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Magoulianitis V, Yang J, Yang Y, Xue J, Kaneko M, Cacciamani G, Abreu A, Duddalwar V, Kuo CCJ, Gill IS, Nikias C. PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation. Comput Med Imaging Graph 2024; 116:102408. [PMID: 38908295 DOI: 10.1016/j.compmedimag.2024.102408] [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: 01/26/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/24/2024]
Abstract
Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.
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Affiliation(s)
- Vasileios Magoulianitis
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA.
| | - Jiaxin Yang
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Yijing Yang
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Jintang Xue
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Masatomo Kaneko
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Giovanni Cacciamani
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Andre Abreu
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Vinay Duddalwar
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - C-C Jay Kuo
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Inderbir S Gill
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Chrysostomos Nikias
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
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Lou F, Li M, Chu T, Duan H, Liu H, Zhang J, Duan K, Liu H, Wei F. Comprehensive analysis of clinical data and radiomic features from contrast enhanced CT for differentiating benign and malignant pancreatic intraductal papillary mucinous neoplasms. Sci Rep 2024; 14:17218. [PMID: 39060387 PMCID: PMC11282090 DOI: 10.1038/s41598-024-68067-6] [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: 04/19/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024] Open
Abstract
The primary aim of this investigation was to leverage radiomics features derived from contrast-enhanced abdominal computed tomography (CT) scans to devise a predictive model to discern the benign and malignant nature of intraductal papillary mucinous neoplasms (IPMNs). Radiomic signatures were meticulously crafted to delineate benign from malignant IPMNs by extracting pertinent features from contrast-enhanced CT images within a designated training cohort (n = 84). Subsequent validation was conducted with data from an independent test cohort (n = 37). The discriminative ability of the model was quantitatively evaluated through receiver operating characteristic (ROC) curve analysis, with the integration of carefully selected clinical features to improve the comparative analysis. Arterial-phase images were utilized to construct a model comprising 8 features for distinguishing between benign and malignant cases. The model achieved an accuracy of 0.891 [95% confidence interval (95% CI), 0.816-0.996] in the cross-validation set and 0.553 (95% CI 0.360-0.745) in the test set. Conversely, employing 9 features from the venous-phase resulted in a model with a cross-validation accuracy of 0.862 (95%CI 0.777-0.946) and a test set accuracy of 0.801 (95% CI 0.653-0.950).Integrating the identified clinical features with imaging features yielded a model with a cross-validation accuracy of 0.934 (95% CI 0.879-0.990) and a test set accuracy of 0.904 (95% CI 0.808-0.999), thereby further improving its discriminatory ability. Our findings distinctly illustrate that venous-phase radiomics features eclipse arterial-phase radiomic features in terms of predictive accuracy regarding the nature of IPMNs. Furthermore, the synthesis and meticulous screening of clinical features with radiomic data significantly increased the diagnostic efficacy of our model, underscoring the pivotal importance of a comprehensive and integrated approach for accurate risk stratification in IPMN management.
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Affiliation(s)
- Fengxiang Lou
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, 130000, China
| | - Tongjia Chu
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Haoyu Duan
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Huan Liu
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Jian Zhang
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Kehang Duan
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Han Liu
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130000, China.
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Xu L, Wu Y, Shen X, Zhou L, Lu Y, Teng Z, Du J, Ding M, Han H, Niu T. Exploring the biological basis of CT imaging features in pancreatic neuroendocrine tumors: a two-center study. Phys Med Biol 2024; 69:125013. [PMID: 38810631 DOI: 10.1088/1361-6560/ad51c7] [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: 01/08/2024] [Accepted: 05/29/2024] [Indexed: 05/31/2024]
Abstract
Objective.Medical imaging offered a non-invasive window to visualize tumors, with radiomics transforming these images into quantitative data for tumor phenotyping. However, the intricate web linking imaging features, clinical endpoints, and tumor biology was mostly uncharted. This study aimed to unravel the connections between CT imaging features and clinical characteristics, including tumor histopathological grading, clinical stage, and endocrine symptoms, alongside immunohistochemical markers of tumor cell growth, such as the Ki-67 index and nuclear mitosis rate.Approach.We conducted a retrospective analysis of data from 137 patients with pancreatic neuroendocrine tumors who had undergone contrast-enhanced CT scans across two institutions. Our study focused on three clinical factors: pathological grade, clinical stage, and endocrine symptom status, in addition to two immunohistochemical markers: the Ki-67 index and the rate of nuclear mitosis. We computed both predefined (2D and 3D) and learning-based features (via sparse autoencoder, or SAE) from the scans. To unearth the relationships between imaging features, clinical factors, and immunohistochemical markers, we employed the Spearman rank correlation along with the Benjamini-Hochberg method. Furthermore, we developed and validated radiomics signatures to foresee these clinical factors.Main results.The 3D imaging features showed the strongest relationships with clinical factors and immunohistochemical markers. For the association with pathological grade, the mean absolute value of the correlation coefficient (CC) of 2D, SAE, and 3D features was 0.3318 ± 0.1196, 0.2149 ± 0.0361, and 0.4189 ± 0.0882, respectively. While for the association with Ki-67 index and rate of nuclear mitosis, the 3D features also showed higher correlations, with CC as 0.4053 ± 0.0786 and 0.4061 ± 0.0806. In addition, the 3D feature-based signatures showed optimal performance in clinical factor prediction.Significance.We found relationships between imaging features, clinical factors, and immunohistochemical markers. The 3D features showed higher relationships with clinical factors and immunohistochemical markers.
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Affiliation(s)
- Lei Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, Guangdong, People's Republic of China
| | - Yan Wu
- Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Luping Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Yongkai Lu
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Ze Teng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, People's Republic of China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, People's Republic of China
| | - Hongbin Han
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
- Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Tianye Niu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, Guangdong, People's Republic of China
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, People's Republic of China
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Guo Q, Gao Y, Lin Y, Li W, Zhang Z, Mao Y, Xu X. A nomogram of preoperative indicators predicting lymph vascular space invasion in cervical cancer. Arch Gynecol Obstet 2024; 309:2079-2087. [PMID: 38358484 DOI: 10.1007/s00404-024-07385-6] [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: 09/24/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE To develop predictive nomograms of lymph vascular space invasion (LVSI) in patients with early-stage cervical cancer. METHODS We identified 403 patients with cervical cancer from the Affiliated Hospital of Jiangnan University from January 2015 to December 2019. Patients were divided into the training set (n = 242) and the validation set (n = 161), with patients in the training set subdivided into LVSI (+) and LVSI (-) groups according to postoperative pathology. Preoperative hematologic indexes were compared between the two subgroups. Univariate and multivariate logistic regression analyses were used to analyze the independent risk factors for LVSI, from which a nomogram was constructed using the R package. RESULTS LVSI (+) was present in 94 out of 242 patients in the training set, accompanied by a significant increase in the preoperative squamous cell carcinoma antigen (SCC), white blood cells (WBC), neutrophil (NE), platelet (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation index (SII), and tumor size (P < 0.05). Univariate analysis showed that SCC, WBC, NE, NLR, PLR, SII, and tumor size were correlated with LVSI (P < 0.05), and multivariate analysis showed that tumor size, SCC, WBC, and NLR were independent risk factors for LVSI (P < 0.05). A nomogram was correspondingly established with good performance in predicting LVSI [training: ROC-AUC = 0.845 (95% CI: 0.731-0.843) and external validation: ROC-AUC = 0.704 (95% CI: 0.683-0.835)] and high accuracy (training: C-index = 0.787; external validation: C-index = 0.759). CONCLUSION The nomogram based on preoperative tumor size, SCC, WBC, and NLR had excellent accuracy and discriminative capability to assess the risk of LVSI in early-stage cervical cancer patients.
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Affiliation(s)
- Qu Guo
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yufeng Gao
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi Medical College, Jiangnan University, Wuxi, China
| | - Yaying Lin
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi Medical College, Jiangnan University, Wuxi, China
| | - Weimin Li
- Ultrasonography Department, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhenyu Zhang
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yurong Mao
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xizhong Xu
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China.
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Chantadisai M, Wongwijitsook J, Ritlumlert N, Rakvongthai Y. Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients. Sci Rep 2024; 14:5001. [PMID: 38424177 PMCID: PMC10904821 DOI: 10.1038/s41598-024-55755-6] [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: 10/12/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).
