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Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
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Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
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2
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Qian WL, Chen Q, Zhang JB, Xu JM, Hu CH. RESOLVE-based radiomics in cervical cancer: improved image quality means better feature reproducibility? Clin Radiol 2023; 78:e469-e476. [PMID: 37029000 DOI: 10.1016/j.crad.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 04/07/2023]
Abstract
AIM To compare the reproducibility of apparent diffusion coefficient (ADC)-based radiomic features between readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) and single-shot echo-planar diffusion-weighted imaging (SS-EPI DWI) in cervical cancer. MATERIALS AND METHODS The RESOLVE and SS-EPI DWI images of 36 patients with histopathologically confirmed cervical cancer were collected retrospectively. Two observers independently delineated the whole tumour on RESOLVE and SS-EPI DWI, and then copied them to the corresponding ADC maps. Shape, first-order, and texture features were extracted from ADC maps in the original and filtered (Laplacian of Gaussian [LoG] and wavelet) images. Thereafter, 1,316 features were generated in each RESOLVE and SS-EPI DWI, respectively. The reproducibility of radiomic features was assessed using intraclass correlation coefficient (ICC). RESULTS In the original images, RESOLVE showed 92.86%, 66.67%, and 86.67% of features with excellent reproducibility in shape, first-order, and texture features, while SS-EPI DWI showed 85.71%, 72.22%, and 60% of features with excellent reproducibility, respectively. In the LoG and wavelet filtered images, RESOLVE had 56.77% and 65.32% of features with excellent reproducibility and SS-EPI DWI had 44.95% and 61.96% of features with excellent reproducibility, respectively. CONCLUSION Compared with SS-EPI DWI, the feature reproducibility of RESOLVE was better in cervical cancer, especially for texture features. The filtered images cannot improve the feature reproducibility compared with the original images for both SS-EPI DWI and RESOLVE.
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3
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Wang X, Xu C, Grzegorzek M, Sun H. Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival. Front Physiol 2022; 13:948767. [PMID: 36091379 PMCID: PMC9452776 DOI: 10.3389/fphys.2022.948767] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose: We aim to develop and validate PET/ CT image-based radiomics to determine the Ki-67 status of high-grade serous ovarian cancer (HGSOC), in which we use the metabolic subregion evolution to improve the prediction ability of the model. At the same time, the stratified effect of the radiomics model on the progression-free survival rate of ovarian cancer patients was illustrated.Materials and methods: We retrospectively reviewed 161 patients with HGSOC from April 2013 to January 2019. 18F-FDG PET/ CT images before treatment, pathological reports, and follow-up data were analyzed. A randomized grouping method was used to divide ovarian cancer patients into a training group and validation group. PET/ CT images were fused to extract radiomics features of the whole tumor region and radiomics features based on the Habitat method. The feature is dimensionality reduced, and meaningful features are screened to form a signature for predicting the Ki-67 status of ovarian cancer. Meanwhile, survival analysis was conducted to explore the hierarchical guidance significance of radiomics in the prognosis of patients with ovarian cancer.Results: Compared with texture features extracted from the whole tumor, the texture features generated by the Habitat method can better predict the Ki-67 state (p < 0.001). Radiomics based on Habitat can predict the Ki-67 expression accurately and has the potential to become a new marker instead of Ki-67. At the same time, the Habitat model can better stratify the prognosis (p < 0.05).Conclusion: We found a noninvasive imaging predictor that could guide the stratification of prognosis in ovarian cancer patients, which is related to the expression of Ki-67 in tumor tissues. This method is of great significance for the diagnosis and treatment of ovarian cancer.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Xu
- Department of Surgical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- *Correspondence: Hongzan Sun,
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Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol 2022; 12:892056. [PMID: 35965542 PMCID: PMC9363668 DOI: 10.3389/fonc.2022.892056] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.
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Affiliation(s)
- Jiaona Xu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuting Meng
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kefan Qiu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Win Topatana
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wei
- Department of Neurology, Affiliated Ningbo First Hospital, Ningbo, China
| | - Tianwen Chen
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110170] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.
