36556948 PMCID: PMC9786785 DOI: 10.3390/medicina58121746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
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Affiliation(s)
- Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
- Correspondence:
| | - Manuel García-Galindo
- Faculty of Medicine, University of Valladolid, Avenida Ramón y Cajal 7, 47003 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
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Piao S, Luo X, Bao Y, Hu B, Liu X, Zhu Y, Yang L, Geng D, Li Y. An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma. Front Neurol 2022; 13:998279. [PMID: 36408523 PMCID: PMC9669344 DOI: 10.3389/fneur.2022.998279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/12/2022] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions. METHODS In our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists. RESULTS The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets. CONCLUSION The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yuqi Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients. J Neurooncol 2022; 160:253-263. [DOI: 10.1007/s11060-022-04150-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
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Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022; 14:3897. [PMID: 36010891 PMCID: PMC9406186 DOI: 10.3390/cancers14163897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.
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Affiliation(s)
- Philip Martin
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Eng-Siew Koh
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Caterina Brighi
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Qu Y, Yan D, Xing E, Zheng F, Zhang J, Liu L, Liang G. Beware the Black-Box of Medical Image Generation: an Uncertainty Analysis by the Learned Feature Space. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3849-3853. [PMID: 36085751 DOI: 10.1109/embc48229.2022.9871921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Deep neural networks (DNNs) are the primary driving force for the current development of medical imaging analysis tools and often provide exciting performance on various tasks. However, such results are usually reported on the overall performance of DNNs, such as the Peak signal-to-noise ratio (PSNR) or mean square error (MSE) for imaging generation tasks. As a black-box, DNNs usually produce a relatively stable performance on the same task across multiple training trials, while the learned feature spaces could be significantly different. We believe additional insightful analysis, such as uncertainty analysis of the learned feature space, is equally important, if not more. Through this work, we evaluate the learned feature space of multiple U-Net architectures for image generation tasks using computational analysis and clustering analysis methods. We demonstrate that the learned feature spaces are easily separable between different training trials of the same architecture with the same hyperparameter setting, indicating the models using different criteria for the same tasks. This phenomenon naturally raises the question of which criteria are correct to use. Thus, our work suggests that assessments other than overall performance are needed before applying a DNN model to real-world practice.
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12
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Jian A, Liu S, Di Ieva A. Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging. Neurosurgery 2022; 91:8-26. [PMID: 35348129 DOI: 10.1227/neu.0000000000001938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/08/2022] [Indexed: 12/30/2022] Open
Abstract
Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.
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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, Australia
- Royal Melbourne Hospital, Melbourne, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
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13
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Han J, Xiao N, Yang W, Luo S, Zhao J, Qiang Y, Chaudhary S, Zhao J. MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data. Int J Comput Assist Radiol Surg 2022; 17:1049-1057. [PMID: 35445285 PMCID: PMC9020752 DOI: 10.1007/s11548-022-02625-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/24/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly. METHODS In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results. RESULTS The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor. CONCLUSION In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.