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Affiliation(s)
- Maythinee Chantadisai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
| | - Jirarot Wongwijitsook
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Surin Hospital, Surin, Thailand
| | - Napat Ritlumlert
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
- School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Yothin Rakvongthai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Sheng L, Yang C, Chen Y, Song B. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors. Biomedicines 2023; 12:58. [PMID: 38255165 PMCID: PMC10813632 DOI: 10.3390/biomedicines12010058] [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: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Rahmim A, Toosi A, Salmanpour MR, Dubljevic N, Janzen I, Shiri I, Yuan R, Ho C, Zaidi H, MacAulay C, Uribe C, Yousefirizi F. Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features. Quant Imaging Med Surg 2023; 13:7680-7694. [PMID: 38106259 PMCID: PMC10722050 DOI: 10.21037/qims-23-163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 08/02/2023] [Indexed: 12/19/2023]
Abstract
Background Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g., bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels. Typically, only one set of parameters is employed, resulting in a single value or "flavour" for each radiomics feature. In contrast, we propose "tensor radiomics" (TR) where tensors of features calculated using multiple parameter combinations (i.e., flavours) are utilized to optimize the creation of radiomics signatures. Methods We provide illustrative instances of TR implementation in positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI), and CT by leveraging machine learning (ML) and deep learning (DL) methodologies, as well as reproducibility analyses: (I) to predict overall survival (OS) in lung cancer (CT) and head and neck cancer (PET-CT), TR was employed by varying bin sizes. This approach involved use of a hybrid deep neural network called 'TR-Net' and two ML-based techniques for combining different flavours. (II) TR was constructed by incorporating different segmentation perturbations and various bin sizes to classify the response of late-stage lung cancer to first-line immunotherapy using CT images. (III) In MRI of glioblastoma (GBM), TR was implemented to generate multi-flavour radiomics features, enabling enhanced analysis and interpretation. (IV) TR was employed via multiple PET-CT fusions in head and neck cancer. Flavours based on different fusions were created using Laplacian pyramids and wavelet transforms. Results Our findings demonstrated that TR outperformed conventional radiomics features in lung cancer CT and head and neck cancer PET-CT images, significantly enhancing OS prediction accuracy. TR also improved classification of lung cancer response to therapy and exhibited notable advantages in reproducibility compared to single-flavour features in MR imaging of GBM. Moreover, in head and neck cancer, TR through multiple PET-CT fusions exhibited improved performance in predicting OS. Conclusions We conclude that the proposed TR paradigm has significant potential to improve performance in different medical imaging tasks. By incorporating multiple flavours of radiomics features, TR overcomes limitations associated with individual features and shows promise in enhancing prognostic capabilities in clinical settings.
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Affiliation(s)
- Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada
| | - Amirhosein Toosi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada
| | | | - Natalia Dubljevic
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada
| | - Ian Janzen
- BC Cancer Research Institute, Vancouver, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ren Yuan
- BC Cancer Research Institute, Vancouver, Canada
| | - Cheryl Ho
- BC Cancer Research Institute, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | | | - Carlos Uribe
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada
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Crombé A, Spinnato P, Italiano A, Brisse HJ, Feydy A, Fadli D, Kind M. Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives. Diagn Interv Imaging 2023; 104:567-583. [PMID: 37802753 DOI: 10.1016/j.diii.2023.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France; 'Sarcotarget' team, BRIC INSERM U1312 and Bordeaux University, 33000 Bordeaux France.
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | | | | | - Antoine Feydy
- Department of Radiology, Hopital Cochin-AP-HP, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - David Fadli
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France
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Nguyen TM, Bertolus C, Giraud P, Burgun A, Saintigny P, Bibault JE, Foy JP. A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas. Cancers (Basel) 2023; 15:5369. [PMID: 38001629 PMCID: PMC10670096 DOI: 10.3390/cancers15225369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/05/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. METHOD We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. RESULTS A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. CONCLUSIONS AND RELEVANCE We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.
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Affiliation(s)
- Tan Mai Nguyen
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
| | - Chloé Bertolus
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
| | - Paul Giraud
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
- Sorbonne Université, Department of Radiation Oncology, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France
| | - Anita Burgun
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
| | - Pierre Saintigny
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
- Department of Medical Oncology, Centre Léon Bérard, 69008 Lyon, France
| | - Jean-Emmanuel Bibault
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
- Department of Radiation Oncology, Hôpital Européen Georges-Pompidou, Université Paris Cité, 75015 Paris, France
| | - Jean-Philippe Foy
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Sorbonne Université, INSERM UMRS 938, Centre de Recherche de Saint Antoine, Team Cancer Biology and Therapeutics, 75011 Paris, France
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10
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Zhou J, Yu B, Guo P, Wang S. The predictive value of the change of the number of pixels under different CT value intervals in the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. BMC Pulm Med 2023; 23:426. [PMID: 37924039 PMCID: PMC10623708 DOI: 10.1186/s12890-023-02732-w] [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: 08/12/2023] [Accepted: 10/24/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND Due to the fact that the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. (CT-occult CLSCC and SEPL) cannot be detected by lung CT screening, early and timely diagnosis of central lung cancer becomes very difficult, which directly affects the prognosis of patients. METHODS We retrospectively review medical records of patients at the Zhejiang Cancer Hospital and enrolled 41 patients with the CT-occult CLSCC and SEPL and 48 patients without the CT-occult CLSCC and SEPL. We compare the clinical characteristics, imaging features and Changes in the number of pixels under different CT value intervals of patients with and without the CT-occult CLSCC and SEPL and we perform univariate and multivariate logistic regression analysis to explore independent factors for the CT-occult CLSCC and SEPL in the patients. RESULTS We demonstrate that pack-years ≥ 20 (OR: 3.848, 95% CI: 1.086 ~ 13.633), the number of pixels change of CT value in interval [-850 ~ -750HU] (OR: 5.302, 95% CI: 1.122 ~ 25.057) and in interval [-900 ~ -850HU] (OR: 3.478, 95% CI: 1.167 ~ 10.365) are independently associated with the CT-occult CLSCC and SEPL in the patients. Ultimately, the logistic model obtained is statistically significant (p < 0.05) and an area under the ROC curve is 0.776 (95% CI: 0.682-0.870). The sensitivity of this model is 90.2% and the specificity is 52.1%. CONCLUSION The results of this study indicate that in the CT value range [-950 ~ -750HU], when the total number of lung pixels tend to increase towards the region with high CT value, the probability of the occurrence of CT-occult CLSCC and SEPL lesions also increases. Meanwhile, these results have guiding significance for the further study of radiomic.
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Affiliation(s)
- Jiaming Zhou
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Bijun Yu
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Peng Guo
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shi Wang
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China.
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11
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Niyoteka S, Seban RD, Rouhi R, Scarsbrook A, Genestie C, Classe M, Carré A, Sun R, La Greca Saint-Esteven A, Chargari C, McKenna J, McDermott G, Malinen E, Tanadini-Lang S, Guckenberger M, Guren MG, Lemanski C, Deutsch E, Robert C. A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers. Eur J Nucl Med Mol Imaging 2023; 50:4010-4023. [PMID: 37632562 DOI: 10.1007/s00259-023-06320-2] [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: 12/22/2022] [Accepted: 06/24/2023] [Indexed: 08/28/2023]
Abstract
Locally advanced cervical cancer (LACC) and anal and oropharyngeal squamous cell carcinoma (ASCC and OPSCC) are mostly caused by oncogenic human papillomaviruses (HPV). In this paper, we developed machine learning (ML) models based on clinical, biological, and radiomic features extracted from pre-treatment fluorine-18-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) images to predict the survival of patients with HPV-induced cancers. For this purpose, cohorts from five institutions were used: two cohorts of patients treated for LACC including 104 patients from Gustave Roussy Campus Cancer (Center 1) and 90 patients from Leeds Teaching Hospitals NHS Trust (Center 2), two datasets of patients treated for ASCC composed of 66 patients from Institut du Cancer de Montpellier (Center 3) and 67 patients from Oslo University Hospital (Center 4), and one dataset of 45 OPSCC patients from the University Hospital of Zurich (Center 5). Radiomic features were extracted from baseline [18F]-FDG PET images. The ComBat technique was applied to mitigate intra-scanner variability. A modified consensus nested cross-validation for feature selection and hyperparameter tuning was applied on four ML models to predict progression-free survival (PFS) and overall survival (OS) using harmonized imaging features and/or clinical and biological variables as inputs. Each model was trained and optimized on Center 1 and Center 3 cohorts and tested on Center 2, Center 4, and Center 5 cohorts. The radiomic-based CoxNet model achieved C-index values of 0.75 and 0.78 for PFS and 0.76, 0.74, and 0.75 for OS on the test sets. Radiomic feature-based models had superior performance compared to the bioclinical ones, and combining radiomic and bioclinical variables did not improve the performances. Metabolic tumor volume (MTV)-based models obtained lower C-index values for a majority of the tested configurations but quite equivalent performance in terms of time-dependent AUCs (td-AUC). The results demonstrate the possibility of identifying common PET-based image signatures for predicting the response of patients with induced HPV pathology, validated on multi-center multiconstructor data.
