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Liu H, Liu J, Chen Y, Yang H, Fang J, Zeng X, Zhang J, Peng S, Liang Y, Zhuang R, Liu G, Zhang X, Guo Z. Development of STING probes and visualization of STING in multiple tumor types. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06919-z. [PMID: 39289182 DOI: 10.1007/s00259-024-06919-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2024] [Accepted: 09/04/2024] [Indexed: 09/19/2024]
Abstract
PURPOSE The stimulator of interferon genes (STING) is a critical component of the innate immune system and plays a pivotal role in tumor immunotherapy. Developing non-invasive in vivo diagnostic methods for visualizing STING is highly valuable for STING-related immunotherapy. This work aimed to build a noninvasive imaging platform that can dynamically and quantitatively monitor tumor STING expression. METHODS We investigated the in vivo positron emission tomography (PET) imaging of STING-expressing tumors (B16F10, MC38, and Panc02) with STING-targeted radioprobe ([18F]F-CRI1). The expression of STING in tumors was quantified, and correlation analysis was performed between these results and the outcomes of PET imaging. Furthermore, we optimized the structure of [18F]F-CRIn with polyethylene glycol (PEG) to improve the pharmacokinetic characteristics in vivo. A comprehensive comparison of the imaging and biodistribution results obtained with the optimized probes was conducted in the B16F10 tumors. RESULTS The PET imaging results showed that the uptake of [18F]F-CRI1 in tumors was positively correlated with the expression of STING in tumors (r = 0.9184, P < 0.001 at 0.5 h). The lipophilicity of the optimized probes was significantly reduced. As a result of employing optimized probes, B16F10 tumor-bearing mice exhibited significantly improved tumor visualization in PET imaging, along with a marked reduction in retention within non-target areas such as the gallbladder and intestines. Biodistribution experiments further validated the efficacy of probe optimization in reducing uptake in non-target areas. CONCLUSION In summary, this work demonstrated a promising pathway for the development of STING-targeted radioprobes, advancing in vivo PET imaging capabilities.
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Affiliation(s)
- Huanhuan Liu
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Jia Liu
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
- Department of Nuclear Technology and Application, China Institute of Atomic Energy, P.O. Box 275(12), Beijing, 102413, China
| | - Yingxi Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Hongzhang Yang
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Jianyang Fang
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Xinying Zeng
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Jingru Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Shilan Peng
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Yuanyuan Liang
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China
| | - Rongqiang Zhuang
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China.
| | - Gang Liu
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China.
| | - Xianzhong Zhang
- Theranostics and Translational Research Center, Institute of Clinical Medicine, Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
| | - Zhide Guo
- State Key Laboratory of Vaccines for Infectious Diseases, Center for Molecular Imaging and Translational Medicine, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, 4221-116 Xiang'An South Rd, Xiamen, 361102, China.
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Chen L, Chao Y, Li W, Wu Z, Wang Q. Soluble immune checkpoint molecules in cancer risk, outcomes prediction, and therapeutic applications. Biomark Res 2024; 12:95. [PMID: 39218939 PMCID: PMC11368031 DOI: 10.1186/s40364-024-00647-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 08/24/2024] [Indexed: 09/04/2024] Open
Abstract
Immunotherapy has emerged as a pivotal modality in cancer treatment, with immune checkpoint inhibitors effectively combating malignancies by impeding crucial pathways within the immune system and stimulating patients' immune responses. Soluble forms of immune checkpoints exhibit a remarkable diversity and can be readily tracked in circulation, holding immense potential as biomarkers for cancer treatment. An increasing number of studies focused on soluble immune checkpoints in cancer have emerged thanks to technological advancements. In this systematic review, we comprehensively summarized the recent studies on soluble immune checkpoints in human cancer risk prediction, outcome prediction, therapeutic applications, and potential molecular mechanisms, which demonstrated the promising future of soluble immune checkpoints in clinical applications. The clinical relevance of soluble immune checkpoints has been recognized in multiple cancers, yet the therapeutic applications and mechanisms remain obscure. Interpreting the impacts and mechanisms of soluble immune checkpoints could shed a light on the novel strategies of cancer screening, treatments, and outcome prediction.
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Affiliation(s)
- Lin Chen
- Department of Surgical Oncology, Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Zhejiang, PR China
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuqing Chao
- Department of Surgical Oncology, Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Zhejiang, PR China
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenjing Li
- Department of Surgical Oncology, Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Zhejiang, PR China
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhixia Wu
- Department of Service and Purchase, Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, China
| | - Qinchuan Wang
- Department of Surgical Oncology, Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Zhejiang, PR China.
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Foffano L, Vida R, Piacentini A, Molteni E, Cucciniello L, Da Ros L, Silvia B, Cereser L, Roncato R, Gerratana L, Puglisi F. Is ctDNA ready to outpace imaging in monitoring early and advanced breast cancer? Expert Rev Anticancer Ther 2024; 24:679-691. [PMID: 38855809 DOI: 10.1080/14737140.2024.2362173] [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: 01/06/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION Circulating tumor DNA (ctDNA) and radiological imaging are increasingly recognized as crucial elements in breast cancer management. While radiology remains the cornerstone for screening and monitoring, ctDNA holds distinctive advantages in anticipating diagnosis, recurrence, or progression, providing concurrent biological insights complementary to imaging results. AREAS COVERED This review delves into the current evidence on the synergistic relationship between ctDNA and imaging in breast cancer. It presents data on the clinical validity and utility of ctDNA in both early and advanced settings, providing insights into emerging liquid biopsy techniques like epigenetics and fragmentomics. Simultaneously, it explores the present and future landscape of imaging methodologies, particularly focusing on radiomics. EXPERT OPINION Numerous are the current technical, strategic, and economic challenges preventing the clinical integration of ctDNA analysis in the breast cancer monitoring. Understanding these complexities and devising targeted strategies is pivotal to effectively embedding this methodology into personalized patient care.
