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Du D, Shiri I, Yousefirizi F, Salmanpour MR, Lv J, Wu H, Zhu W, Zaidi H, Lu L, Rahmim A. Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes. EJNMMI Phys 2025; 12:34. [PMID: 40192981 PMCID: PMC11977052 DOI: 10.1186/s40658-025-00750-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Accepted: 03/24/2025] [Indexed: 04/10/2025] Open
Abstract
BACKGROUND Medical imaging data frequently encounter image-generation heterogeneity and class imbalance properties, challenging strong generalized predictive performances with data-driven machine-learning methods. The purpose of this study was to investigate the impact of harmonization and oversampling methods on multi-center imbalanced datasets, with specific application to PET-based radiomics modeling for histologic subtype prediction in non-small cell lung cancer (NSCLC). METHODS The retrospective study included 245 patients with adenocarcinoma (ADC) and 78 patients with squamous cell carcinoma (SCC) from 4 centers. Utilizing 1502 radiomics features per patient, we trained, validated, and tested 4 machine-learning classifiers, to investigate the effect of no harmonization (NoH) or 4 feature harmonization methods, paired with no oversampling (NoO) or 5 oversampling methods on subtype prediction. Model performance was evaluated using the average area under the ROC curve (AUROC) and G-mean via 5 times 5-fold cross-validations. Statistical comparisons of the combined models against baseline (NoH + NoO) were performed for each fold of cross-validation using the DeLong test. RESULTS The number of cross-combinations with both AUROC and G-mean outperforming baseline in validation and testing was 15, 4, 2, and 7 (out of 29) for random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM), respectively. ComBat harmonization combined with oversampling (SMOTE) via RF yielded better performance than baseline (AUROC and G-mean of validation: 0.725 vs. 0.608 and 0.625 vs. 0.398; testing: 0.637 vs. 0.567 and 0.506 vs. 0.287), though statistical significances were not observed. CONCLUSIONS Applying harmonization and oversampling methods in multi-center imbalanced datasets can improve NSCLC-subtype prediction, but the effect varies widely across classifiers. We have created open-source comparisons of harmonization and oversampling on different classifiers for comprehensive evaluations in different studies.
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Affiliation(s)
- Dongyang Du
- College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, 010021, China
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, V5Z 1L3, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, V5Z 1L3, Canada
| | - Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, V5Z 1L3, Canada
| | - Jieqin Lv
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Huiqin Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, 311121, China
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Lijun Lu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Pazhou Lab, Guangzhou, Guangdong, 510330, China.
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, V5Z 1M9, Canada
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2
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Barzegar-Golmoghani E, Mohebi M, Gohari Z, Aram S, Mohammadzadeh A, Firouznia S, Shakiba M, Naghibi H, Moradian S, Ahmadi M, Almasi K, Issaiy M, Anjomrooz M, Tavangar SM, Javadi S, Bitarafan-Rajabi A, Davoodi M, Sharifian H, Mohammadzadeh M. ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning. Sci Rep 2025; 15:8763. [PMID: 40082527 PMCID: PMC11906654 DOI: 10.1038/s41598-025-93226-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2024] [Accepted: 03/05/2025] [Indexed: 03/16/2025] Open
Abstract
Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.
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Affiliation(s)
| | - Mobin Mohebi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
- Institut de Biologie Valrose (IBV), Université Côte d'Azur, CNRS, Inserm, Nice, France
| | - Zahra Gohari
- Department of Radiology, Tehran University of Medical Science, Tehran, Iran
| | - Sadaf Aram
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ali Mohammadzadeh
- Department of Radiology, Iran University of Medical Sciences, Tehran, Iran
| | - Sina Firouznia
- Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Madjid Shakiba
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Naghibi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Sadegh Moradian
- Department of Radiology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Maryam Ahmadi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Kazhal Almasi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mahbod Issaiy
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Mehran Anjomrooz
- Department of Radiology, Tehran University of Medical Science, Tehran, Iran
| | | | - Sheida Javadi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Rajaie Cardiovascular Medical and Research Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Davoodi
- Department of Radiology, Tehran University of Medical Science, Tehran, Iran.
| | - Hashem Sharifian
- Department of Radiology, Tehran University of Medical Science, Tehran, Iran.