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Affiliation(s)
- Stephane Niyoteka
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France.
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France.
| | - Romain-David Seban
- Department of Nuclear Medicine, Institut Curie, Saint Cloud, France
- Department of Nuclear Medicine, Gustave Roussy, 94805, Villejuif, France
| | - Rahimeh Rouhi
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | | | - Marion Classe
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Pathology Department, Gustave Roussy, F-94805, Villejuif, France
| | - Alexandre Carré
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Roger Sun
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | | | - Cyrus Chargari
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Jack McKenna
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Garry McDermott
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | | | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, Zurich, Switzerland
| | - Marianne G Guren
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Claire Lemanski
- Department of Radiation Oncology, Institut Régional du Cancer de Montpellier, Montpellier, France
| | - Eric Deutsch
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
| | - Charlotte Robert
- Université Paris Saclay, INSERM UMR1030, Gustave Roussy, 94805, Villejuif, France
- Department of Radiation Oncology, Gustave Roussy, F-94805, Villejuif, France
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12
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Cheng Y, Wan S, Wu W, Chen F, Jiang J, Cai D, Bao Z, Li Y, Zhang L. Computed Tomography Angiography-Based Thrombus Radiomics for Predicting the Time Since Stroke Onset. Acad Radiol 2023; 30:2469-2476. [PMID: 36697269 DOI: 10.1016/j.acra.2022.12.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/11/2022] [Accepted: 12/18/2022] [Indexed: 01/25/2023]
Abstract
RATIONALE AND OBJECTIVES The measurement of the time since stroke onset (TSS) is crucial for decision-making in the treatment of acute ischemic stroke (AIS). This study assessed the utility of computed tomography angiography (CTA) radiomics features (RFs) to estimate TSS. MATERIALS AND METHODS A total of 221 patients with AIS were enrolled in this retrospective study and were divided into a training group (n = 154) and a test group (n = 67). Thrombi in CTA images were manually outlined using ITK-SNAP. Images were aligned, normalized, and pre-processed to extract RFs. The TSS was calculated as the time from stroke onset to CTA completion. The patients were classified into two groups according to estimated TSS: ≤4.5 and >4.5 hours. A total of 944 RFs were extracted from CTA images. Clinical factors associated with TSS were identified using multivariate logistic regression, and a combined model (clinical data and RFs) was constructed. The predictive value of the models was assessed by the area under the receiver operating characteristic curve (AUC). The performance of the models was compared using the DeLong test, and clinical utility was evaluated by decision curve analysis. RESULTS The AUC of the radiomics model was 0.803 (95% confidence interval [CI]: 0.733-0.873) and 0.803 (95% CI: 0.698-0.908) in the training and test cohorts, respectively. The AUC of the combined model (containing data on age, diabetes, and atrial fibrillation) in the training and test sets was 0.813 (95% CI: 0.750-0.889) and 0.803 (95% CI: 0.699-0.907), respectively. The DeLong test showed no significant difference between the radiomics and combined models. Decision curve analysis showed that both models had clinical utility. CONCLUSION CTA-based thrombus radiomics can estimate TSS in patients with AIS. The addition of clinical data to the model does not improve predictive performance.
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Affiliation(s)
- Yue Cheng
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, Jiangsu, China; Department of Radiology, Wuxi NO.2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, China
| | - Sunli Wan
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, Jiangsu, China
| | - Wenjuan Wu
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, Jiangsu, China
| | - Fangming Chen
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, Jiangsu, China
| | - Jingxuan Jiang
- Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dongmei Cai
- Department of Radiology, Xishan People's Hospital of Wuxi, Wuxi, China
| | - Zhongyuan Bao
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, Jiangsu, China
| | - Yuehua Li
- Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Zhang
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, Jiangsu, China.
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13
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Chu X, Niu L, Yang X, He S, Li A, Chen L, Liang Z, Jing D, Zhou R. Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial. iScience 2023; 26:107634. [PMID: 37664612 PMCID: PMC10474462 DOI: 10.1016/j.isci.2023.107634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/07/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.
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Affiliation(s)
- Xianjing Chu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Lishui Niu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xianghui Yang
- Department of Oncology, Changsha Central Hospital, Changsha 410004, China
| | - Shiqi He
- Department of Computer Science, University of British Columbia, 2329 West Mall, Vancouver, British Columbia, Canada
| | - Aixin Li
- Department of Radiotherapy, The First Affiliated Hospital, Hengyang Medical School, University of South, Hengyang 421001, China
| | - Liu Chen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhan Liang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Rongrong Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
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14
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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: 0] [Impact Index Per Article: 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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15
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Lyu S, Zhang Y, Zhang M, Zhu J, Yu J, Zhang B, Gao L, Wei H. The Application of Ultrasound Image-Based Radiomics in the Diagnosis of Mild Carpal Tunnel Syndrome. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1499-1508. [PMID: 36565451 DOI: 10.1002/jum.16160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
OBJECTIVES The ultrasound diagnosis of mild carpal tunnel syndrome (CTS) is challenging. Radiomics can identify image information that the human eye cannot recognize. The purpose of our study was to explore the value of ultrasound image-based radiomics in the diagnosis of mild CTS. METHODS This retrospective study included 126 wrists in the CTS group and 88 wrists in the control group. The radiomics features were extracted from the cross-sectional ultrasound images at the entrance of median nerve carpal tunnel, and the modeling was based on robust features. Two radiologists with different experiences diagnosed CTS according to two guidelines. The area under receiver (AUC) operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the diagnostic efficacy of the two radiologists and the radiomics model. RESULTS According to guideline one, the AUC values of the two radiologists for CTS were 0.72 and 0.67, respectively; according to guideline two, the AUC were 0.73 and 0.68, respectively. The radiomics model achieved the best accuracy when 16 important robust features were selected. The AUC values of training set and test set were 0.92 and 0.90, respectively. CONCLUSIONS The radiomics label based on ultrasound images had excellent diagnostic efficacy for mild CTS. It is expected to help radiologists to identify early CTS patients as soon as possible, especially for inexperienced doctors.
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Affiliation(s)
- Shuyi Lyu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Yan Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Meiwu Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Jiazhen Zhu
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
- Multi-disciplinary Diagnosis and Treatment Department, Ningbo No. 2 Hospital, Zhejiang, China
| | - Jianjun Yu
- Department of Neuroelectrophysiology, Ningbo No. 2 Hospital, Zhejiang, China
| | - Baisong Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Libo Gao
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Huilin Wei
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
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16
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Mishra A, Ravina M, Kote R, Kumar A, Kashyap Y, Dasgupta S, Reddy M. Role of Textural Analysis of Pretreatment 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Response Prediction in Esophageal Carcinoma Patients. Indian J Nucl Med 2023; 38:255-263. [PMID: 38046976 PMCID: PMC10693362 DOI: 10.4103/ijnm.ijnm_1_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/30/2023] [Indexed: 12/05/2023] Open
Abstract
Introduction Positron emission tomography/computed tomography (PET/CT) is routinely used for staging, response assessment, and surveillance in esophageal carcinoma patients. The aim of this study was to investigate whether textural features of pretreatment 18F-fluorodeoxyglucose (18F-FDG) PET/CT images can contribute to prognosis prediction in carcinoma oesophagus patients. Materials and Methods This is a retrospective study of 30 diagnosed carcinoma esophagus patients. These patients underwent pretreatment 18F-FDG PET/CT for staging. The images were processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between progression group and nonprogression group. The original dataset was subject separately to receiver operating curve analysis. Receiver operating characteristic (ROC) curves were used to identify the cutoff values for textural features with a P < 0.05 for statistical significance. Feature selection was done with principal component analysis. The selected features of each evaluator were subject to 4 machine-learning algorithms. The highest area under the curve (AUC) values was selected for 10 features. Results A retrospective study of 30 primary carcinoma esophagus patients was done. Patients were followed up after chemo-radiotherapy and they underwent follow-up PET/CT. On the basis of their response, patients were divided into progression group and nonprogression group. Among them, 15 patients showed disease progression and 15 patients were in the nonprogression group. Ten textural analysis parameters turned out to be significant in the prediction of disease progression. Cutoff values were calculated for these parameters according to the ROC curves, GLZLM_long zone emphasis (Gray Level Zone Length Matrix)_long zone emphasis (44.9), GLZLM_low gray level zone emphasis (0.006), GLZLM_short zone low gray level emphasis (0.0032), GLZLM_long zone low gray level emphasis (0.185), GLRLM_long run emphasis (Gray Level Run Length Matrix) (1.31), GLRLM_low gray level run emphasis (0.0058), GLRLM_short run low gray level emphasis (0.005496), GLRLM_long run low gray level emphasis (0.00727), NGLDM_Busyness (Neighborhood Gray Level Difference Matrix) (0.75), and gray level co-occurrence matrix_homogeneity (0.37). Feature selection by principal components analysis and feature classification by the K-nearest neighbor machine-learning model using independent training and test samples yielded the overall highest AUC. Conclusions Textural analysis parameters could provide prognostic information in carcinoma esophagus patients. Larger multicenter studies are needed for better clinical prognostication of these parameters.