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Laudicella R, Bauckneht M, Cuppari L, Donegani MI, Arnone A, Baldari S, Burger IA, Quartuccio N. Emerging applications of imaging in glioma: focus on PET/MRI and radiomics. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00464-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Li Z, Kong Z, Chen J, Li J, Li N, Yang Z, Wang Y, Liu Z. 18F-Boramino acid PET/CT in healthy volunteers and glioma patients. Eur J Nucl Med Mol Imaging 2021; 48:3113-3121. [PMID: 33590273 DOI: 10.1007/s00259-021-05212-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/18/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE In this work, the safety, biodistribution, and radiation dosimetry of large neutral amino acid transporter type-1 (LAT-1) targeting PET tracer 18F-trifluorobborate-derived tyrosine (denoted as 18F-FBY) has been investigated. It is designed as a first-in-human study in healthy volunteers and to assay LAT-1 expression level in glioma patients. METHODS Six healthy volunteers (3 M, 3 F) underwent whole-body PET acquisitions at multiple time points after bolus injection of 18F-FBY. Regions of interest (ROIs) were mapped manually on major organs, and then the time-activity curves (TACs) were obtained. Dosimetry was calculated with the OLINDA/EXM software. Thirteen patients who were suspected of glioma were scanned with PET/CT at 30 min after 18F-FBY injection. Within 7 days after PET/CT, the tumor was removed surgically, and LAT-1 immunohistochemical staining for LAT-1 was performed on tumor samples and correlated with 18F-FBY PET imaging. RESULTS 18F-FBY was well tolerated by all healthy volunteers, and no adverse symptoms were observed or reported. 18F-FBY is rapidly cleared from the blood circulation and excreted mainly through the kidneys and urinary tract. The effective dose (ED) was 0.0039 ± 0.0006 mSv/MBq. In 14 surgical confirmed gliomas (one of the patiens had two gliomas), 18F-FBY uptake increased consistently with tumor grade, with maximum standard uptake values (SUVmax) of 0.28 ± 0.14 and 2.84 ± 0.46 and tumor-to-normal contralateral activity (T/N) ratio of 2.30 ± 1.26 and 24.56 ± 6.32 in low- and high-grade tumors, respectively. In addition to the significant difference in the uptakes between low- and high-grade gliomas (P < 0.001), the immunohistochemical staining confirmed the positive correlations between the SUVmax, LAT-1 expression (r2 = 0.80, P < 0.001), and Ki-67 labeling index (r2 = 0.79, P < 0.001). CONCLUSION 18F-FBY is a PET tracer with favorable dosimetry profile and pharmacokinetics. It has the potential to assay LAT-1 expression in glioma patients and may provide imaging guidance for further boron neutron capture therapy of gliomas. TRIAL REGISTRATION clinicaltrials.gov (NCT03980431).
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Affiliation(s)
- Zhu Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of nuclear medicine, Peking University Cancer Hospital & Institute, Beijing, 100871, China
| | - Ziren Kong
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junyi Chen
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Jiyuan Li
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Nan Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of nuclear medicine, Peking University Cancer Hospital & Institute, Beijing, 100871, China
| | - Zhi Yang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of nuclear medicine, Peking University Cancer Hospital & Institute, Beijing, 100871, China.
| | - Yu Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Zhibo Liu
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China. .,Peking University-Tsinghua University Center for Life Sciences, Beijing, 100871, China.
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11
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Lohmann P, Meißner AK, Kocher M, Bauer EK, Werner JM, Fink GR, Shah NJ, Langen KJ, Galldiks N. Feature-based PET/MRI radiomics in patients with brain tumors. Neurooncol Adv 2021; 2:iv15-iv21. [PMID: 33521637 PMCID: PMC7829472 DOI: 10.1093/noajnl/vdaa118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Radiomics allows the extraction of quantitative features from medical images such as CT, MRI, or PET, thereby providing additional, potentially relevant diagnostic information for clinical decision-making. Because the computation of these features is performed highly automated on medical images acquired during routine follow-up, radiomics offers this information at low cost. Further, the radiomics features can be used alone or combined with other clinical or histomolecular parameters to generate predictive or prognostic mathematical models. These models can then be applied for various important diagnostic indications in neuro-oncology, for example, to noninvasively predict relevant biomarkers in glioma patients, to differentiate between treatment-related changes and local brain tumor relapse, or to predict treatment response. In recent years, amino acid PET has become an important diagnostic tool in patients with brain tumors. Therefore, the number of studies in patients with brain tumors investigating the potential of PET radiomics or combined PET/MRI radiomics is steadily increasing. This review summarizes current research regarding feature-based PET as well as combined PET/MRI radiomics in neuro-oncology.