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Affiliation(s)
- Lorenzo Foffano
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Riccardo Vida
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | | | - Elisabetta Molteni
- Department of Medicine, University of Udine, Udine, Italy
- Weill Cornell Medicine, Department of Medicine, Division of Hematology-Oncology, New York, NY, USA
| | - Linda Cucciniello
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Lucia Da Ros
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Buriolla Silvia
- Department of Oncology, Santa Maria della Misericordia University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy
| | - Lorenzo Cereser
- Department of Medicine, University of Udine, Udine, Italy
- Azienda Sanitaria-Universitaria Friuli Centrale (ASUFC), University Hospital S. Maria della Misericordia, Udine, Italy
| | | | - Lorenzo Gerratana
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Fabio Puglisi
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
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Zakari S, Niels NK, Olagunju GV, Nnaji PC, Ogunniyi O, Tebamifor M, Israel EN, Atawodi SE, Ogunlana OO. Emerging biomarkers for non-invasive diagnosis and treatment of cancer: a systematic review. Front Oncol 2024; 14:1405267. [PMID: 39132504 PMCID: PMC11313249 DOI: 10.3389/fonc.2024.1405267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/05/2024] [Indexed: 08/13/2024] Open
Abstract
Cancer remains a global health challenge, necessitating continuous advancements in diagnostic and treatment strategies. This review focuses on the utility of non-invasive biomarkers in cancer diagnosis and treatment, their role in early detection, disease monitoring, and personalized therapeutic interventions. Through a systematic review of the literature, we identified 45 relevant studies that highlight the potential of these biomarkers across various cancer types, such as breast, prostate, lung, and colorectal cancers. The non-invasive biomarkers discussed include liquid biopsies, epigenetic markers, non-coding RNAs, exosomal cargo, and metabolites. Notably, liquid biopsies, particularly those based on circulating tumour DNA (ctDNA), have emerged as the most promising method for early, non-invasive cancer detection due to their ability to provide comprehensive genetic and epigenetic information from easily accessible blood samples. This review demonstrates how non-invasive biomarkers can facilitate early cancer detection, accurate subtyping, and tailored treatment strategies, thereby improving patient outcomes. It underscores the transformative potential of non-invasive biomarkers in oncology, highlighting their application for enhancing early detection, survival rates, and treatment precision in cancer care. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023474749 PROSPERO, identifier CRD42023474749.
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Affiliation(s)
- Suleiman Zakari
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, Nigeria
- Department of Biochemistry, College of Medicine, Federal University of Health Sciences Otukpo, Otukpo, Benue State, Nigeria
| | - Nguedia K. Niels
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, Nigeria
- Biotechnology Centre, University of Yaounde I, Yaounde, Cameroon
| | - Grace V. Olagunju
- Department of Molecular Biology, New Mexico State University, Las Cruces, NM, United States
| | - Precious C. Nnaji
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
| | - Oluwabusayo Ogunniyi
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
| | - Mercy Tebamifor
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, Nigeria
| | - Emmanuel N. Israel
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, Nigeria
| | - Sunday E. Atawodi
- Department of Biochemistry, Federal University Lokoja, Lokoja, Kogi State, Nigeria
| | - Olubanke Olujoke Ogunlana
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, Nigeria
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Lu Y, Houson HA, Gallegos CA, Mascioni A, Jia F, Aivazian A, Song PN, Lynch SE, Napier TS, Mansur A, Larimer BM, Lapi SE, Hanker AB, Sorace AG. Evaluating the immunologically "cold" tumor microenvironment after treatment with immune checkpoint inhibitors utilizing PET imaging of CD4 + and CD8 + T cells in breast cancer mouse models. Breast Cancer Res 2024; 26:104. [PMID: 38918836 PMCID: PMC11201779 DOI: 10.1186/s13058-024-01844-3] [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: 10/23/2023] [Accepted: 05/17/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Immune-positron emission tomography (PET) imaging with tracers that target CD8 and granzyme B has shown promise in predicting the therapeutic response following immune checkpoint blockade (ICB) in immunologically "hot" tumors. However, immune dynamics in the low T-cell infiltrating "cold" tumor immune microenvironment during ICB remain poorly understood. This study uses molecular imaging to evaluate changes in CD4 + T cells and CD8 + T cells during ICB in breast cancer models and examines biomarkers of response. METHODS [89Zr]Zr-DFO-CD4 and [89Zr]Zr-DFO-CD8 radiotracers were used to quantify changes in intratumoral and splenic CD4 T cells and CD8 T cells in response to ICB treatment in 4T1 and MMTV-HER2 mouse models, which represent immunologically "cold" tumors. A correlation between PET quantification metrics and long-term anti-tumor response was observed. Further biological validation was obtained by autoradiography and immunofluorescence. RESULTS Following ICB treatment, an increase in the CD8-specific PET signal was observed within 6 days, and an increase in the CD4-specific PET signal was observed within 2 days in tumors that eventually responded to immunotherapy, while no significant differences in CD4 or CD8 were found at the baseline of treatment that differentiated responders from nonresponders. Furthermore, mice whose tumors responded to ICB had a lower CD8 PET signal in the spleen and a higher CD4 PET signal in the spleen compared to non-responders. Intratumoral spatial heterogeneity of the CD8 and CD4-specific PET signals was lower in responders compared to non-responders. Finally, PET imaging, autoradiography, and immunofluorescence signals were correlated when comparing in vivo imaging to ex vivo validations. CONCLUSIONS CD4- and CD8-specific immuno-PET imaging can be used to characterize the in vivo distribution of CD4 + and CD8 + T cells in response to immune checkpoint blockade. Imaging metrics that describe the overall levels and distribution of CD8 + T cells and CD4 + T cells can provide insight into immunological alterations, predict biomarkers of response to immunotherapy, and guide clinical decision-making in those tumors where the kinetics of the response differ.