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Affiliation(s)
- Ajit Mishra
- Department of Surgical Gastroenterology, DKS Multispeciality Hospital, Raipur, India
| | - Mudalsha Ravina
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Rutuja Kote
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Amit Kumar
- Department of Medical Oncology, All India Institute of Medical Sciences, Raipur, India
| | - Yashwant Kashyap
- Department of Medical Oncology, All India Institute of Medical Sciences, Raipur, India
| | - Subhajit Dasgupta
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Moulish Reddy
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
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17
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Masud MA, Kim JY, Kim E. Effective dose window for containing tumor burden under tolerable level. NPJ Syst Biol Appl 2023; 9:17. [PMID: 37221258 DOI: 10.1038/s41540-023-00279-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
A maximum-tolerated dose (MTD) reduces the drug-sensitive cell population, though it may result in the competitive release of drug resistance. Alternative treatment strategies such as adaptive therapy (AT) or dose modulation aim to impose competitive stress on drug-resistant cell populations by maintaining a sufficient number of drug-sensitive cells. However, given the heterogeneous treatment response and tolerable tumor burden level of individual patients, determining an effective dose that can fine-tune competitive stress remains challenging. This study presents a mathematical model-driven approach that determines the plausible existence of an effective dose window (EDW) as a range of doses that conserve sufficient sensitive cells while maintaining the tumor volume below a threshold tolerable tumor volume (TTV). We use a mathematical model that explains intratumor cell competition. Analyzing the model, we derive an EDW determined by TTV and the competitive strength. By applying a fixed endpoint optimal control model, we determine the minimal dose to contain cancer at a TTV. As a proof of concept, we study the existence of EDW for a small cohort of melanoma patients by fitting the model to longitudinal tumor response data. We performed identifiability analysis, and for the patients with uniquely identifiable parameters, we deduced patient-specific EDW and minimal dose. The tumor volume for a patient could be theoretically contained at the TTV either using continuous dose or AT strategy with doses belonging to EDW. Further, we conclude that the lower bound of the EDW approximates the minimum effective dose (MED) for containing tumor volume at the TTV.
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Affiliation(s)
- M A Masud
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung, 25451, Republic of Korea
| | - Jae-Young Kim
- Graduate School of Analytical Science and Technology (GRAST), Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung, 25451, Republic of Korea.
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18
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Sengupta PP, Vucic E. Expert-Level Intelligence for Prosthetic Valve Endocarditis Detection: Can Radiomics Bridge the Gap? JACC Cardiovasc Imaging 2023:S1936-878X(23)00185-7. [PMID: 37227332 DOI: 10.1016/j.jcmg.2023.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/30/2023] [Indexed: 05/26/2023]
Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital, and Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
| | - Esad Vucic
- Division of Cardiovascular Diseases, Department of Medicine, RWJ-BH Newark Beth Israel Medical Center, Newark, New Jersey, USA
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19
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Chiu FY, Yen Y. Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 2023; 11:35. [PMID: 36991494 DOI: 10.1186/s40364-023-00476-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.
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Affiliation(s)
- Fang-Ying Chiu
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Center for Brain and Neurobiology Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Teaching and Research Headquarters for Sustainable Development Goals, Tzu Chi University, Hualien City, 970374, Taiwan.
| | - Yun Yen
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Ph.D. Program for Cancer Biology and Drug Discovery, Taipei Medical University, Taipei City, 110301, Taiwan.
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei City, 110301, Taiwan.
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei City, 110301, Taiwan.
- Cancer Center, Taipei Municipal WanFang Hospital, Taipei City, 116081, Taiwan.
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20
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Kumar S, Virarkar M, Vulasala SSR, Daoud T, Ozdemir S, Wieseler C, Vincety-Latorre F, Gopireddy DR, Bhosale P, Lall C. Magnetic Resonance Imaging Virtual Biopsy of Common Solid Renal Masses-A Pictorial Review. J Comput Assist Tomogr 2023; 47:186-198. [PMID: 36790908 DOI: 10.1097/rct.0000000000001424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
ABSTRACT The expanded application of radiologic imaging resulted in an increased incidence of renal masses in the recent decade. Clinically, it is difficult to determine the malignant potential of the renal masses, thus resulting in complex management. Image-guided biopsies are the ongoing standard of care to identify molecular variance but are limited by tumor accessibility and heterogeneity. With the evolving importance of individualized cancer therapies, radiomics has displayed promising results in the identification of tumoral mutation status on routine imaging. This article discusses how magnetic resonance imaging features can guide a radiologist toward identifying renal mass characteristics.
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Affiliation(s)
- Sindhu Kumar
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Mayur Virarkar
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Sai Swarupa R Vulasala
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Taher Daoud
- Division of Diagnostic Imaging, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Savas Ozdemir
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Carissa Wieseler
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | | | - Dheeraj R Gopireddy
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Priya Bhosale
- Division of Diagnostic Imaging, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Chandana Lall
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
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21
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Khalid F, Goya-Outi J, Escobar T, Dangouloff-Ros V, Grigis A, Philippe C, Boddaert N, Grill J, Frouin V, Frouin F. Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities. Front Med (Lausanne) 2023; 10:1071447. [PMID: 36910474 PMCID: PMC9995801 DOI: 10.3389/fmed.2023.1071447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Purpose Predicting H3.1, TP53, and ACVR1 mutations in DIPG could aid in the selection of therapeutic options. The contribution of clinical data and multi-modal MRI were studied for these three predictive tasks. To keep the maximum number of subjects, which is essential for a rare disease, missing data were considered. A multi-modal model was proposed, collecting all available data for each patient, without performing any imputation. Methods A retrospective cohort of 80 patients with confirmed DIPG and at least one of the four MR modalities (T1w, T1c, T2w, and FLAIR), acquired with two different MR scanners was built. A pipeline including standardization of MR data and extraction of radiomic features within the tumor was applied. The values of radiomic features between the two MR scanners were realigned using the ComBat method. For each prediction task, the most robust features were selected based on a recursive feature elimination with cross-validation. Five different models, one based on clinical data and one per MR modality, were developed using logistic regression classifiers. The prediction of the multi-modal model was defined as the average of all possible prediction results among five for each patient. The performances of the models were compared using a leave-one-out approach. Results The percentage of missing modalities ranged from 6 to 11% across modalities and tasks. The performance of each individual model was dependent on each specific task, with an AUC of the ROC curve ranging from 0.63 to 0.80. The multi-modal model outperformed the clinical model for each prediction tasks, thus demonstrating the added value of MRI. Furthermore, regardless of performance criteria, the multi-modal model came in the first place or second place (very close to first). In the leave-one-out approach, the prediction of H3.1 (resp. ACVR1 and TP53) mutations achieved a balanced accuracy of 87.8% (resp. 82.1 and 78.3%). Conclusion Compared with a single modality approach, the multi-modal model combining multiple MRI modalities and clinical features was the most powerful to predict H3.1, ACVR1, and TP53 mutations and provided prediction, even in the case of missing modality. It could be proposed in the absence of a conclusive biopsy.