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Affiliation(s)
- Jiahao Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ning Xiao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wanting Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shichao Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jun Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Suman Chaudhary
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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14
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Li Z, Wu F, Hong F, Gai X, Cao W, Zhang Z, Yang T, Wang J, Gao S, Peng C. Computer-Aided Diagnosis of Spinal Tuberculosis From CT Images Based on Deep Learning With Multimodal Feature Fusion. Front Microbiol 2022; 13:823324. [PMID: 35283815 PMCID: PMC8905347 DOI: 10.3389/fmicb.2022.823324] [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: 11/27/2021] [Accepted: 01/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Spinal tuberculosis (TB) has the highest incidence in remote plateau areas, particularly in Tibet, China, due to inadequate local healthcare services, which not only facilitates the transmission of TB bacteria but also increases the burden on grassroots hospitals. Computer-aided diagnosis (CAD) is urgently required to improve the efficiency of clinical diagnosis of TB using computed tomography (CT) images. However, classical machine learning with handcrafted features generally has low accuracy, and deep learning with self-extracting features relies heavily on the size of medical datasets. Therefore, CAD, which effectively fuses multimodal features, is an alternative solution for spinal TB detection. Methods A new deep learning method is proposed that fuses four elaborate image features, specifically three handcrafted features and one convolutional neural network (CNN) feature. Spinal TB CT images were collected from 197 patients with spinal TB, from 2013 to 2020, in the People’s Hospital of Tibet Autonomous Region, China; 3,000 effective lumbar spine CT images were randomly screened to our dataset, from which two sets of 1,500 images each were classified as tuberculosis (positive) and health (negative). In addition, virtual data augmentation is proposed to enlarge the handcrafted features of the TB dataset. Essentially, the proposed multimodal feature fusion CNN consists of four main sections: matching network, backbone (ResNet-18/50, VGG-11/16, DenseNet-121/161), fallen network, and gated information fusion network. Detailed performance analyses were conducted based on the multimodal features, proposed augmentation, model stability, and model-focused heatmap. Results Experimental results showed that the proposed model with VGG-11 and virtual data augmentation exhibited optimal performance in terms of accuracy, specificity, sensitivity, and area under curve. In addition, an inverse relationship existed between the model size and test accuracy. The model-focused heatmap also shifted from the irrelevant region to the bone destruction caused by TB. Conclusion The proposed augmentation effectively simulated the real data distribution in the feature space. More importantly, all the evaluation metrics and analyses demonstrated that the proposed deep learning model exhibits efficient feature fusion for multimodal features. Our study provides a profound insight into the preliminary auxiliary diagnosis of spinal TB from CT images applicable to the Tibetan area.
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Affiliation(s)
- Zhaotong Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,School of Health Humanities, Peking University, Beijing, China
| | - Fengliang Wu
- Beijing Key Laboratory of Spinal Disease Research, Engineering Research Center of Bone and Joint Precision Medicine, Department of Orthopedics, Peking University Third Hospital, Beijing, China.,Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| | - Fengze Hong
- Medical College, Tibet University, Lhasa, China
| | - Xiaoyan Gai
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Wenli Cao
- Tuberculosis Department, Beijing Geriatric Hospital, Beijing, China
| | - Zeru Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,School of Health Humanities, Peking University, Beijing, China
| | - Timin Yang
- Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| | - Jiu Wang
- Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| | - Song Gao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Chao Peng
- Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
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15
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Kaur G, Rana PS, Arora V. State-of-the-art techniques using pre-operative brain MRI scans for survival prediction of glioblastoma multiforme patients and future research directions. Clin Transl Imaging 2022; 10:355-389. [PMID: 35261910 PMCID: PMC8891433 DOI: 10.1007/s40336-022-00487-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/15/2022] [Indexed: 11/28/2022]
Abstract
Objective Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment. Methods This study aims at examining the recent methodologies developed using automated learning and radiomics to automate the process of SP. Automated techniques use pre-operative raw magnetic resonance imaging (MRI) scans and clinical data related to GBM patients. All SP methods submitted for the multimodal brain tumour segmentation (BraTS) challenge are examined to extract the generic workflow for SP. Results The maximum accuracies achieved by 21 state-of-the-art different SP techniques reviewed in this study are 65.5 and 61.7% using the validation and testing subsets of the BraTS dataset, respectively. The comparisons based on segmentation architectures, SP models, training parameters and hardware configurations have been made. Conclusion The limited accuracies achieved in the literature led us to review the various automated methodologies and evaluation metrics to find out the research gaps and other findings related to the survival prognosis of GBM patients so that these accuracies can be improved in future. Finally, the paper provides the most promising future research directions to improve the performance of automated SP techniques and increase their clinical relevance.