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Affiliation(s)
- Yun Lu
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Graduate Biomedical Sciences, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Hailey A Houson
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Carlos A Gallegos
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | | | - Fang Jia
- ImaginAb, Inc, Inglewood, CA, 90301, USA
| | | | - Patrick N Song
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Graduate Biomedical Sciences, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Shannon E Lynch
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Graduate Biomedical Sciences, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Tiara S Napier
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Graduate Biomedical Sciences, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Ameer Mansur
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Benjamin M Larimer
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Suzanne E Lapi
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Department of Chemistry, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Ariella B Hanker
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
- Departments of Radiology and Biomedical Engineering, O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Small Animal Imaging Facility, 1670 University Blvd, Birmingham, USA.
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Wang H, Zhang Y, Zhang H, Cao H, Mao J, Chen X, Wang L, Zhang N, Luo P, Xue J, Qi X, Dong X, Liu G, Cheng Q. Liquid biopsy for human cancer: cancer screening, monitoring, and treatment. MedComm (Beijing) 2024; 5:e564. [PMID: 38807975 PMCID: PMC11130638 DOI: 10.1002/mco2.564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/30/2024] Open
Abstract
Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.
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Affiliation(s)
- Hao Wang
- Department of NeurosurgeryThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Yi Zhang
- Department of NeurosurgeryThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Hao Zhang
- Department of NeurosurgeryThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Hui Cao
- Department of PsychiatryThe School of Clinical Medicine, Hunan University of Chinese MedicineChangshaChina
- Department of PsychiatryBrain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province)ChangshaChina
| | - Jinning Mao
- Health Management CenterThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Xinxin Chen
- Department of NeurosurgeryThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Liangchi Wang
- Department of NeurosurgeryFengdu People's Hospital, ChongqingChongqingChina
| | - Nan Zhang
- College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Peng Luo
- Department of OncologyZhujiang Hospital, Southern Medical UniversityGuangzhouChina
| | - Ji Xue
- Department of NeurosurgeryTraditional Chinese Medicine Hospital Dianjiang ChongqingChongqingChina
| | - Xiaoya Qi
- Health Management CenterThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Xiancheng Dong
- Department of Cerebrovascular DiseasesDazhou Central HospitalSichuanChina
| | - Guodong Liu
- Department of NeurosurgeryThe Second Affiliated Hospital, Chongqing Medical UniversityChongqingChina
| | - Quan Cheng
- Department of NeurosurgeryXiangya Hospital, Central South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya Hospital, Central South UniversityChangshaChina
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Pesapane F, Nicosia L, Tantrige P, Schiaffino S, Liguori A, Montesano M, Bozzini A, Rotili A, Cellina M, Orsi M, Penco S, Pizzamiglio M, Carrafiello G, Cassano E. Inter-reader agreement of breast magnetic resonance imaging and contrast-enhanced mammography in breast cancer diagnosis: a multi-reader retrospective study. Breast Cancer Res Treat 2023; 202:451-459. [PMID: 37747580 DOI: 10.1007/s10549-023-07093-w] [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: 05/08/2023] [Accepted: 08/11/2023] [Indexed: 09/26/2023]
Abstract
OBJECTIVE Breast magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM) are nowadays used in breast imaging but studies about their inter-reader agreement are lacking. Therefore, we compared the inter-reader agreement of CEM and MRI in breast cancer diagnosis in the same patients. METHODS Breast MRI and CEM exams performed in a single center (09/2020-09/2021) for an IRB-approved study were retrospectively and independently evaluated by four radiologists of two different centers with different levels of experience who were blinded to the clinical and other imaging data. The reference standard was the histological diagnosis or at least 1-year negative imaging follow-up. Inter-reader agreement was examined using Cohen's and Fleiss' kappa (κ) statistics and compared with the Wald test. RESULTS Of the 750 patients, 395 met inclusion criteria (44.5 ± 14 years old), with 752 breasts available for CEM and MRI. Overall agreement was moderate (κ = 0.60) for MRI and substantial (κ = 0.74) for CEM. For expert readers, the agreement was substantial (κ = 0.77) for MRI and almost perfect (κ = 0.82) for CEM; for non-expert readers was fair (κ = 0.39); and for MRI and moderate (κ = 0.57) for CEM. Pairwise agreement between expert readers and non-expert readers was moderate (κ = 0.50) for breast MRI and substantial (κ = 0.74) for CEM and it showed a statistically superior agreement of the expert over the non-expert readers only for MRI (p = 0.011) and not for CEM (p = 0.062). CONCLUSIONS The agreement of CEM was superior to that of MRI (p = 0.012), including for both expert (p = 0.031) and non-expert readers (p = 0.005).
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King's College Hospital NHS Foundation Trust, London, UK
| | - Simone Schiaffino
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), 6900, Lugano, Switzerland
| | - Alessandro Liguori
- Department of Radiology, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy
| | - Marta Montesano
- Department of Radiology, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy
| | - Anna Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Michaela Cellina
- Department of Radiology, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20131, Milan, Italy
| | - Marcello Orsi
- Department of Radiology, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20131, Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy
- Department of Health Sciences, University of Milan, 20122, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
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Zhang M, He G, Pan C, Yun B, Shen D, Meng M. Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning. J Cancer Res Ther 2023; 19:1589-1596. [PMID: 38156926 DOI: 10.4103/jcrt.jcrt_325_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 09/26/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS The diagnostic efficiencies of the VGG19, ResNet50, and DenseNet201 models were tested under the same dataset. The model with the highest performance was selected and modified utilizing three fine-tuning strategies (S1-3). Fifty additional lesions were selected to form the validation set to verify the generalization abilities of these models. The accuracy (Ac) of the different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), F1 score (), and area under the receiver operating characteristic curve (AUC), were primary performance indicators. Finally, the kappa test was used to compare the degree of agreement between the DTL models and pathological diagnosis in differentiating malignant from benign breast lesions. RESULTS The Pr, Rc, f1, and AUC of VGG19 (86.0%, 0.81, 0.81, and 0.81, respectively) were higher than those of DenseNet201 (70.0%, 0.61, 0.63, and 0.61, respectively) and ResNet50 (61.0%, 0.59, 0.59, and 0.59). After fine-tuning, the Pr, Rc, f1, and AUC of S1 (87.0%, 0.86, 0.86, and 0.86, respectively) were higher than those of VGG19. Notably, the degree of agreement between S1 and pathological diagnosis in differentiating malignant from benign breast lesions was 0.720 (κ = 0.720), which was higher than that of DenseNet201 (κ = 0.440), VGG19 (κ = 0.640), and ResNet50 (κ = 0.280). CONCLUSION The VGG19 model is an effective method for identifying benign and malignant breast lesions on DCE-MRI, and its performance can be further improved via fine-tuning. Overall, our findings insinuate that this technique holds potential clinical application value.