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Affiliation(s)
- Fahad Khalid
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO)-U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
| | - Jessica Goya-Outi
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO)-U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
| | - Thibault Escobar
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO)-U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France.,DOSIsoft SA, Cachan, France
| | - Volodia Dangouloff-Ros
- Department of Paediatric Radiology, Hôpital Universitaire Necker Enfants Malades, Paris, France.,Institut Imagine, Inserm U1163 and U1299, Université Paris Cité, Paris, France
| | | | | | - Nathalie Boddaert
- Department of Paediatric Radiology, Hôpital Universitaire Necker Enfants Malades, Paris, France.,Institut Imagine, Inserm U1163 and U1299, Université Paris Cité, Paris, France
| | - Jacques Grill
- Département Cancérologie de l'enfant et de l'adolescent, Gustave-Roussy, Villejuif, France.,Prédicteurs moléculaires et nouvelles cibles en oncologie-U981, Inserm, Université Paris-Saclay, Villejuif, France
| | | | - Frédérique Frouin
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO)-U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
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22
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Berger T, Noble DJ, Yang Z, Shelley LEA, McMullan T, Bates A, Thomas S, Carruthers LJ, Beckett G, Duffton A, Paterson C, Jena R, McLaren DB, Burnet NG, Nailon WH. Assessing the generalisability of radiomics features previously identified as predictive of radiation-induced sticky saliva and xerostomia. Phys Imaging Radiat Oncol 2023; 25:100404. [PMID: 36660107 PMCID: PMC9843480 DOI: 10.1016/j.phro.2022.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/30/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Background and purpose While core to the scientific approach, reproducibility of experimental results is challenging in radiomics studies. A recent publication identified radiomics features that are predictive of late irradiation-induced toxicity in head and neck cancer (HNC) patients. In this study, we assessed the generalisability of these findings. Materials and Methods The procedure described in the publication in question was applied to a cohort of 109 HNC patients treated with 50-70 Gy in 20-35 fractions using helical radiotherapy although there were inherent differences between the two patient populations and methodologies. On each slice of the planning CT with delineated parotid and submandibular glands, the imaging features that were previously identified as predictive of moderate-to-severe xerostomia and sticky saliva 12 months post radiotherapy (Xer12m and SS12m) were calculated. Specifically, Short Run Emphasis (SRE) and maximum CT intensity (maxHU) were evaluated for improvement in prediction of Xer12m and SS12m respectively, compared to models solely using baseline toxicity and mean dose to the salivary glands. Results None of the associations previously identified as statistically significant and involving radiomics features in univariate or multivariate models could be reproduced on our cohort. Conclusion The discrepancies observed between the results of the two studies delineate limits to the generalisability of the previously reported findings. This may be explained by the differences in the approaches, in particular the imaging characteristics and subsequent methodological implementation. This highlights the importance of external validation, high quality reporting guidelines and standardisation protocols to ensure generalisability, replication and ultimately clinical implementation.
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Affiliation(s)
- Thomas Berger
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.,Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - David J Noble
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.,The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK.,Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Zhuolin Yang
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.,School of Engineering, the University of Edinburgh, the King's Buildings, Mayfield Road, Edinburgh EH9 3JL, UK
| | - Leila E A Shelley
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Thomas McMullan
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Amy Bates
- The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Simon Thomas
- Department of Medical Physics and Clinical Engineering, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Linda J Carruthers
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - George Beckett
- Edinburgh Parallel Computing Centre, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Aileen Duffton
- Beatson West of Scotland Cancer Centre, Great Western Road, Glasgow G12 0YN, UK
| | - Claire Paterson
- Beatson West of Scotland Cancer Centre, Great Western Road, Glasgow G12 0YN, UK
| | - Raj Jena
- The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Duncan B McLaren
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Neil G Burnet
- The Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK
| | - William H Nailon
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.,School of Engineering, the University of Edinburgh, the King's Buildings, Mayfield Road, Edinburgh EH9 3JL, UK
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23
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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24
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Wang Q, Chen Y, Qin S, Liu X, Liu K, Xin P, Zhao W, Yuan H, Lang N. Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach. Cancers (Basel) 2022; 14:5201. [PMID: 36358621 PMCID: PMC9658803 DOI: 10.3390/cancers14215201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/15/2023] Open
Abstract
The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This retrospective study consisted of 107 patients, including a training set (n = 82) and a validation set (n = 25). Kaplan-Meier survival analysis was used to validate the prognostic value of RANKL status. Radiomic feature extraction of three heterogeneous regions (VOIentire, VOIedge, and VOIcore) from pretreatment CT were performed. Followed by feature selection using Selected K Best and least absolute shrinkage and selection operator (LASSO) analysis, three classifiers (random forest (RF), support vector machine, and logistic regression) were used to build models. The area under the curve (AUC), accuracy, F1 score, recall, precision, sensitivity, and specificity were used to evaluate the models' performance. Classification of 75 patients with eligible follow-up based on RANKL status resulted in a significant difference in progression-free survival (p = 0.035). VOIcore-based RF classifier performs best. Using this model, the AUCs for the training and validation cohorts were 0.880 and 0.766, respectively. In conclusion, a machine learning approach based on CT radiomic features could discriminate prognostically significant RANKL status in spinal GCTB, which may ultimately aid clinical decision-making.
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Affiliation(s)
- Qizheng Wang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Siyuan Qin
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Xiaoming Liu
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Yongteng North Road, Haidian District, Beijing 100089, China
- Beijing United Imaging Research Institute of Intelligent Imaging, Yongteng North Road, Haidian District, Beijing 100089, China
| | - Ke Liu
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Peijin Xin
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Weili Zhao
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
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25
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Crombé A, Roulleau‐Dugage M, Italiano A. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1288-1313. [PMID: 36260064 PMCID: PMC9759765 DOI: 10.1002/cac2.12373] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/20/2022] [Accepted: 10/08/2022] [Indexed: 01/25/2023]
Abstract
Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring. Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings. Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides including a strong translational program that will allow for a better understanding of the determinants involved in sensitivity and treatment resistance to immune-oncology agents.
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Affiliation(s)
- Amandine Crombé
- Department of ImagingInstitut BergoniéBordeauxNouvelle‐AquitaineF‐33076France,Faculty of MedicineUniversity of BordeauxBordeauxNouvelle‐AquitaineF‐33000France
| | | | - Antoine Italiano
- Faculty of MedicineUniversity of BordeauxBordeauxNouvelle‐AquitaineF‐33000France,Early Phase Trials and Sarcoma UnitInstitut BergoniéBordeauxNouvelle‐AquitaineF‐33076France
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26
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Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:e004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient's outcomes and to assess response to these promising combinations.
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Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- INSERM U1030, Gustave Roussy, Villejuif, France
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27
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Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imaging (Bellingham) 2022; 9:044005. [PMID: 35992729 PMCID: PMC9386367 DOI: 10.1117/1.jmi.9.4.044005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/09/2022] [Indexed: 08/20/2023] Open
Abstract
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
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Affiliation(s)
- Robba Rai
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael B. Barton
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Phillip Chlap
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Gary Liney
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Carsten Brink
- Odense University Hospital, Laboratory of Radiation Physics, Department of Oncology, Odense, Denmark
- University of Southern Denmark, Department of Clinical Research, Odense, Denmark
| | - Shalini Vinod
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | | | - Yuvnik Trada
- Calvary Mater Newcastle, Department of Radiation Oncology, Newcastle, New South Wales, Australia
| | - Lois C. Holloway
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- University of Wollongong, Centre of Radiation Physics, Wollongong, New South Wales, Australia
- University of Sydney, Institute of Medical Physics, School of Physics, Sydney, New South Wales, Australia
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28
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Soft Tissue Sarcomas: The Role of Quantitative MRI in Treatment Response Evaluation. Acad Radiol 2022; 29:1065-1084. [PMID: 34548230 DOI: 10.1016/j.acra.2021.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/29/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Although curative surgery remains the cornerstone of the therapeutic strategy in patients with soft tissue sarcomas (STS), neoadjuvant radiotherapy and chemotherapy (NART and NACT, respectively) are increasingly used to improve operability, surgical margins and patient outcome. The best imaging modality for locoregional assessment of STS is MRI but these tumors are mostly evaluated in a qualitative manner. OBJECTIVE After an overview of the current standard of care regarding treatment for patients with locally advanced STS, this review aims to summarize the principles and limitations of (i) the current methods used to evaluate response to neoadjuvant treatment in clinical practice and clinical trials in STS (RECIST 1.1 and modified Choi criteria), (ii) quantitative MRI sequences (i.e., diffusion weighted imaging and dynamic contrast enhanced MRI), and (iii) texture analyses and (delta-) radiomics.