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Affiliation(s)
- Gurinderjeet Kaur
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab India
| | - Prashant Singh Rana
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab India
| | - Vinay Arora
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab India
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16
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Zhou Q, Xue C, Ke X, Zhou J. Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI. J Magn Reson Imaging 2022; 56:325-340. [PMID: 35129845 DOI: 10.1002/jmri.28103] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 12/19/2022] Open
Abstract
In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
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17
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Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI. Cancers (Basel) 2021; 13:cancers13205047. [PMID: 34680199 PMCID: PMC8533879 DOI: 10.3390/cancers13205047] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/28/2021] [Accepted: 10/06/2021] [Indexed: 01/06/2023] Open
Abstract
Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.
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18
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Aili Y, Maimaitiming N, Mahemuti Y, Qin H, Wang Y, Wang Z. The Role of Exosomal miRNAs in Glioma: Biological Function and Clinical Application. Front Oncol 2021; 11:686369. [PMID: 34540663 PMCID: PMC8442992 DOI: 10.3389/fonc.2021.686369] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/10/2021] [Indexed: 12/16/2022] Open
Abstract
Gliomas are complex and heterogeneous central nervous system tumors with poor prognosis. Despite the increasing development of aggressive combination therapies, the prognosis of glioma is generally unsatisfactory. Exosomal microRNA (miRNA) has been successfully used in other diseases as a reliable biomarker and even therapeutic target. Recent studies show that exosomal miRNA plays an important role in glioma occurrence, development, invasion, metastasis, and treatment resistance. However, the association of exosomal miRNA between glioma has not been systemically characterized. This will provide a theoretical basis for us to further explore the relationship between exosomal miRNAs and glioma and also has a positive clinical significance in the innovative diagnosis and treatment of glioma.
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Affiliation(s)
- Yirizhati Aili
- Department of Neurosurgery, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | | | - Yusufu Mahemuti
- Department of Neurosurgery, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Hu Qin
- Department of Neurosurgery, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Yongxin Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
| | - Zengliang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China
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19
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Fu J, Singhrao K, Zhong X, Gao Y, Qi SX, Yang Y, Ruan D, Lewis JH. An Automatic Deep Learning-Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study. Adv Radiat Oncol 2021; 6:100746. [PMID: 34458648 PMCID: PMC8377554 DOI: 10.1016/j.adro.2021.100746] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 06/14/2021] [Accepted: 06/23/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Most radiomic studies use the features extracted from the manually drawn tumor contours for classification or survival prediction. However, large interobserver segmentation variations lead to inconsistent features and hence introduce more challenges in constructing robust prediction models. Here, we proposed an automatic workflow for glioblastoma (GBM) survival prediction based on multimodal magnetic resonance (MR) images. METHODS AND MATERIALS Two hundred eighty-five patients with glioma (210 GBM, 75 low-grade glioma) were included. One hundred sixty-three of the patients with GBM had overall survival data. Every patient had 4 preoperative MR images and manually drawn tumor contours. A 3-dimensional convolutional neural network, VGG-Seg, was trained and validated using 122 patients with glioma for automatic GBM segmentation. The trained VGG-Seg was applied to the remaining 163 patients with GBM to generate their autosegmented tumor contours. The handcrafted and deep learning (DL)-based radiomic features were extracted from the autosegmented contours using explicitly designed algorithms and a pretrained convolutional neural network, respectively. One hundred sixty-three patients with GBM were randomly split into training (n = 122) and testing (n = 41) sets for survival analysis. Cox regression models were trained to construct the handcrafted and DL-based signatures. The prognostic powers of the 2 signatures were evaluated and compared. RESULTS The VGG-Seg achieved a mean Dice coefficient of 0.86 across 163 patients with GBM for GBM segmentation. The handcrafted signature achieved a C-index of 0.64 (95% confidence interval, 0.55-0.73), whereas the DL-based signature achieved a C-index of 0.67 (95% confidence interval, 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into 2 prognostically distinct groups. CONCLUSIONS The VGG-Seg generated accurate GBM contours from 4 MR images. The DL-based signature achieved a numerically higher C-index than the handcrafted signature and significant patient stratification. The proposed automatic workflow demonstrated the potential of improving patient stratification and survival prediction in patients with GBM.