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Affiliation(s)
- Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Anna-Katharina Meißner
- Department of Neurosurgery, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Martin Kocher
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.,Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany
| | - Elena K Bauer
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jan-Michael Werner
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gereon R Fink
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Juelich, Germany.,Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nadim J Shah
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Juelich, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Juelich, Germany.,Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), RWTH Aachen University, Aachen, Germany
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Juelich, Germany.,Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany.,Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Sun K, Liu Z, Li Y, Wang L, Tang Z, Wang S, Zhou X, Shao L, Sun C, Liu X, Jiang T, Wang Y, Tian J. Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images. Front Oncol 2020; 10:1096. [PMID: 32733804 PMCID: PMC7360821 DOI: 10.3389/fonc.2020.01096] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/02/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose: The present study aimed to evaluate the performance of radiomics features in the preoperative prediction of epileptic seizure following surgery in patients with LGG. Methods: This retrospective study collected 130 patients with LGG. Radiomics features were extracted from the T2-weighted MR images obtained before surgery. Multivariable Cox-regression with two nested leave-one-out cross validation (LOOCV) loops was applied to predict the prognosis, and elastic net was used in each LOOCV loop to select the predictive features. Logistic models were then built with the selected features to predict epileptic seizures at two time points. Student's t-tests were then used to compare the logistic model predicted probabilities of developing epilepsy in the epilepsy and non-epilepsy groups. The t-test was used to identify features that differentiated patients with early-onset epilepsy from their late-onset counterparts. Results: Seventeen features were selected with the two nested LOOCV loops. The index of concordance (C-index) of the Cox model was 0.683, and the logistic model predicted probabilities of seizure were significantly different between the epilepsy and non-epilepsy groups at each time point. Moreover, one feature was found to be significantly different between the patients with early- or late-onset epilepsy. Conclusion: A total of 17 radiomics features were correlated with postoperative epileptic seizures in patients with LGG and one feature was a significant predictor of the time of epilepsy onset.
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Affiliation(s)
- Kai Sun
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Yiming Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lei Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhenchao Tang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Xuezhi Zhou
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Caixia Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Xing Liu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jie Tian
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.,University of Chinese Academy of Science, Beijing, China
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Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, Mottaghy FM, Fink GR, Jon Shah N, Langen KJ. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods 2020; 188:112-121. [PMID: 32522530 DOI: 10.1016/j.ymeth.2020.06.003] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/28/2020] [Accepted: 06/03/2020] [Indexed: 02/02/2023] Open
Abstract
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various time-consuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.
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Affiliation(s)
- Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany.
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany; Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Kerpener Str. 62, 50937 Cologne, Germany
| | - Martin Kocher
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany; Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Kerpener Str. 62, 50937 Cologne, Germany
| | - Alexander Heinzel
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Christian P Filss
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Carina Stegmayr
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany
| | - Felix M Mottaghy
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Kerpener Str. 62, 50937 Cologne, Germany; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), P.Debeylaan 25, 6229 HX Maastricht, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands
| | - Gereon R Fink
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; JARA - BRAIN - Translational Medicine, Aachen, Germany; Department of Neurology, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, -4, -11), Research Center Juelich, Wilhelm-Johnen-Str., 52428 Juelich, Germany; Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Kerpener Str. 62, 50937 Cologne, Germany; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany; JARA - BRAIN - Translational Medicine, Aachen, Germany
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Fan Y, Feng M, Wang R. Application of Radiomics in Central Nervous System Diseases: a Systematic literature review. Clin Neurol Neurosurg 2019; 187:105565. [DOI: 10.1016/j.clineuro.2019.105565] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 10/12/2019] [Accepted: 10/13/2019] [Indexed: 01/01/2023]
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