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29
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Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study. Acad Radiol 2022; 29:806-816. [PMID: 34238656 DOI: 10.1016/j.acra.2021.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve. RESULTS A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical. CONCLUSION The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.
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Affiliation(s)
- Yancheng Song
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Bo Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Chentong Yuan
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Hao Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Ziwen Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Fanyi Min
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Yu Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong.
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30
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Kong X, Zhang Q, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y, Li Z, Li Z. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. Front Oncol 2022; 12:816297. [PMID: 35669440 PMCID: PMC9163342 DOI: 10.3389/fonc.2022.816297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.
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Affiliation(s)
- Xianshu Kong
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Qian Zhang
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Xuemei Wu
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Tianning Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jiajun Duan
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Shujie Song
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jianyun Nie
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Chu Tao
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Mi Tang
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Maohua Wang
- First Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jieya Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhen Li
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
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31
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Wei G, Jiang P, Tang Z, Qu A, Deng X, Guo F, Sun H, Zhang Y, Gu L, Zhang S, Mu W, Wang J, Tian J. MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone. Magn Reson Imaging 2022; 91:81-90. [DOI: 10.1016/j.mri.2022.05.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 01/16/2023]
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32
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Sun R, Deutsch E, Fournier L. [Artificial intelligence and medical imaging]. Bull Cancer 2021; 109:83-88. [PMID: 34782120 DOI: 10.1016/j.bulcan.2021.09.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/31/2021] [Accepted: 09/02/2021] [Indexed: 01/06/2023]
Abstract
The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial intelligence are particularly promising, with numerous applications and a strong interest in the search for new biomarkers. Here, we will present the general methods used in these approaches as well as the potential areas of application.
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Affiliation(s)
- Roger Sun
- Gustave Roussy Cancer Campus, université Paris-Saclay, département de Radiothérapie, Inserm U1030, 94805 Villejuif, France.
| | - Eric Deutsch
- Gustave Roussy Cancer Campus, université Paris-Saclay, département de Radiothérapie, Inserm U1030, 94805 Villejuif, France
| | - Laure Fournier
- Hôpital Européen Georges-Pompidou, département de radiologie, 20, rue Leblanc, 75015 Paris, France
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33
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Orlhac F, Nioche C, Klyuzhin I, Rahmim A, Buvat I. Radiomics in PET Imaging:: A Practical Guide for Newcomers. PET Clin 2021; 16:597-612. [PMID: 34537132 DOI: 10.1016/j.cpet.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critical reading of the content of articles. The advantages and limitations of handcrafted radiomics compared with deep-learning approaches for the characterization of PET images are also discussed.
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Affiliation(s)
- Fanny Orlhac
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France.
| | - Christophe Nioche
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
| | - Ivan Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Irène Buvat
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
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34
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de Causans A, Carré A, Roux A, Tauziède-Espariat A, Ammari S, Dezamis E, Dhermain F, Reuzé S, Deutsch E, Oppenheim C, Varlet P, Pallud J, Edjlali M, Robert C. Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis. Front Oncol 2021; 11:638262. [PMID: 34327133 PMCID: PMC8315001 DOI: 10.3389/fonc.2021.638262] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 06/17/2021] [Indexed: 01/06/2023] Open
Abstract
Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort. Methods We enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test. Results The ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination. Conclusions The proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.
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Affiliation(s)
- Alix de Causans
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Alexandre Roux
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Arnault Tauziède-Espariat
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neuropathologie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Samy Ammari
- Département de Radiologie, Gustave Roussy, Université Paris Saclay, Villejuif, France.,BioMaps UMR1281, Université Paris-Saclay, CNRS, INSERM, CEA, Orsay, France
| | - Edouard Dezamis
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Frederic Dhermain
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Sylvain Reuzé
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Eric Deutsch
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Catherine Oppenheim
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | | | - Johan Pallud
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Myriam Edjlali
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Charlotte Robert
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
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35
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Cucchiara F, Petrini I, Romei C, Crucitta S, Lucchesi M, Valleggi S, Scavone C, Capuano A, De Liperi A, Chella A, Danesi R, Del Re M. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 2021; 169:105643. [PMID: 33940185 DOI: 10.1016/j.phrs.2021.105643] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Chiara Romei
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Simona Valleggi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Cristina Scavone
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa Capuano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa De Liperi
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Antonio Chella
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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Kim E, Brown JS, Eroglu Z, Anderson AR. Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models. Cancers (Basel) 2021; 13:823. [PMID: 33669315 PMCID: PMC7920057 DOI: 10.3390/cancers13040823] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/11/2021] [Indexed: 02/07/2023] Open
Abstract
Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points in a patient-specific manner. Here we develop a combination of mathematical models that examine interactions between drug-sensitive and resistant cells to facilitate melanoma adaptive therapy dosing and switch time points. The first model assumes genetically fixed drug-sensitive and -resistant popul tions that compete for limited resources. The second model considers phenotypic switching between drug-sensitive and -resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6-25 months compared to continuous therapy with dose rates of 6-74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy, such as the number of initial sensitive cells, competitive effect, switching rate from resistant to sensitive cells, and sensitive cell growth rate. This study highlights that there is a range of potential patient-specific benefits of adaptive therapy and identifies parameters that modulate this benefit.
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Affiliation(s)
- Eunjung Kim
- Natural Product Research Center, Korea Institute of Science and Technology, Gangneung 25451, Korea
| | - Joel S. Brown
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612, USA;
| | - Zeynep Eroglu
- Cutaneous Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612, USA;
| | - Alexander R.A. Anderson
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612, USA;
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Hatt M, Cheze Le Rest C, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, Lucia F, Bourbonne V, Schick U, Badic B, Visvikis D. Radiomics in PET/CT: Current Status and Future AI-Based Evolutions. Semin Nucl Med 2020; 51:126-133. [PMID: 33509369 DOI: 10.1053/j.semnuclmed.2020.09.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study design, data acquisition, segmentation, features calculation and modeling by the radiomics community, and these are often the same issues across all image modalities and clinical applications, however some of these are specific to PET/CT (and SPECT/CT) imaging and therefore the present paper focuses on those. In most cases, recommendations and potential methodological solutions do exist and should therefore be followed to improve the overall quality and reproducibility of published studies. In terms of future evolutions, the techniques from the larger field of artificial intelligence (AI), including those relying on deep neural networks (also known as deep learning) have already shown impressive potential to provide solutions, especially in terms of automation, but also to maybe fully replace the tools the radiomics community has been using until now in order to build the usual radiomics workflow. Some important challenges remain to be addressed before the full impact of AI may be realized but overall the field has made striking advances over the last few years and it is expected advances will continue at a rapid pace.
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Affiliation(s)
- Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France; Nuclear Medicine Department, CHU Milétrie, Poitiers, France
| | - Nils Antonorsi
- Nuclear Medicine Department, CHU Milétrie, Poitiers, France
| | - Florent Tixier
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States of America
| | | | - Vincent Jaouen
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France; IMT-Atlantique, Plouzané, France
| | - Francois Lucia
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
| | | | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
| | - Bogdan Badic
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
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Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.07.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M. Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J Radiol 2020; 132:109283. [PMID: 32980727 DOI: 10.1016/j.ejrad.2020.109283] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 08/29/2020] [Accepted: 09/08/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Sarcomas are a model for intra- and inter-tumoral heterogeneities making them particularly suitable for radiomics analyses. Our purposes were to review the aims, methods and results of radiomics studies involving sarcomas METHODS: Pubmed and Web of Sciences databases were searched for radiomics or textural studies involving bone, soft-tissues and visceral sarcomas until June 2020. Two radiologists evaluated their objectives, results and quality of their methods, imaging pre-processing and machine-learning workflow helped by the items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), Image Biomarker Standardization Initiative (IBSI) and 'Radiomics Quality Score' (RQS). Statistical analyses included inter-reader agreements, correlations between methodological assessments, scientometrics indices, and their changes over years, and between RQS, number of patients and models performance. RESULTS Fifty-two studies were included involving: soft-tissue sarcomas (29/52, 55.8 %), bone sarcomas (15/52, 28.8 %), gynecological sarcomas (6/52, 11.5 %) and mixed sarcomas (2/52, 3.8 %), mostly imaged with MRI (36/52, 69.2 %), for a total of distinct patients. Median RQS was 4.5 (28.4 % of the maximum, range: -7 - 17). Performances of predictive models and number of patients negatively correlated (p = 0.027). None of the studies detailed all the items from the IBSI guidelines. There was a significant increase in studies' impact factors since the establishing of the RQS in 2017 (p = 0.038). CONCLUSION Although showing promising results, further efforts are needed to make sarcoma radiomics studies reproducible with an acceptable level of evidence. A better knowledge of the RQS and IBSI reporting guidelines could improve the quality of sarcoma radiomics studies and accelerate clinical applications.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France; Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France; University of Bordeaux, F-33000, Bordeaux, France.