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Affiliation(s)
- Jie Fu
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Kamal Singhrao
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California
| | - Xinran Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Yu Gao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California
| | - Sharon X. Qi
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California
| | - Dan Ruan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California
| | - John H. Lewis
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California
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20
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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21
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Zhang M, Tong E, Hamrick F, Lee EH, Tam LT, Pendleton C, Smith BW, Hug NF, Biswal S, Seekins J, Mattonen SA, Napel S, Campen CJ, Spinner RJ, Yeom KW, Wilson TJ, Mahan MA. Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study. Neurosurgery 2021; 89:509-517. [PMID: 34131749 PMCID: PMC8364819 DOI: 10.1093/neuros/nyab212] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/27/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001). CONCLUSION Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.
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Affiliation(s)
- Michael Zhang
- Department of Neurosurgery, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Forrest Hamrick
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - Edward H Lee
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Lydia T Tam
- Stanford School of Medicine, Stanford University, Stanford, California, USA
| | | | - Brandon W Smith
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicholas F Hug
- Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Sandip Biswal
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jayne Seekins
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Sarah A Mattonen
- Department of Medical Biophysics, Western University, London, Canada
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Cynthia J Campen
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA
| | - Robert J Spinner
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Kristen W Yeom
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Thomas J Wilson
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Mark A Mahan
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
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Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31:6001-6012. [PMID: 33492473 PMCID: PMC8270834 DOI: 10.1007/s00330-020-07598-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
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Affiliation(s)
- Laure Fournier
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
| | - Lena Costaridou
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nicolas Michoux
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Frederic E Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Ronald Boellaard
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
| | - Daniela E Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
| | - Nancy A Obuchowski
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Caroli
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Wolfgang G Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - James P B O'Connor
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Marius E Mayerhoefer
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Manuela Franca
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Angel Alberich-Bayarri
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
| | - Christophe M Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Christian Loewe
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cardiovascular and Interventional Radiology, Dept. for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rashindra Manniesing
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, Humanitas Clinical and Research Hospital - IRCCS, Rozzano, MI, Italy
| | - Nathalie Lassau
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Imaging Department, Gustave Roussy Cancer Campus Grand, Paris, UMR 1281, INSERM, CNRS, CEA, Universite Paris-Saclay, Saint-Aubin, France
| | - Anders Persson
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rik Achten
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Medical Imaging, Ghent University Hospital, Gent, Belgium
| | - Karen Rosendahl
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital of North Norway, Tromsø, Norway
| | - Olivier Clement
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Elmar Kotter
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Xavier Golay
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marion Smits
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marc Dewey
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel C Sullivan
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Dept. of Radiology, Duke University, 311 Research Dr, Durham, NC, 27710, USA
| | - Aad van der Lugt
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Nandita M deSouza
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria.
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium.
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
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Chen H, Li C, Zheng L, Lu W, Li Y, Wei Q. A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters. Cancer Med 2021; 10:2774-2786. [PMID: 33760360 PMCID: PMC8026951 DOI: 10.1002/cam4.3838] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/02/2020] [Accepted: 02/23/2021] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high-grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians when developing therapeutic strategies. Therefore, we propose a machine learning-based survival prediction model by analyzing clinical and dose-volume histogram (DVH) parameters, to improve the performance of the risk model in HGG patients. METHODS Eight clinical variables and 39 DVH parameters were extracted for each patient, who received radiotherapy for HGG with active follow-up. Ninety-five patients were randomly divided into training and testing cohorts, and we employed random survival forest (RSF), support vector machine (SVM), and Cox proportional hazards (CPHs) models to predict survival. Calibration plots, concordance indexes, and decision curve analyses were used to evaluate the calibration, discrimination, and clinical utility of these three models. RESULTS The RSF model showed the best performance among the three models, with concordance indexes of 0.824 and 0.847 in the training and testing sets, respectively, followed by the SVM (0.792/0.823) and CPH (0.821/0.811) models. Specifically, in the RSF model, we identified age, gross tumor volume (GTV), grade, Karnofsky performance status (KPS), isocitrate dehydrogenase (IDH), and D99 as important variables associated with survival. The AUCs of the testing set were 92.4%, 87.7%, and 84.0% for 1-, 2-, and 3-year survival, respectively. According to this model, HGG patients can be divided into high- and low-risk groups. CONCLUSION The machine learning-based RSF model integrating both clinical and DVH variables is an improved and useful tool for predicting the survival of HGG patients.