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Salimi Y, Hajianfar G, Mansouri Z, Sanaat A, Amini M, Shiri I, Zaidi H. Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning. Clin Nucl Med 2024; 49:899-908. [PMID: 39192505 DOI: 10.1097/rlu.0000000000005400] [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: 08/29/2024]
Abstract
PURPOSE Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms. PATIENTS AND METHODS This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric. RESULTS For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them. CONCLUSIONS The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.
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Affiliation(s)
- Yazdan Salimi
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Amirhosein Sanaat
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Mehdi Amini
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
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Shiri I, Amini M, Yousefirizi F, Vafaei Sadr A, Hajianfar G, Salimi Y, Mansouri Z, Jenabi E, Maghsudi M, Mainta I, Becker M, Rahmim A, Zaidi H. Information fusion for fully automated segmentation of head and neck tumors from PET and CT images. Med Phys 2024; 51:319-333. [PMID: 37475591 DOI: 10.1002/mp.16615] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi-modal information available are still lacking. PURPOSE Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output-level voting-based fusions. METHODS The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image-level fusions were implemented. In addition, a modified U2-Net architecture as DL fusion model baseline was used. Three different input, layer, and decision-level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image-level and network-level fusions), that is, output-level information fusion (voting-based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. RESULTS In single modalities, PET had a reasonable performance with a Dice score of 0.77 ± 0.09, while CT did not perform acceptably and reached a Dice score of only 0.38 ± 0.22. Conventional fusion algorithms obtained a Dice score range of [0.76-0.81] with guided-filter-based context enhancement (GFCE) at the low-end, and anisotropic diffusion and Karhunen-Loeve transform fusion (ADF), multi-resolution singular value decomposition (MSVD), and multi-level image decomposition based on latent low-rank representation (MDLatLRR) at the high-end. All DL fusion models achieved Dice scores of 0.80. Output-level voting-based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak , SUVmean and SUVmedian . CONCLUSION PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET-only and CT-only methods. In addition, both conventional image-level and DL fusions achieve competitive results. Meanwhile, output-level voting-based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, USA
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ismini Mainta
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Minerva Becker
- Service of Radiology, Geneva University Hospital, Geneva, Switzerland
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
- Department of Radiology and Physics, University of British Columbia, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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5
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Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
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Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
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Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [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: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
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Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
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Hajianfar G, Haddadi Avval A, Hosseini SA, Nazari M, Oveisi M, Shiri I, Zaidi H. Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics. LA RADIOLOGIA MEDICA 2023; 128:1521-1534. [PMID: 37751102 PMCID: PMC10700216 DOI: 10.1007/s11547-023-01725-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. MATERIALS AND METHODS One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. RESULTS Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). CONCLUSION ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | | | - Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
| | - Mostafa Nazari
- Department of Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Clin Oncol (R Coll Radiol) 2023; 35:713-725. [PMID: 37599160 DOI: 10.1016/j.clon.2023.08.003] [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: 09/27/2022] [Revised: 06/10/2023] [Accepted: 08/05/2023] [Indexed: 08/22/2023]
Abstract
AIMS We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.