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Affiliation(s)
- Ming Zhang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital, Nanjing Medical University, Changzhou, Jiangsu Province, P.R. China
| | - Guangyuan He
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital, Nanjing Medical University, Changzhou, Jiangsu Province, P.R. China
| | - Changjie Pan
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital, Nanjing Medical University, Changzhou, Jiangsu Province, P.R. China
| | - Bing Yun
- Teaching and Research Department of English, Nanjing Forestry University Nanjing 210037, Jiangsu Province, P.R. China
| | - Dong Shen
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital, Nanjing Medical University, Changzhou, Jiangsu Province, P.R. China
| | - Mingzhu Meng
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital, Nanjing Medical University, Changzhou, Jiangsu Province, P.R. China
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9
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Pesapane F, Nicosia L, Cassano E. Updates on Breast Cancer. Cancers (Basel) 2023; 15:5392. [PMID: 38001652 PMCID: PMC10669992 DOI: 10.3390/cancers15225392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
This collection of 18 articles, comprising 12 original studies, 1 systematic review, and 5 reviews, is a collaborative effort by distinguished experts in breast cancer research, and it has been edited by Dr [...].
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (L.N.); (E.C.)
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10
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Starodubtseva NL, Tokareva AO, Rodionov VV, Brzhozovskiy AG, Bugrova AE, Chagovets VV, Kometova VV, Kukaev EN, Soares NC, Kovalev GI, Kononikhin AS, Frankevich VE, Nikolaev EN, Sukhikh GT. Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study. Biomedicines 2023; 11:1786. [PMID: 37509426 PMCID: PMC10376786 DOI: 10.3390/biomedicines11071786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/30/2023] Open
Abstract
Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.
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Affiliation(s)
- Natalia L Starodubtseva
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Department of Chemical Physics, Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - Alisa O Tokareva
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Valeriy V Rodionov
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Alexander G Brzhozovskiy
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Anna E Bugrova
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vitaliy V Chagovets
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Vlada V Kometova
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Evgenii N Kukaev
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Nelson C Soares
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Grigoriy I Kovalev
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Alexey S Kononikhin
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Vladimir E Frankevich
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Translational Medicine, Siberian State Medical University, 634050 Tomsk, Russia
| | - Evgeny N Nikolaev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Gennady T Sukhikh
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
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11
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Rotili A, Pesapane F, Signorelli G, Penco S, Nicosia L, Bozzini A, Meneghetti L, Zanzottera C, Mannucci S, Bonanni B, Cassano E. An Unenhanced Breast MRI Protocol Based on Diffusion-Weighted Imaging: A Retrospective Single-Center Study on High-Risk Population for Breast Cancer. Diagnostics (Basel) 2023; 13:1996. [PMID: 37370892 DOI: 10.3390/diagnostics13121996] [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: 03/01/2023] [Revised: 05/10/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
PURPOSE This study aimed to investigate the use of contrast-free magnetic resonance imaging (MRI) as an innovative screening method for detecting breast cancer in high-risk asymptomatic women. Specifically, the researchers evaluated the diagnostic performance of diffusion-weighted imaging (DWI) in this population. METHODS MR images from asymptomatic women, carriers of a germline mutation in either the BRCA1 or BRCA2 gene, collected in a single center from January 2019 to December 2021 were retrospectively evaluated. A radiologist with experience in breast imaging (R1) and a radiology resident (R2) independently evaluated DWI/ADC maps and, in case of doubts, T2-WI. The standard of reference was the pathological diagnosis through biopsy or surgery, or ≥1 year of clinical and radiological follow-up. Diagnostic performances were calculated for both readers with a 95% confidence interval (CI). The agreement was assessed using Cohen's kappa (κ) statistics. RESULTS Out of 313 women, 145 women were included (49.5 ± 12 years), totaling 344 breast MRIs with DWI/ADC maps. The per-exam cancer prevalence was 11/344 (3.2%). The sensitivity was 8/11 (73%; 95% CI: 46-99%) for R1 and 7/11 (64%; 95% CI: 35-92%) for R2. The specificity was 301/333 (90%; 95% CI: 87-94%) for both readers. The diagnostic accuracy was 90% for both readers. R1 recalled 40/344 exams (11.6%) and R2 recalled 39/344 exams (11.3%). Inter-reader reproducibility between readers was in moderate agreement (κ = 0.43). CONCLUSIONS In female carriers of a BRCA1/2 mutation, breast DWI supplemented with T2-WI allowed breast cancer detection with high sensitivity and specificity by a radiologist with extensive experience in breast imaging, which is comparable to other screening tests. The findings suggest that DWI and T2-WI have the potential to serve as a stand-alone method for unenhanced breast MRI screening in a selected population, opening up new perspectives for prospective trials.
