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Ban QQ, Zhang HT, Wang W, Du YF, Zhao Y, Peng AJ, Qu H. Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT. AJNR Am J Neuroradiol 2024; 45:1260-1268. [PMID: 39025637 PMCID: PMC11392366 DOI: 10.3174/ajnr.a8301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/03/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND AND PURPOSE Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT. MATERIALS AND METHODS This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification. RESULTS The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model (P = .002) and the clinical features model (P = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model (P = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features (P = .028; P = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron (P < .001) and the clinical-radiomics model with logistic regression (P = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration. CONCLUSIONS The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies.
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Affiliation(s)
- Qi-Qi Ban
- From the Department of Radiology (Q.-q.B., W.W., Y.Z., H.Q.), Affiliated Hospital of Yangzhou University, Yangzhou, China
- College of Medical Imaging (Q.-q.B., Y.-f.D.), Dalian Medical University, Dalian, China
| | - Hao-Tian Zhang
- Department of Industrial and Systems Engineering (H.-t.Z.), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China
| | - Wei Wang
- From the Department of Radiology (Q.-q.B., W.W., Y.Z., H.Q.), Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Yi-Fan Du
- College of Medical Imaging (Q.-q.B., Y.-f.D.), Dalian Medical University, Dalian, China
| | - Yi Zhao
- From the Department of Radiology (Q.-q.B., W.W., Y.Z., H.Q.), Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Ai-Jun Peng
- Department of Neurosurgery (A.-j.P.), Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Hang Qu
- From the Department of Radiology (Q.-q.B., W.W., Y.Z., H.Q.), Affiliated Hospital of Yangzhou University, Yangzhou, China
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Badesha AS, Frood R, Bailey MA, Coughlin PM, Scarsbrook AF. A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease. Tomography 2024; 10:1455-1487. [PMID: 39330754 PMCID: PMC11435603 DOI: 10.3390/tomography10090108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 08/27/2024] [Accepted: 08/30/2024] [Indexed: 09/28/2024] Open
Abstract
BACKGROUND Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease. METHODS MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted. RESULTS Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning. CONCLUSION Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.
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Affiliation(s)
- Arshpreet Singh Badesha
- Department of Radiology, St. James's University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK
| | - Russell Frood
- Department of Radiology, St. James's University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK
- Faculty of Medicine and Health, University of Leeds, Leeds LS2 9TJ, UK
| | - Marc A Bailey
- Faculty of Medicine and Health, University of Leeds, Leeds LS2 9TJ, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX, UK
| | - Patrick M Coughlin
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX, UK
| | - Andrew F Scarsbrook
- Department of Radiology, St. James's University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK
- Faculty of Medicine and Health, University of Leeds, Leeds LS2 9TJ, UK
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Zhu S, Lin L, Liu Q, Liu J, Song Y, Xu Q. Integrating a deep neural network and Transformer architecture for the automatic segmentation and survival prediction in cervical cancer. Quant Imaging Med Surg 2024; 14:5408-5419. [PMID: 39144008 PMCID: PMC11320496 DOI: 10.21037/qims-24-560] [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: 03/19/2024] [Accepted: 05/24/2024] [Indexed: 08/16/2024]
Abstract
Background Automated tumor segmentation and survival prediction are critical to clinical diagnosis and treatment. This study aimed to develop deep-learning models for automatic tumor segmentation and survival prediction in magnetic resonance imaging (MRI) of cervical cancer (CC) by combining deep neural networks and Transformer architecture. Methods This study included 406 patients with CC, each with comprehensive clinical information and MRI scans. We randomly divided patients into training, validation, and independent test cohorts in a 6:2:2 ratio. During the model training, we employed two architecture types: one being a hybrid model combining convolutional neural network (CNN) and ransformer (CoTr) and one of pure CNNs. For survival prediction, the hybrid model combined tumor image features extracted by segmentation models with clinical information. The performance of the segmentation models was evaluated using the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). The performance of the survival models was assessed using the concordance index. Results The CoTr model performed well in both contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) imaging segmentation tasks, with average DSCs of 0.827 and 0.820, respectively, which outperformed other the CNN models such as U-Net (DSC: 0.807 and 0.808), attention U-Net (DSC: 0.814 and 0.811), and V-Net (DSC: 0.805 and 0.807). For survival prediction, the proposed deep-learning model significantly outperformed traditional methods, yielding a concordance index of 0.732. Moreover, it effectively divided patients into low-risk and high-risk groups for disease progression (P<0.001). Conclusions Combining Transformer architecture with a CNN can improve MRI tumor segmentation, and this deep-learning model excelled in the survival prediction of patients with CC as compared to traditional methods.