| | - David Fadli
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Antoine Italiano
- University of Bordeaux, F-33000, Bordeaux, France; Department of Medical Oncology, Institut Bergonie, F-33000, Bordeaux, France
| | - Olivier Saut
- Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
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Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020; 146:197-208. [PMID: 32563015 PMCID: PMC7383235 DOI: 10.1016/j.lungcan.2020.05.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 12/24/2022]
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
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Affiliation(s)
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gareth J Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK
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Radiomics-based prediction model for outcomes of PD-1/PD-L1 immunotherapy in metastatic urothelial carcinoma. Eur Radiol 2020; 30:5392-5403. [PMID: 32394281 DOI: 10.1007/s00330-020-06847-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/27/2020] [Accepted: 03/30/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To evaluate the usefulness of a radiomics-based prediction model for predicting response and survival outcomes of patients with metastatic urothelial carcinoma treated with immunotherapy targeting programmed cell death 1 (PD-1) and its ligand (PD-L1). METHODS Sixty-two patients who underwent immunotherapy were divided into training (n = 41) and validation sets (n = 21). A total of 224 measurable lesions were identified on contrast-enhanced CT. A radiomics signature was constructed with features selected using a least absolute shrinkage and selection operator algorithm in the training set. A radiomics-based model was built based on a radiomics signature consisting of five reliable RFs and the presence of visceral organ involvement using multivariate logistic regression. According to a cutoff determined on the training set, patients in the validation set were assigned to either high- or low-risk groups. Kaplan-Meier analysis was performed to compare progression-free and overall survival between high- and low-risk groups. RESULTS For predicting objective response and disease control, the areas under the receiver operating characteristic curves of the radiomics-based model were 0.87 (95% CI, 0.65-0.97) and 0.88 (95% CI, 0.67-0.98) for the validation set, providing larger net benefit determined by decision curve analysis than without radiomics-based model. The high-risk group in the validation set showed shorter progression-free and overall survival than the low-risk group (log-rank p = 0.044 and p = 0.035). CONCLUSIONS The radiomics-based model may predict the response and survival outcome in patients treated with PD-1/PD-L1 immunotherapy for metastatic urothelial carcinoma. This approach may provide important and decision tool for planning immunotherapy. KEY POINTS • A radiomics-based model was built based on radiomics features and the presence of visceral organ involvement for prediction of outcomes in metastatic urothelial carcinoma treated with immunotherapy. • This prediction model demonstrated good prediction of treatment response and higher net benefit than no model in the independent validation set. • This radiomics-based model demonstrated significant associations with progression-free and overall survival between low-risk and high-risk groups.
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Rai R, Holloway LC, Brink C, Field M, Christiansen RL, Sun Y, Barton MB, Liney GP. Multicenter evaluation of MRI-based radiomic features: A phantom study. Med Phys 2020; 47:3054-3063. [PMID: 32277703 DOI: 10.1002/mp.14173] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/09/2020] [Accepted: 03/31/2020] [Indexed: 12/13/2022] Open
Abstract
INTRODUCTION This work describes the development of a novel radiomics phantom designed for magnetic resonance imaging (MRI) that can be used in a multicenter setting. The purpose of this study is to assess the stability and reproducibility of MRI-based radiomic features using this phantom across different MRI scanners. METHODS & MATERIALS A set of phantoms were three-dimensional (3D) printed using MRI visible materials. One set of phantoms were imaged on seven MRI scanners and one was imaged on one MRI scanner. Radiomics analysis of the phantoms, which included first-order features, shape and texture features was performed. Intraclass correlation coefficient (ICC) was used to assess the stability of radiomic features across eight scanners and the reproducibility of two printed models on one scanner. Coefficient of variation (COV) was used to assess the reproducibility of radiomics measurements in the phantom on a single scanner. RESULTS The phantom models provide sufficient signal-to-noise and contrast in all the tumor models permitting robust automatic segmentation. During a 12-month period of monitoring, the phantom material was stable with T1 and T2 of 150.7 ± 6.7 ms and 56.1 ± 3.9 ms, respectively. Of all the radiomic features computed, 34 of 69 had COV < 10%. Features from first-order statistics were the most robust in stability across the eight scanners with eight of 12 (67%) having high stability. About 29 of 50 (58%) texture features had high stability and no shape features had high stability features across the eight scanners. CONCLUSION A novel MRI radiomics phantom has been developed to assess the reproducibility and stability of MRI-based radiomic features across multiple institutions. The variation in radiomic feature stability demonstrates the need for caution when interpreting these features for clinical studies.
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Affiliation(s)
- Robba Rai
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, 2170, Australia.,Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, 2170, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
| | - Lois C Holloway
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, 2170, Australia.,Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, 2170, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia.,Centre of Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Carsten Brink
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.,Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Matthew Field
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, 2170, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
| | - Rasmus L Christiansen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.,Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Yu Sun
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Michael B Barton
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, 2170, Australia.,Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, 2170, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
| | - Gary P Liney
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, 2170, Australia.,Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, 2170, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia.,Centre of Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia
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Benzakoun J, Robert C, Legrand L, Pallud J, Meder JF, Oppenheim C, Dhermain F, Edjlali M. Anatomical and functional MR imaging to define tumoral boundaries and characterize lesions in neuro-oncology. Cancer Radiother 2020; 24:453-462. [PMID: 32278653 DOI: 10.1016/j.canrad.2020.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 03/04/2020] [Indexed: 12/19/2022]
Abstract
Neuroimaging and especially MRI has emerged as a necessary imaging modality to detect, measure, characterize and monitor brain tumours. Advanced MRI sequences such as perfusion MRI, diffusion MRI and spectroscopy as well as new post-processing techniques such as automatic segmentation of tumours and radiomics play a crucial role in characterization and follow up of brain tumours. The purpose of this review is to provide an overview on anatomical and functional MRI use for brain tumours boundaries determination and tumour characterization in the specific context of radiotherapy. The usefulness of anatomical and functional MRI on particular challenges posed by radiotherapy such as pseudo progression and pseudo esponse and new treatment strategies such as dose painting is also described.
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Affiliation(s)
- J Benzakoun
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France.
| | - C Robert
- Medical Physics Department, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Molecular Radiotherapy, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Inserm, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Paris-Sud University, Paris-Saclay University, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - L Legrand
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - J Pallud
- Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France; Neurosurgery Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France
| | - J-F Meder
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - C Oppenheim
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - F Dhermain
- Radiotherapy Department, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - M Edjlali
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
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Tixier F, Cheze-le-Rest C, Schick U, Simon B, Dufour X, Key S, Pradier O, Aubry M, Hatt M, Corcos L, Visvikis D. Transcriptomics in cancer revealed by Positron Emission Tomography radiomics. Sci Rep 2020; 10:5660. [PMID: 32221360 PMCID: PMC7101432 DOI: 10.1038/s41598-020-62414-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 03/13/2020] [Indexed: 11/09/2022] Open
Abstract
Metabolic images from Positron Emission Tomography (PET) are used routinely for diagnosis, follow-up or treatment planning purposes of cancer patients. In this study we aimed at determining if radiomic features extracted from 18F-Fluoro Deoxy Glucose (FDG) PET images could mirror tumor transcriptomics. In this study we analyzed 45 patients with locally advanced head and neck cancer (H&N) that underwent FDG-PET scans at the time of diagnosis and transcriptome analysis using RNAs from both cancer and healthy tissues on microarrays. Association between PET radiomics and transcriptomics was carried out with the Genomica software and a functional annotation was used to associate PET radiomics, gene expression and altered biological pathways. We identified relationships between PET radiomics and genes involved in cell-cycle, disease, DNA repair, extracellular matrix organization, immune system, metabolism or signal transduction pathways, according to the Reactome classification. Our results suggest that these FDG PET radiomic features could be used to infer tissue gene expression and cellular pathway activity in H&N cancers. These observations strengthen the value of radiomics as a promising approach to personalize treatments through targeting tumor-specific molecular processes.