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Affiliation(s)
- Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Signorelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Cristina Zanzottera
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Sara Mannucci
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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12
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An J, Yang J, Kwon H, Lim W, Kim YK, Moon BI. Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention. Sci Rep 2023; 13:5110. [PMID: 36991044 PMCID: PMC10060235 DOI: 10.1038/s41598-023-32227-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 03/24/2023] [Indexed: 03/31/2023] Open
Abstract
The incidence of breast cancer (BC) is increasing in South Korea, and diet is closely related to the high prevalence of BC. The microbiome directly reflects eating habits. In this study, a diagnostic algorithm was developed by analyzing the microbiome patterns of BC. Blood samples were collected from 96 patients with BC and 192 healthy controls. Bacterial extracellular vesicles (EVs) were collected from each blood sample, and next-generation sequencing (NGS) of bacterial EVs was performed. Microbiome analysis of patients with BC and healthy controls identified significantly higher bacterial abundances using EVs in each group and confirmed the receiver operating characteristic (ROC) curves. Using this algorithm, animal experiments were performed to determine which foods affect EV composition. Compared to BC and healthy controls, statistically significant bacterial EVs were selected from both groups, and a receiver operating characteristic (ROC) curve was drawn with a sensitivity of 96.4%, specificity of 100%, and accuracy of 99.6% based on the machine learning method. This algorithm is expected to be applicable to medical practice, such as in health checkup centers. In addition, the results obtained from animal experiments are expected to select and apply foods that have a positive effect on patients with BC.
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Affiliation(s)
- Jeongshin An
- Institute of Convergence Medicine Research, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, 1071 Anyangcheon-ro, Yangcheon-gu, Seoul, 07985, Republic of Korea
- Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, 1071 Anyangcheon-ro, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Jinho Yang
- MD Healthcare, Room 1303, Woori Technology Inc. building, Sangam-dong, World Cup Buk-ro 56-gil, Mapo-gu, Seoul, Republic of Korea
- Department of Occupational Health and Safety, Semyung University, 65 Semyung-ro, Jecheon, Chungcheongbuk-do, 27136, Republic of Korea
| | - Hyungju Kwon
- Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, 1071 Anyangcheon-ro, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Woosung Lim
- Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, 1071 Anyangcheon-ro, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Yoon-Keun Kim
- MD Healthcare, Room 1303, Woori Technology Inc. building, Sangam-dong, World Cup Buk-ro 56-gil, Mapo-gu, Seoul, Republic of Korea.
| | - Byung-In Moon
- Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, 1071 Anyangcheon-ro, Yangcheon-gu, Seoul, 07985, Republic of Korea.
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13
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Pesapane F, De Marco P, Rapino A, Lombardo E, Nicosia L, Tantrige P, Rotili A, Bozzini AC, Penco S, Dominelli V, Trentin C, Ferrari F, Farina M, Meneghetti L, Latronico A, Abbate F, Origgi D, Carrafiello G, Cassano E. How Radiomics Can Improve Breast Cancer Diagnosis and Treatment. J Clin Med 2023; 12:jcm12041372. [PMID: 36835908 PMCID: PMC9963325 DOI: 10.3390/jcm12041372] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-574891
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rapino
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Eleonora Lombardo
- UOC of Diagnostic Imaging, Policlinico Tor Vergata University, 00133 Rome, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Chiara Trentin
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Mariagiorgia Farina
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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14
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Vladimirov N, Perlman O. Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response. Int J Mol Sci 2023; 24:3151. [PMID: 36834563 PMCID: PMC9959624 DOI: 10.3390/ijms24043151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.
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Affiliation(s)
- Nikita Vladimirov
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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15
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Li H, Wang S, Li X, Cheng C, Shen X, Wang T. Dual-Channel Detection of Breast Cancer Biomarkers CA15-3 and CEA in Human Serum Using Dialysis-Silicon Nanowire Field Effect Transistor. Int J Nanomedicine 2022; 17:6289-6299. [PMID: 36536938 PMCID: PMC9758920 DOI: 10.2147/ijn.s391234] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/03/2022] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is the most common malignant tumors and the leading cause of cancer deaths among women. The early diagnosis and treatment of BC are effective measures that can increase survival rates and reduce mortality. Carbohydrate antigens 15-3 (CA15-3) and carcinoma embryonic antigens (CEA) have been regarded as the most two valuable tumor markers of BC. The combined detection of CA15-3 and CEA could improve the sensitivity and accuracy of early diagnosis for BC. METHODS The multi-channel double-gate silicon nanowire field effect transistor (SiNW-FET) biosensors were fabricated by using the top-down semiconductor manufacturing technology. By surface modification of the different SiNW surfaces with monoclonal CA15-3 and CEA antibodies separately, the prepared SiNW-FET was processed into biosensor for dual-channel detection of CA15-3 and CEA. RESULTS The prepared SiNW-FET biosensors were proved to have high sensitivity and specificity for the dual-channel detection of CA15-3 and CEA, and the detection limit is as low as 0.1U/mL CA15-3 and 0.01 ng/mL CEA. Moreover, the SiNW-FET biosensors were able to detect CA15-3 and CEA in serum by connecting a miniature hemodialyzer. CONCLUSION The present study reported a SiNW-FET biosensor for dual-channel detection of breast cancer biomarkers CA15-3 and CEA in serum, which has potential clinical application value for the early diagnosis and curative effect observation of BC.
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Affiliation(s)
- Hang Li
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
| | - Shuai Wang
- Department of General Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Xiaosong Li
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
| | - Cong Cheng
- Department of General Surgery, Nanjing Medical University Affiliated Wuxi People’s Hospital, Wuxi, Jiangsu Province, People’s Republic of China
| | - Xiping Shen
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
| | - Tong Wang
- Department of General Surgery, Nanjing Medical University Affiliated Wuxi People’s Hospital, Wuxi, Jiangsu Province, People’s Republic of China
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16
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Guo Z, Xie J, Wan Y, Zhang M, Qiao L, Yu J, Chen S, Li B, Yao Y. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Open Life Sci 2022; 17:1600-1611. [PMID: 36561500 PMCID: PMC9743193 DOI: 10.1515/biol-2022-0517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/24/2022] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.