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Affiliation(s)
- Shitao Zhu
- College of Computer and Data Science, Fuzhou University, Fuzhou, China
| | - Ling Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Qin Liu
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jing Liu
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Yanwen Song
- Department of Radiation Oncology, Xiamen Humanity Hospital, Xiamen, China
| | - Qin Xu
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
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Peeken JC, Etzel L, Tomov T, Münch S, Schüttrumpf L, Shaktour JH, Kiechle J, Knebel C, Schaub SK, Mayr NA, Woodruff HC, Lambin P, Gersing AS, Bernhardt D, Nyflot MJ, Menze B, Combs SE, Navarro F. Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas. Radiother Oncol 2024; 197:110338. [PMID: 38782301 DOI: 10.1016/j.radonc.2024.110338] [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: 07/30/2023] [Revised: 05/05/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), German Research Center for Environmental Health GmbH, Neuherberg, Germany; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany.
| | - Tim Tomov
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Lars Schüttrumpf
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Julius H Shaktour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Johannes Kiechle
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
| | - Carolin Knebel
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Stephanie K Schaub
- Department of Radiation Oncology, University of Washington, Seattle, USA
| | - Nina A Mayr
- College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Henry C Woodruff
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands; Department of Radiology and Nuclear Imaging, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, the Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands; Department of Radiology and Nuclear Imaging, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, the Netherlands
| | | | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Matthew J Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, USA; Department of Radiology, University of Washington, Seattle, USA
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), German Research Center for Environmental Health GmbH, Neuherberg, Germany
| | - Fernando Navarro
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
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Faustino R, Lopes C, Jantarada A, Mendonça A, Raposo R, Ferrão C, Freitas J, Mateus C, Pinto A, Almeida E, Gomes N, Marques L, Palavra F. Neuroimaging characterization of multiple sclerosis lesions in pediatric patients: an exploratory radiomics approach. Front Neurosci 2024; 18:1294574. [PMID: 38370435 PMCID: PMC10869542 DOI: 10.3389/fnins.2024.1294574] [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: 09/14/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction Multiple sclerosis (MS), a chronic inflammatory immune-mediated disease of the central nervous system (CNS), is a common condition in young adults, but it can also affect children. The aim of this study was to construct radiomic models of lesions based on magnetic resonance imaging (MRI, T2-weighted-Fluid-Attenuated Inversion Recovery), to understand the correlation between extracted radiomic features, brain and lesion volumetry, demographic, clinical and laboratorial data. Methods The neuroimaging data extracted from eleven scans of pediatric MS patients were analyzed. A total of 60 radiomic features based on MR T2-FLAIR images were extracted and used to calculate gray level co-occurrence matrix (GLCM). The principal component analysis and ROC analysis were performed to select the radiomic features, respectively. The realized classification task by the logistic regression models was performed according to these radiomic features. Results Ten most relevant features were selected from data extracted. The logistic regression applied to T2-FLAIR radiomic features revealed significant predictor for multiple sclerosis (MS) lesion detection. Only the variable "contrast" was statistically significant, indicating that only this variable played a significant role in the model. This approach enhances the classification of lesions from normal tissue. Discussion and conclusion Our exploratory results suggest that the radiomic models based on MR imaging (T2-FLAIR) may have a potential contribution to characterization of brain tissues and classification of lesions in pediatric MS.