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Affiliation(s)
- Florent Tixier
- Department of Nuclear Medicine, Poitiers University Hospital, Poitiers, France.
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
| | - Catherine Cheze-le-Rest
- Department of Nuclear Medicine, Poitiers University Hospital, Poitiers, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
- Radiation Oncology Department, University Hospital, Brest, France
| | - Brigitte Simon
- INSERM, UMR 1078, Université de Brest, Génétique Génomique Fonctionnelle et Biotechnologies, Etablissement Français du Sang, Brest, France
| | - Xavier Dufour
- Head and Neck Department, Poitiers University Hospital, Poitiers, France
| | - Stéphane Key
- Radiation Oncology Department, University Hospital, Brest, France
| | - Olivier Pradier
- Radiation Oncology Department, University Hospital, Brest, France
| | - Marc Aubry
- CNRS, UMR 6290, IGDR, Université de Rennes 1, Rennes, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Laurent Corcos
- INSERM, UMR 1078, Université de Brest, Génétique Génomique Fonctionnelle et Biotechnologies, Etablissement Français du Sang, Brest, France
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Bianchini L, Botta F, Origgi D, Rizzo S, Mariani M, Summers P, García-Polo P, Cremonesi M, Lascialfari A. PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis. Phys Med 2020; 71:71-81. [PMID: 32092688 DOI: 10.1016/j.ejmp.2020.02.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/29/2020] [Accepted: 02/04/2020] [Indexed: 01/26/2023] Open
Abstract
PURPOSE To develop a phantom for methodological radiomic investigation on Magnetic Resonance (MR) images of female patients affected by pelvic cancer. METHODS A pelvis-shaped container was filled with a MnCl2 solution reproducing the relaxation times (T1, T2) of muscle surrounding pelvic malignancies. Inserts simulating multi-textured lesions were embedded in the phantom. The relaxation times of muscle and tumour were measured on an MR scanner on healthy volunteers and patients; T1 and T2 of MnCl2 solutions were evaluated with a relaxometer to find the concentrations providing a match to in vivo relaxation times. Radiomic features were extracted from the phantom inserts and the patients' lesions. Their repeatability was assessed by multiple measurements. RESULTS Muscle T1 and T2 were 1128 (806-1378) and 51 (40-65) ms, respectively. The phantom reproduced in vivo values within 13% (T1) and 12% (T2). T1 and T2 of tumour tissue were 1637 (1396-2121) and 94 (79-101) ms, respectively. The phantom insert best mimicking the tumour agreed within 7% (T1) and 24% (T2) with in vivo values. Out of 1034 features, 75% (95%) had interclass correlation coefficient greater than 0.9 on T1 (T2)-weighted images, reducing to 33% (25%) if the phantom was repositioned. The most repeatable features on phantom showed values in agreement with the features extracted from patients' lesions. CONCLUSIONS We developed an MR phantom with inserts mimicking both relaxation times and texture of pelvic tumours. As exemplified with repeatability assessment, such phantom is useful to investigate features robustness and optimise the radiomic workflow on pelvic MR images.
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Affiliation(s)
- Linda Bianchini
- Department of Physics and INSTM RU, Università degli Studi di Milano, Italy.
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto di Imaging della Svizzera Italiana, Sede Ospedale Regionale di Lugano, Switzerland
| | - Manuel Mariani
- Department of Physics and INSTM RU, Università degli Studi di Pavia, Italy
| | - Paul Summers
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Pablo García-Polo
- Southern Europe Global Research Organization, GE Healthcare, Madrid, Spain
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Alessandro Lascialfari
- Department of Physics and INSTM RU, Università degli Studi di Milano, Italy; Department of Physics and INSTM RU, Università degli Studi di Pavia, Italy
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Bach Cuadra M, Favre J, Omoumi P. Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics. Semin Musculoskelet Radiol 2020; 24:50-64. [DOI: 10.1055/s-0039-3400268] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractAlthough still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.
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Affiliation(s)
- Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
- Centre d'Imagerie BioMédicale (CIBM), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Julien Favre
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Meng Y, Sun J, Qu N, Zhang G, Yu T, Piao H. Application of Radiomics for Personalized Treatment of Cancer Patients. Cancer Manag Res 2019; 11:10851-10858. [PMID: 31920394 PMCID: PMC6941598 DOI: 10.2147/cmar.s232473] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 12/16/2019] [Indexed: 12/14/2022] Open
Abstract
Radiomics is a novel concept that relies on obtaining image data from examinations such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). With the appropriate algorithm, the extracted results have broad applicability and potential for a massive positive impact in radiology. For example, clinicians can verify treatment efficiency, predict the location of tumor metastasis, correlate results with a histopathological examination, or more accurately define the type of cancer. Combining radiomics with other testing techniques allows every patient to have a personalized treatment plan that is essential for advanced examination and treatment. This article explains the process of radiomics, including data collection mechanisms, combined use with genomics, and artificial intelligence and immunology techniques, which may solve many of the challenges faced by doctors in diagnosing and treating their patients.
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Affiliation(s)
- Yiming Meng
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Jing Sun
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Na Qu
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Guirong Zhang
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Tao Yu
- Department of Medical Image, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Haozhe Piao
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China.,Department of Neurosurgery, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
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Chen J, Chen Y, Zheng D, Pang P, Lu J, Zheng X. Pretreatment MR-Based Radiomics Signature as Potential Imaging Biomarker for Assessing the Expression of Topoisomerase II alpha (TOPO-IIα) in Rectal Cancer. J Magn Reson Imaging 2019; 51:1881-1889. [PMID: 31675149 DOI: 10.1002/jmri.26972] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/26/2019] [Accepted: 09/26/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Rectal cancer (RC) is one of the most common cancers throughout the world. Chemotherapy or neoadjuvant chemotherapy play an important role in the treatment of advanced RC. Whether to add topoisomerase inhibitor to individualized chemotherapy is a puzzling question for clinicians. PURPOSE To investigate whether pretreatment MR-based radiomics signature can assess the expression of topoisomerase II alpha (TOPO-IIα) in RC. STUDY TYPE Retrospective. POPULATION In all, 122 patients with RC. Field Strength/Sequence: Pretreatment 3.0T; T2 WI turbo spin echo (TSE) sequence. ASSESSMENT A training group (n = 85) and a test group (n = 37) with pathologically confirmed RC. Patients underwent TOPO-IIα expression. A total of 180 radiomics features were extracted from oblique axial T2 WI TSE images of the entire primary tumor. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce the dimension of the data and select the features. STATISTICAL TESTS The assessment models were established by multivariable logistic regression analysis. The performance of the model was assessed by the receiver operating characteristic (ROC) curve, nomogram, and calibration. RESULTS The radiomics signature, which consisted of 10 selected optimal features, was significantly associated with TOPO-IIα expression (P < 0.01 for both training and test groups). The area under the curve (AUC), the sensitivity, and the specificity for assessing TOPO-IIα expression, were 0.859, 0.872, and 0.739, respectively, in the training group, while they were 0.762, 0.941, and 0.600 in the test group. The nomogram model of the radiomics signature (Rad-score) had good calibration. Calibration curves were plotted to assess the calibration of the radiomics nomogram that was accompanied with the Hosmer-Lemeshow test (P = 0.52). DATA CONCLUSION The proposed pretreatment MR-based radiomics signature was associated with TOPO-IIα expression. A radiomics nomogram might be helpful in the individualized assessment of TOPO-IIα expression in patients with RC. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1881-1889.
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Affiliation(s)
- Jiayou Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Ying Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Dechun Zheng
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | | | - Jianping Lu
- Department of Pathology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Xiang Zheng
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
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Zanfardino M, Franzese M, Pane K, Cavaliere C, Monti S, Esposito G, Salvatore M, Aiello M. Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases. J Transl Med 2019; 17:337. [PMID: 31590671 PMCID: PMC6778975 DOI: 10.1186/s12967-019-2073-2] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/18/2019] [Indexed: 02/07/2023] Open
Abstract
Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.
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Hatt M, Le Rest CC, Tixier F, Badic B, Schick U, Visvikis D. Radiomics: Data Are Also Images. J Nucl Med 2019; 60:38S-44S. [DOI: 10.2967/jnumed.118.220582] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 03/28/2019] [Indexed: 12/14/2022] Open
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