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Affiliation(s)
- Zicheng Guo
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Jiping Xie
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Yi Wan
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Min Zhang
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Liang Qiao
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Jiaxuan Yu
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Sijing Chen
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Bingxin Li
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Yongqiang Yao
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
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17
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Classic and New Markers in Diagnostics and Classification of Breast Cancer. Cancers (Basel) 2022; 14:cancers14215444. [PMID: 36358862 PMCID: PMC9654192 DOI: 10.3390/cancers14215444] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022] Open
Abstract
Simple Summary With ever-increasing incidence, breast cancer is considered a most diagnosed type of cancer among women worldwide. Breast cancer arises through malignant transformation of ductal or lobular cells in female (or male) breast and the genetic, phenotypic and morphological heterogeneity has an effect on tumour’s behaviour, thereby instigating a need for individual personalized therapy. A traditional assessment of tumour’s characteristics involves a biopsy and histological analysis of a tumour tissue, and in recent years has been accompanied by analysis of molecular biomarkers to enhance the results. In this work we aimed to thoroughly investigate the latest data in this field of study and give a comprehensive review of novel molecular biomarkers of breast cancer and methodologies used to analyse them. Abstract Breast cancer remains the most frequently diagnosed form of female’s cancer, and in recent years it has become the most common cause of cancer death in women worldwide. Like many other tumours, breast cancer is a histologically and biologically heterogeneous disease. In recent years, considerable progress has been made in diagnosis, subtyping, and complex treatment of breast cancer with the aim of providing best suited tumour-specific personalized therapy. Traditional methods for breast cancer diagnosis include mammography, MRI, biopsy and histological analysis of tumour tissue in order to determine classical markers such as estrogen and progesterone receptors (ER, PR), cytokeratins (CK5/6, CK14, C19), proliferation index (Ki67) and human epidermal growth factor type 2 receptor (HER2). In recent years, these methods have been supplemented by modern molecular methodologies such as next-generation sequencing, microRNA, in situ hybridization, and RT-qPCR to identify novel molecular biomarkers. MicroRNAs (miR-10b, miR-125b, miR145, miR-21, miR-155, mir-30, let-7, miR-25-3p), altered DNA methylation and mutations of specific genes (p16, BRCA1, RASSF1A, APC, GSTP1), circular RNA (hsa_circ_0072309, hsa_circRNA_0001785), circulating DNA and tumour cells, altered levels of specific proteins (apolipoprotein C-I), lipids, gene polymorphisms or nanoparticle enhanced imaging, all these are promising diagnostic and prognostic tools to disclose any specific features from the multifaceted nature of breast cancer to prepare best suited individualized therapy.
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Liu J, Peng X, Yang Y, Zhang Y, Han M, Shi X, Zheng J, Li T, Chen J, Lv W, Liu Y, Qi Y, Zhang L, Liu Q. The value of hsa_circ_0058514 in plasma extracellular vesicles for breast cancer. Front Oncol 2022; 12:995196. [PMID: 36387225 PMCID: PMC9663982 DOI: 10.3389/fonc.2022.995196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/04/2022] [Indexed: 01/25/2023] Open
Abstract
The aim of this study was to investigate the diagnostic value of hsa_circ_0058514 in plasma extracellular vesicles (EVs) in BC patients and its predictive value for neoadjuvant chemotherapy. The expression of hsa_circ_0058514 in a large sample of BC plasma and healthy subjects' plasma was detected by qPCR, and the ROC curve was drawn to verify its diagnostic value as a plasma tumor marker. Furthermore, the association between the expression of hsa_circ_0058514 and clinicopathological characteristics before and after treatment was detected in the plasma of 40 pairs of BC patients undergoing neoadjuvant therapy. The expression level of hsa_circ_0058514 in the plasma of BC patients was significantly higher than that of healthy subjects. The ROC curve showed that plasma hsa_circ_0058514 ROC in differentiating non-metastatic BC and healthy people had better diagnostic efficiency than conventional tumor markers CA153, CA125, and CEA. In patients with neoadjuvant therapy, the decrease in plasma hsa_circ_0058514 value before and after treatment correlated with pathological MP grade (r = 0.444, p = 0.004) and imaging tumor regression value (r = 0.43, p = 0.005) positive correlation. The detection of hsa_circ_0058514 in both extracellular vesicles of BC cell culture medium and human plasma was demonstrated. Hsa_circ_0058514 is detected in the plasma from BC cells secreted in the form of vesicles. Hsa_circ_0058514 can be used as an early plasma biological indicator for the diagnosis of BC in clinical applications, with a higher risk of recurrence and metastasis, and as a predictor of the effect of neoadjuvant therapy to guide the clinical use of neoadjuvant therapy.
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Affiliation(s)
- Jiani Liu
- Department of Breast Surgery, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Xinyu Peng
- Department of Gastrointestinal Surgery, Affiliated Hospital of Hebei University, Baoding, China
| | - Yang Yang
- Department of Oncology, Affiliated Hospital of Hebei University, Baoding, China
| | - Yao Zhang
- Department of Plastic Surgery, Hangzhou Xiaoshan Yaoran Medical Cosmetology Clinic Co. Ltd, Hangzhou, China
| | - Meng Han
- Department of Breast Surgery, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Xiaohui Shi
- Department of Breast Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jie Zheng
- Department of Breast Surgery, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Tong Li
- Graduate school of Chengde Medical University, Chengde, China
| | - Jinxia Chen
- Clinical Laboratory of the Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Weihua Lv
- Clinical Laboratory of the Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yunjiang Liu
- Department of Breast Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China,*Correspondence: Yunjiang Liu, ; Yixin Qi,
| | - Yixin Qi
- Department of Breast Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China,*Correspondence: Yunjiang Liu, ; Yixin Qi,
| | - Lei Zhang
- School of Nursing, Hebei Medical University, Shijiazhuang, China
| | - Qi Liu
- School of Nursing, Hebei Medical University, Shijiazhuang, China
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19
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Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic. RADIATION 2021. [DOI: 10.3390/radiation1040022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 crisis has exposed some of the most pressing challenges affecting healthcare and highlighted the benefits that robust integration of digital and AI technologies in the healthcare setting may bring. Although medical solutions based on AI are growing rapidly, regulatory issues and policy initiatives including ownership and control of data, data sharing, privacy protection, telemedicine, and accountability need to be carefully and continually addressed as AI research requires robust and ethical guidelines, demanding an update of the legal and regulatory framework all over the world. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. A global effort is needed for an open, mature conversation about the best possible way to guard against and mitigate possible harms to realize the potential of AI across health systems in a respectful and ethical way. This conversation must include national and international policymakers, physicians, digital health and machine learning leaders from industry and academia. If this is done properly and in a timely fashion, the potential of AI in healthcare will be realized.