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Affiliation(s)
- Z Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - M Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - G Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - M Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - I Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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9
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Zhan F, He L, Yu Y, Chen Q, Guo Y, Wang L. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. Sci Rep 2023; 13:16397. [PMID: 37773310 PMCID: PMC10541909 DOI: 10.1038/s41598-023-43543-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
We developed and validated a multimodal radiomic machine learning approach to noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase (LCK) expression and clinical prognosis of patients with high-grade serous ovarian cancer (HGSOC). We analyzed gene enrichment using 343 HGSOC cases extracted from The Cancer Genome Atlas. The corresponding biomedical computed tomography images accessed from The Cancer Imaging Archive were used to construct the radiomic signature (Radscore). A radiomic nomogram was built by combining the Radscore and clinical and genetic information based on multimodal analysis. We compared the model performances and clinical practicability via area under the curve (AUC), Kaplan-Meier survival, and decision curve analyses. LCK mRNA expression was associated with the prognosis of HGSOC patients, serving as a significant prognostic marker of the immune response and immune cells infiltration. Six radiomic characteristics were chosen to predict the expression of LCK and overall survival (OS) in HGSOC patients. The logistic regression (LR) radiomic model exhibited slightly better predictive abilities than the support vector machine model, as assessed by comparing combined results. The performance of the LR radiomic model for predicting the level of LCK expression with five-fold cross-validation achieved AUCs of 0.879 and 0.834, respectively, in the training and validation sets. Decision curve analysis at 60 months demonstrated the high clinical utility of our model within thresholds of 0.25 and 0.7. The radiomic nomograms were robust and displayed effective calibration. Abnormally high expression of LCK in HGSOC patients is significantly correlated with the tumor immune microenvironment and can be used as an essential indicator for predicting the prognosis of HGSOC. The multimodal radiomic machine learning approach can capture the heterogeneity of HGSOC, noninvasively predict the expression of LCK, and replace LCK for predictive analysis, providing a new idea for predicting the clinical prognosis of HGSOC and formulating a personalized treatment plan.
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Affiliation(s)
- Feng Zhan
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
- College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, People's Republic of China
| | - Lidan He
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Yuanlin Yu
- Department of Medical Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Qian Chen
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
| | - Yina Guo
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China.
| | - Lili Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
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10
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Amini M, Pursamimi M, Hajianfar G, Salimi Y, Saberi A, Mehri-Kakavand G, Nazari M, Ghorbani M, Shalbaf A, Shiri I, Zaidi H. Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study. Sci Rep 2023; 13:14920. [PMID: 37691039 PMCID: PMC10493219 DOI: 10.1038/s41598-023-42142-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023] Open
Abstract
This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models' evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mohamad Pursamimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdi Ghorbani
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- University Research and Innovation Center, Obuda University, Budapest, Hungary.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University of Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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11
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Mohebi M, Amini M, Alemzadeh-Ansari MJ, Alizadehasl A, Rajabi AB, Shiri I, Zaidi H, Orooji M. Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study. J Digit Imaging 2023; 36:1348-1363. [PMID: 37059890 PMCID: PMC10407007 DOI: 10.1007/s10278-023-00820-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/16/2023] Open
Abstract
In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient's scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.
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Affiliation(s)
- Mobin Mohebi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | | | - Azin Alizadehasl
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Cardio-Oncology Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan Rajabi
- Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
- Geneva University Neuro Center, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mahdi Orooji
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
- Department of Electrical and Computer Engineering, University of California–Davis, Davis, CA USA
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12
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Samimi R, Shiri I, Ahmadyar Y, van den Hoff J, Kamali-Asl A, Rezaee A, Yousefirizi F, Geramifar P, Rahmim A. Radiomics predictive modeling from dual-time-point FDG PET K i parametric maps: application to chemotherapy response in lymphoma. EJNMMI Res 2023; 13:70. [PMID: 37493872 PMCID: PMC10371962 DOI: 10.1186/s13550-023-01022-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [18F]FDG PET metabolic uptake rate Ki parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients. METHODS We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP Ki parametric maps. Spearman's rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets. RESULTS Via Spearman's rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann-Whitney test). CONCLUSIONS Our results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.
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Affiliation(s)
- Rezvan Samimi
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Yashar Ahmadyar
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | - Jörg van den Hoff
- PET Center, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, 01328, Dresden, Germany
- Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307, Dresden, Germany
| | - Alireza Kamali-Asl
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
| | | | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
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13
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Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artif Intell Med 2023; 141:102572. [PMID: 37295902 DOI: 10.1016/j.artmed.2023.102572] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/16/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Danial Javaheri
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
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14
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Noortman WA, Aide N, Vriens D, Arkes LS, Slump CH, Boellaard R, Goeman JJ, Deroose CM, Machiels JP, Licitra LF, Lhommel R, Alessi A, Woff E, Goffin K, Le Tourneau C, Gal J, Temam S, Delord JP, van Velden FHP, de Geus-Oei LF. Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer. Cancers (Basel) 2023; 15:2681. [PMID: 37345017 DOI: 10.3390/cancers15102681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023] Open
Abstract
AIM To build and externally validate an [18F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). METHODS Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [18F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index). RESULTS In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82. CONCLUSION Although assessed in two small but independent cohorts, an [18F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.