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Affiliation(s)
- Haiyan Chen
- Department of Radiation OncologyKey Laboratory of Cancer Prevention and InterventionMinistry of EducationThe Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiangChina
- Zhejiang University Cancer CenterHangzhouZhejiangChina
| | - Chao Li
- Department of Radiation OncologyKey Laboratory of Cancer Prevention and InterventionMinistry of EducationThe Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiangChina
| | - Lin Zheng
- Department of Radiation OncologyKey Laboratory of Cancer Prevention and InterventionMinistry of EducationThe Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiangChina
- Department of Radiation OncologyTaizhou Tumor HospitalTaizhouZhejiangChina
| | - Wei Lu
- Zhejiang University Cancer CenterHangzhouZhejiangChina
- Department of Colorectal Surgery and OncologyKey Laboratory of Cancer Prevention and InterventionMinistry of EducationThe Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiangChina
| | - Yanlin Li
- College of ScienceHangzhou Normal UniversityHangzhouZhejiangChina
| | - Qichun Wei
- Department of Radiation OncologyKey Laboratory of Cancer Prevention and InterventionMinistry of EducationThe Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiangChina
- Zhejiang University Cancer CenterHangzhouZhejiangChina
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24
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Priya S, Agarwal A, Ward C, Locke T, Monga V, Bathla G. Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models. Neuroradiol J 2021; 34:355-362. [PMID: 33533273 DOI: 10.1177/1971400921990766] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. METHODS We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. RESULTS The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. CONCLUSIONS First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.
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Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Hospitals and Clinics, USA
| | - Amit Agarwal
- Department of Radiology, UT Southwestern Medical Center, USA
| | - Caitlin Ward
- Department of Biostatistics, College of Public Health, University of Iowa Hospitals and Clinics, USA
| | - Thomas Locke
- Department of Radiology, University of Iowa Hospitals and Clinics, USA
| | - Varun Monga
- Division of Hematology, Oncology, Department of Internal Medicine, University of Iowa Hospitals and Clinics, USA
| | - Girish Bathla
- Department of Radiology, University of Iowa Hospitals and Clinics, USA
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25
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Yan M, Wang W. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Sci Prog 2021; 104:36850421997295. [PMID: 33687294 PMCID: PMC10453712 DOI: 10.1177/0036850421997295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
To predict the volume change of stage III NSCLC after radiotherapy with 60 Gy.This retrospective study included two independent cohorts, a train cohort of 192 patients, and a test cohort of 31 patients. We developed a radiomics model based on radiomics features and clinical variables. LIFEx package was used to extract radiomics texture features from CT images. The classification method was logistic regression analysis and feature selection was performed by correlation coefficients. Performance metrics of logistic regression include accuracy, precision, the receiver operating characteristic curves, and recall.The combination features of clinical variables and radiomics can predict the tumor volume change after radiotherapy with 88.7% accuracy (88.6% precision, 88.7% recall, and 88.7% ROC area).Radiomics features combined with medical knowledge have a great potential to predict accurately tumor volume change of stage III NSCLC after radiotherapy with 60 Gy.