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20
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Sardarabadi P, Kojabad AA, Jafari D, Liu CH. Liquid Biopsy-Based Biosensors for MRD Detection and Treatment Monitoring in Non-Small Cell Lung Cancer (NSCLC). BIOSENSORS 2021; 11:394. [PMID: 34677350 PMCID: PMC8533977 DOI: 10.3390/bios11100394] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022]
Abstract
Globally, non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths. Despite advancements in chemotherapy and targeted therapies, the 5-year survival rate has remained at 16% for the past forty years. Minimal residual disease (MRD) is described as the existence of either isolated tumour cells or circulating tumour cells in biological liquid of patients after removal of the primary tumour without any clinical signs of cancer. Recently, liquid biopsy has been promising as a non-invasive method of disease monitoring and treatment guidelines as an MRD marker. Liquid biopsy could be used to detect and assess earlier stages of NSCLC, post-treatment MRD, resistance to targeted therapies, immune checkpoint inhibitors (ICIs) and tumour mutational burden. MRD surveillance has been proposed as a potential marker for lung cancer relapse. Principally, biosensors provide the quantitative analysis of various materials by converting biological functions into quantifiable signals. Biosensors are usually operated to detect antibodies, enzymes, DNA, RNA, extracellular vesicles (EVs) and whole cells. Here, we present a category of biosensors based on the signal transduction method for identifying biosensor-based biomarkers in liquid biopsy specimens to monitor lung cancer treatment.
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Affiliation(s)
- Parvaneh Sardarabadi
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 30044, Taiwan;
| | - Amir Asri Kojabad
- Department of Hematology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran 14535, Iran;
| | - Davod Jafari
- Department of Medical Biotechnology, School of Allied Medicine, Iran University of Medical Sciences, Tehran 14535, Iran;
| | - Cheng-Hsien Liu
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 30044, Taiwan;
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30044, Taiwan
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21
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Alcalay M, Jereczek-Fossa BA, Pepa M, Volpe S, Zaffaroni M, Fiore F, Marvaso G, Pravettoni G, Curigliano G, Santaguida S, Pelicci PG. Biomedical omics: first insights of a new MSc degree of the University of Milan. TUMORI JOURNAL 2021; 108:6-11. [PMID: 34585604 DOI: 10.1177/03008916211047268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The advent of technologies allowing the global analysis of biological phenomena, referred to as "omics" (genomics, epigenomics, proteomics, metabolomics, microbiomics, radiomics, and radiogenomics), has revolutionized the study of human diseases and traced the path for quantitative personalized medicine. The newly inaugurated Master of Science Program in Biomedical Omics of the University of Milan, Italy, aims at addressing the unmet need to create professionals with a broad understanding of omics disciplines. The course is structured over 2 years and admits students with a bachelor's degree in biotechnology, biology, chemistry, or pharmaceutical sciences. All teaching activities are fully held in English. A total of nine students enrolled in the first academic year and attended the courses of radiomics, genomics and epigenomics, proteomics, and high-throughput screenings, and their feedback was evaluated by means of an online questionnaire. Faculty with different backgrounds were recruited according to the subject. Due to restrictions imposed by the coronavirus disease 2019 (COVID-19) pandemic, laboratory activities were temporarily suspended, while lectures, journal clubs, and examinations were mainly held online. After the end of the first semester, despite the difficulties brought on by the COVID-19 pandemic, the course overall met the expectations of the students, specifically regarding teaching effectiveness, interpersonal interactions with the lecturers, and courses organization. Future efforts will be undertaken to better calibrate the overall workload of the course and to implement the most relevant suggestions from the students together with omics science evolution in order to guarantee state-of-the-art omics teaching and to prepare future omics specialists.
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Affiliation(s)
- Myriam Alcalay
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | | | - Giulia Marvaso
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Gabriella Pravettoni
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Applied Division for Cognitive and Psychological Sciences, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giuseppe Curigliano
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Division of Early Drug Development, European Institute of Oncology, IRCCS, Milan, Italy
| | - Stefano Santaguida
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
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22
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Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis. Cancers (Basel) 2021; 13:cancers13174271. [PMID: 34503081 PMCID: PMC8428336 DOI: 10.3390/cancers13174271] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/19/2021] [Indexed: 12/29/2022] Open
Abstract
Simple Summary Nowadays, the only widely recognized method for evaluating the efficacy of neoadjuvant chemotherapy is the assessment of the pathological response through surgery. However, delivering chemotherapy to not-responders could expose them to unnecessary drug toxicity with delayed access to other potentially effective therapies. Radiomics could be useful in the early detection of resistance to chemotherapy, which is crucial for switching treatment strategy. We determined whether tumor radiomic features extracted from a highly homogeneous database of breast MRI can improve the prediction of response to chemotherapy in patients with breast cancer, in addiction to biological characteristics, potentially avoiding unnecessary treatment. Abstract Objectives: We aimed to determine whether radiomic features extracted from a highly homogeneous database of breast MRI could non-invasively predict pathological complete responses (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Methods: One hundred patients with breast cancer receiving NACT in a single center (01/2017–06/2019) and undergoing breast MRI were retrospectively evaluated. For each patient, radiomic features were extracted within the biopsy-proven tumor on T1-weighted (T1-w) contrast-enhanced MRI performed before NACT. The pCR to NACT was determined based on the final surgical specimen. The association of clinical/biological and radiomic features with response to NACT was evaluated by univariate and multivariable analysis by using random forest and logistic regression. The performances of all models were assessed using the areas under the receiver operating characteristic curves (AUC) with 95% confidence intervals (CI). Results: Eighty-three patients (mean (SD) age, 47.26 (8.6) years) were included. Patients with HER2+, basal-like molecular subtypes and Ki67 ≥ 20% presented a pCR to NACT more frequently; the clinical/biological model’s AUC (95% CI) was 0.81 (0.71–0.90). Using 136 representative radiomics features selected through cluster analysis from the 1037 extracted features, a radiomic score was calculated to predict the response to NACT, with AUC (95% CI): 0.64 (0.51–0.75). After combining the clinical/biological and radiomics models, the AUC (95% CI) was 0.83 (0.73–0.92). Conclusions: MRI-based radiomic features slightly improved the pre-treatment prediction of pCR to NACT, in addiction to biological characteristics. If confirmed on larger cohorts, it could be helpful to identify such patients, to avoid unnecessary treatment.