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Affiliation(s)
- Wyanne A Noortman
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Nicolas Aide
- Nuclear Medicine Department, Centre Hospitalier Universitaire de Caen, 14000 Caen, France
| | - Dennis Vriens
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lisa S Arkes
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Technical Medicine, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Cornelis H Slump
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ronald Boellaard
- Amsterdam University Medical Center, 1081 HV Amsterdam, The Netherlands
| | - Jelle J Goeman
- Department of Biomedical Data Sciences, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Christophe M Deroose
- Nuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Jean-Pascal Machiels
- Department of Medical Oncology, Institut Roi Albert II, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium
- Institute for Experimental and Clinical Research (IREC, pôle MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Lisa F Licitra
- Department of Head and Neck Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, University of Milan, 20133 Milan, Italy
| | - Renaud Lhommel
- Division of Nuclear Medicine, Institut de Recherche Clinique, Cliniques Universitaires Saint Luc, 1200 Brussels, Belgium
| | - Alessandra Alessi
- Department of Nuclear Medicine-PET Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Erwin Woff
- Nuclear Medicine Department, Institut Jules Bordet, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), 1070 Bruxelles, Belgium
| | - Karolien Goffin
- Nuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation, Institut Curie, Paris-Saclay University, 75005 Paris, France
| | - Jocelyn Gal
- Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, University Côte d'Azur, 06100 Nice, France
| | - Stéphane Temam
- Department of Head and Neck Surgery Gustave Roussy, 94805 Villejuif, France
| | | | - Floris H P van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Radiation Science & Technology, Delft University of Technology, 2628 CD Delft, The Netherlands
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15
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Sabouri M, Hajianfar G, Hosseini Z, Amini M, Mohebi M, Ghaedian T, Madadi S, Rastgou F, Oveisi M, Bitarafan Rajabi A, Shiri I, Zaidi H. Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition. J Digit Imaging 2023; 36:497-509. [PMID: 36376780 PMCID: PMC10039187 DOI: 10.1007/s10278-022-00705-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/31/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study's final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning.
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Affiliation(s)
- Maziar Sabouri
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Zahra Hosseini
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mobin Mohebi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Tahereh Ghaedian
- Nuclear Medicine and Molecular Imaging Research Center, School of Medicine, Namazi Teaching Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shabnam Madadi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Fereydoon Rastgou
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Computer Science, University of British Columbia, Vancouver BC, Canada
| | - Ahmad Bitarafan Rajabi
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
- Cardiovascular Interventional Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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16
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Shiri I, Mostafaei S, Haddadi Avval A, Salimi Y, Sanaat A, Akhavanallaf A, Arabi H, Rahmim A, Zaidi H. High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms. Sci Rep 2022; 12:14817. [PMID: 36050434 PMCID: PMC9437017 DOI: 10.1038/s41598-022-18994-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/23/2022] [Indexed: 12/11/2022] Open
Abstract
We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805-0.887) and 0.807 (0.752-0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | | | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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18
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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: 1.3] [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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19
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Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar MR, Shayesteh SP, Khosravi B, Babaei MR, Jenabi E, Hasanian M, Shahhamzeh A, Foroghi Ghomi SY, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-Aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard AR, Arabi H, Rezaei-Kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med 2022; 145:105467. [PMID: 35378436 PMCID: PMC8964015 DOI: 10.1016/j.compbiomed.2022.105467] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qum, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran
| | - Ehsan Sharifipour
- Neuroscience Research Center, Qom University of Medical Sciences, Qum, Iran
| | - Saleh Sandoughdaran
- Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Bardia Khosravi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical Research Development Center, Qom University of Medical Sciences, Qum, Iran
| | - Seyaed Yaser Foroghi Ghomi
- Clinical Research Development Center, Shahid Beheshti Hospital, Qom University Of Medical Sciences, Qom, Iran
| | - Abolfazl Mozafari
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fatemeh Movaseghi
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Azin Ahmari
- Ayatolah Khansary Hospital, Arak University of Medical Sciences, Arak, Iran
| | - Neda Goharpey
- Department of Radiation Oncology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rama Bozorgmehr
- Clinical Research Development Unit, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Roozbeh Mortazavi
- Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Nazanin Mortazavi
- Dental Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Sima Besharat
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mandana Afsharpad
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Parham Geramifar
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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20
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:1329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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21
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Jena R, Dekker A, Kang J. A Glimmer of Hope Within the Mountain of Hype - Reviewing the Role of Artificial Intelligence in Radiotherapy. Clin Oncol (R Coll Radiol) 2021; 34:71-73. [PMID: 34924257 DOI: 10.1016/j.clon.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/03/2021] [Indexed: 11/03/2022]
Affiliation(s)
- R Jena
- Department of Oncology, University of Cambridge, Cambridge, UK.