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Affiliation(s)
- Mengmeng Yan
- Urban Vocational College of Sichuan, Chengdu, China
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Weidong Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
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26
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Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol 2020; 72:238-250. [PMID: 32371013 DOI: 10.1016/j.semcancer.2020.04.002] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 12/15/2022]
Abstract
Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions' identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC.
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Affiliation(s)
- Allegra Conti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Iole Indovina
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Medicine and Surgery, Saint Camillus International University of Health and Medical Sciences, Rome, Italy
| | - Maria Guerrisi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States.
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Kofler F, Berger C, Waldmannstetter D, Lipkova J, Ezhov I, Tetteh G, Kirschke J, Zimmer C, Wiestler B, Menze BH. BraTS Toolkit: Translating BraTS Brain Tumor Segmentation Algorithms Into Clinical and Scientific Practice. Front Neurosci 2020; 14:125. [PMID: 32410929 PMCID: PMC7201293 DOI: 10.3389/fnins.2020.00125] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 01/31/2020] [Indexed: 01/01/2023] Open
Abstract
Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major challenge. Several factors impede successful implementations, including data standardization and preprocessing. However, these steps are pivotal for the deployment of state-of-the-art image segmentation algorithms. To overcome these issues, we present BraTS Toolkit. BraTS Toolkit is a holistic approach to brain tumor segmentation and consists of three components: First, the BraTS Preprocessor facilitates data standardization and preprocessing for researchers and clinicians alike. It covers the entire image analysis workflow prior to tumor segmentation, from image conversion and registration to brain extraction. Second, BraTS Segmentor enables orchestration of BraTS brain tumor segmentation algorithms for generation of fully-automated segmentations. Finally, Brats Fusionator can combine the resulting candidate segmentations into consensus segmentations using fusion methods such as majority voting and iterative SIMPLE fusion. The capabilities of our tools are illustrated with a practical example to enable easy translation to clinical and scientific practice.
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Affiliation(s)
- Florian Kofler
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany.,Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Christoph Berger
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Diana Waldmannstetter
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jana Lipkova
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Giles Tetteh
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jan Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Bjoern H Menze
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
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Vollmann-Zwerenz A, Leidgens V, Feliciello G, Klein CA, Hau P. Tumor Cell Invasion in Glioblastoma. Int J Mol Sci 2020; 21:E1932. [PMID: 32178267 PMCID: PMC7139341 DOI: 10.3390/ijms21061932] [Citation(s) in RCA: 150] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a particularly devastating tumor with a median survival of about 16 months. Recent research has revealed novel insights into the outstanding heterogeneity of this type of brain cancer. However, all GBM subtypes share the hallmark feature of aggressive invasion into the surrounding tissue. Invasive glioblastoma cells escape surgery and focal therapies and thus represent a major obstacle for curative therapy. This review aims to provide a comprehensive understanding of glioma invasion mechanisms with respect to tumor-cell-intrinsic properties as well as cues provided by the microenvironment. We discuss genetic programs that may influence the dissemination and plasticity of GBM cells as well as their different invasion patterns. We also review how tumor cells shape their microenvironment and how, vice versa, components of the extracellular matrix and factors from non-neoplastic cells influence tumor cell motility. We further discuss different research platforms for modeling invasion. Finally, we highlight the importance of accounting for the complex interplay between tumor cell invasion and treatment resistance in glioblastoma when considering new therapeutic approaches.
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Affiliation(s)
- Arabel Vollmann-Zwerenz
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| | - Verena Leidgens
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| | - Giancarlo Feliciello
- Fraunhofer-Institute for Toxicology and Experimental Medicine, Division of Personalized Tumor Therapy, 93053 Regensburg, Germany; (G.F.); (C.A.K.)
| | - Christoph A. Klein
- Fraunhofer-Institute for Toxicology and Experimental Medicine, Division of Personalized Tumor Therapy, 93053 Regensburg, Germany; (G.F.); (C.A.K.)
- Experimental Medicine and Therapy Research, University of Regensburg, 93053 Regensburg, Germany
| | - Peter Hau
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
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