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Affiliation(s)
- Ricardo Faustino
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
- Faculty of Science, Institute of Biophysics and Biomedical Engineering, University of Lisbon, Lisbon, Portugal
- Biomedical Research Group, Faculty of Engineering, Faculty of Veterinary Medicine NICiTeS, Lusófona University, Lisbon, Portugal
| | - Cristina Lopes
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Afonso Jantarada
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ana Mendonça
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Rafael Raposo
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Cristina Ferrão
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Joana Freitas
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Constança Mateus
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ana Pinto
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ellen Almeida
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Nuno Gomes
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Liliana Marques
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Filipe Palavra
- Centre for Child Development – Neuropediatrics Unit, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Laboratory of Pharmacology and Experimental Therapeutics, Faculty of Medicine, Coimbra Institute for Clinical and Biomedical Research, University of Coimbra, Coimbra, Portugal
- Clinical Academic Center of Coimbra, Coimbra, Portugal
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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Pacella G, Brunese MC, D’Imperio E, Rotondo M, Scacchi A, Carbone M, Guerra G. Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis. J Clin Med 2023; 12:7380. [PMID: 38068432 PMCID: PMC10707069 DOI: 10.3390/jcm12237380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 09/10/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. METHODS The PubMed database was searched for papers published in the English language no earlier than January 2018. RESULTS We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. CONCLUSIONS It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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Affiliation(s)
- Giulia Pacella
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | - Maria Chiara Brunese
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | | | - Marco Rotondo
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | - Andrea Scacchi
- General Surgery Unit, University of Milano-Bicocca, 20126 Milan, Italy
| | - Mattia Carbone
- San Giovanni di Dio e Ruggi d’Aragona Hospital, 84131 Salerno, Italy;
| | - Germano Guerra
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
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Mărginean L, Filep RC, Suciu BA, Jovin TG, Ștefan PA, Lupean RA, Arbănași EM, Arbănași EM, Opriș DR, Timm AN, Vodă R, Vunvulea V. Textural Analysis of the Hyperdense Artery Sign in Patients with Acute Ischemic Stroke Predicts the Outcome of Thrombectomy. J Cardiovasc Dev Dis 2023; 10:359. [PMID: 37754788 PMCID: PMC10532176 DOI: 10.3390/jcdd10090359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/03/2023] [Accepted: 08/17/2023] [Indexed: 09/28/2023] Open
Abstract
Textural analysis is pivotal in augmenting the diagnosis and outcomes of endovascular procedures for stroke patients. Due to the detection of changes imperceptible to the human eye, this type of analysis can potentially aid in deciding the optimal type of endovascular treatment. We included 40 patients who suffered from acute ischemic stroke caused by large vessel occlusion, and calculated 130 different textural features based on the non-enhanced CT scan using an open-source software (3D Slicer). Using chi-squared and Mann-Whitney tests and receiver operating characteristics analysis, we identified a total of 21 different textural parameters capable of predicting the outcome of thrombectomy (quantified as the mTICI score), with variable sensitivity (50-97.9%) and specificity (64.6-99.4%) rates. In conclusion, CT-based radiomics features are potential factors that can predict the outcome of thrombectomy in patients suffering from acute ischemic stroke, aiding in the decision between aspiration, mechanical, or combined thrombectomy procedure.
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Affiliation(s)
- Lucian Mărginean
- Radiology and Medical Imaging, Clinical Sciences Department, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
- Interventional Radiology Department, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
| | - Rares Cristian Filep
- Interventional Radiology Department, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
| | - Bogdan Andrei Suciu
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Tudor G. Jovin
- Cooper Neurological Institute, Cherry Hill, NJ 08002, USA;
| | - Paul-Andrei Ștefan
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș, 400012 Cluj-Napoca, Romania
- Radiology and Imaging Department, County Emergency Hospital, Clinicilor Street, Number 3–5, 400006 Cluj-Napoca, Romania
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, Number 4, 400349 Cluj-Napoca, Romania;
| | - Eliza Mihaela Arbănași
- Faculty of Pharmacy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Emil Marian Arbănași
- Clinic of Vascular Surgery, Mures County Emergency Hospital, 540136 Targu Mures, Romania
- Center for Advanced Medical and Pharmaceutical Research, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Diana Roxana Opriș
- Emergency Institute for Cardiovascular Diseases and Transplantation (IUBCVT) of Târgu Mureș, 540136 Targu Mures, Romania
| | - Alexander Niklas Timm
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Rareș Vodă
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Vlad Vunvulea
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
- Radiology and Medical Imaging Laboratory, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
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