| | - A Dekker
- MAASTRO Clinic, Maastricht, the Netherlands
| | - J Kang
- University of Washington Medical Center Montlake - Radiation Oncology Center, Seattle, Washington, USA
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22
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Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection. Comput Biol Med 2021; 141:105145. [PMID: 34929466 DOI: 10.1016/j.compbiomed.2021.105145] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Robust differentiation between infarcted and normal tissue is important for clinical diagnosis and precision medicine. The aim of this work is to investigate the radiomic features and to develop a machine learning algorithm for the differentiation of myocardial infarction (MI) and viable tissues/normal cases in the left ventricular myocardium on non-contrast Cine Cardiac Magnetic Resonance (Cine-CMR) images. METHODS Seventy-two patients (52 with MI and 20 healthy control patients) were enrolled in this study. MR imaging was performed on a 1.5 T MRI using the following parameters: TR = 43.35 ms, TE = 1.22 ms, flip angle = 65°, temporal resolution of 30-40 ms. N4 bias field correction algorithm was applied to correct the inhomogeneity of images. All images were segmented and verified simultaneously by two cardiac imaging experts in consensus. Subsequently, features extraction was performed within the whole left ventricular myocardium (3D volume) in end-diastolic volume phase. Re-sampling to 1 × 1 × 1 mm3 voxels was performed for MR images. All intensities within the VOI of MR images were discretized to 64 bins. Radiomic features were normalized to obtain Z-scores, followed by Student's t-test statistical analysis for comparison. A p-value < 0.05 was used as a threshold for statistically significant differences and false discovery rate (FDR) correction performed to report q-value (FDR adjusted p-value). The extracted features were ranked using the MSVM-RFE algorithm, then Spearman correlation between features was performed to eliminate highly correlated features (R2 > 0.80). Ten different machine learning algorithms were used for classification and different metrics used for evaluation and various parameters used for models' evaluation. RESULTS In univariate analysis, the highest area under the curve (AUC) of receiver operating characteristic (ROC) value was achieved for the Maximum 2D diameter slice (M2DS) shape feature (AUC = 0.88, q-value = 1.02E-7), while the average of univariate AUCs was 0.62 ± 0.08. In multivariate analysis, Logistic Regression (AUC = 0.93 ± 0.03, Accuracy = 0.86 ± 0.05, Recall = 0.87 ± 0.1, Precision = 0.93 ± 0.03 and F1 Score = 0.90 ± 0.04) and SVM (AUC = 0.92 ± 0.05, Accuracy = 0.85 ± 0.04, Recall = 0.92 ± 0.01, Precision = 0.88 ± 0.04 and F1 Score = 0.90 ± 0.02) yielded optimal performance as the best machine learning algorithm for this radiomics analysis. CONCLUSION This study demonstrated that using radiomics analysis on non-contrast Cine-CMR images enables to accurately detect MI, which could potentially be used as an alternative diagnostic method for Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR).
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