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Shoukry M, Broccard S, Kaplan J, Gabriel E. The Emerging Role of Circulating Tumor DNA in the Management of Breast Cancer. Cancers (Basel) 2021; 13:3813. [PMID: 34359713 PMCID: PMC8345044 DOI: 10.3390/cancers13153813] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 12/30/2022] Open
Abstract
With the incidence of breast cancer steadily rising, it is important to explore novel technologies that can allow for earlier detection of disease as well more a personalized and effective treatment approach. The concept of "liquid biopsies" and the data they provide have been increasingly studied in the recent decades. More specifically, circulating tumor DNA (ctDNA) has emerged as a potential biomarker for various cancers, including breast cancer. While methods such as mammography and tissue biopsies are the current standards for the detection and surveillance of breast cancer, ctDNA analysis has shown some promise. This review discusses the versatility of ctDNA by exploring its multiple emerging uses for the management of breast cancer. Its efficacy is also compared to current biomarkers and technologies.
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Affiliation(s)
- Mira Shoukry
- Department of Surgery, Section of Surgical Oncology, Mayo Clinic, Jacksonville, FL 32224, USA; (S.B.); (J.K.); (E.G.)
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24
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Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future. ACTA ACUST UNITED AC 2021; 28:2351-2372. [PMID: 34202321 PMCID: PMC8293249 DOI: 10.3390/curroncol28040217] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/14/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.
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25
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Perspectives on the Systemic Staging in Newly Diagnosed Breast Cancer. Clin Breast Cancer 2021; 21:309-316. [PMID: 33962905 DOI: 10.1016/j.clbc.2021.03.010] [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: 10/08/2020] [Revised: 03/06/2021] [Accepted: 03/23/2021] [Indexed: 11/23/2022]
Abstract
Breast cancer is a complex disease, and accurate systemic staging is an essential aspect of the evaluation of a patient with newly diagnosed breast cancer. Considering that the chance of having metastatic disease at breast cancer diagnosis is different in each patient and depends on a variety of anatomic and biologic factors, it is crucial to understand that some populations may benefit from more intensive staging because their pretest probability of metastatic disease is higher than that of the average patient. Identifying these patients with de novo stage IV breast cancer is associated with substantial prognostic and therapeutic implications. Unfortunately, recent advances in understanding breast cancer heterogeneity and molecular biology have not been incorporated in the international guidelines and recommendations about imaging examinations for detecting de novo metastatic breast cancer. This review article discusses important issues regarding the rationale for performing systemic staging, addresses current and innovative imaging methods, and proposes an algorithm for systemic staging in patients with newly diagnosed breast cancer.
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26
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Pesapane F, Rotili A, Penco S, Montesano M, Agazzi GM, Dominelli V, Trentin C, Pizzamiglio M, Cassano E. Inter-Reader Agreement of Diffusion-Weighted Magnetic Resonance Imaging for Breast Cancer Detection: A Multi-Reader Retrospective Study. Cancers (Basel) 2021; 13:cancers13081978. [PMID: 33924033 PMCID: PMC8073591 DOI: 10.3390/cancers13081978] [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: 03/03/2021] [Revised: 04/12/2021] [Accepted: 04/16/2021] [Indexed: 11/16/2022] Open
Abstract
PURPOSE In order to evaluate the use of un-enhanced magnetic resonance imaging (MRI) for detecting breast cancer, we evaluated the accuracy and the agreement of diffusion-weighted imaging (DWI) through the inter-reader reproducibility between expert and non-expert readers. MATERIAL AND METHODS Consecutive breast MRI performed in a single centre were retrospectively evaluated by four radiologists with different levels of experience. The per-breast standard of reference was the histological diagnosis from needle biopsy or surgical excision, or at least one-year negative follow-up on imaging. The agreement across readers (by inter-reader reproducibility) was examined for each breast examined using Cohen's and Fleiss' kappa (κ) statistics. The Wald test was used to test the difference in inter-reader agreement between expert and non-expert readers. RESULTS Of 1131 examinations, according to our inclusion and exclusion criteria, 382 women were included (49.5 ± 12 years old), 40 of them with unilateral mastectomy, totaling 724 breasts. Overall inter-reader reproducibility was substantial (κ = 0.74) for expert readers and poor (κ = 0.37) for non- expert readers. Pairwise agreement between expert readers and non-expert readers was moderate (κ = 0.60) and showed a statistically superior agreement of the expert readers over the non-expert readers (p = 0.003). CONCLUSIONS DWI showed substantial inter-reader reproducibility among expert-level readers. Pairwise comparison showed superior agreement of the expert readers over the non-expert readers, with the expert readers having higher inter-reader reproducibility than the non-expert readers. These findings open new perspectives for prospective studies investigating the actual role of DWI as a stand-alone method for un-enhanced breast MRI.
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Affiliation(s)
- Filippo Pesapane
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
- Correspondence:
| | - Anna Rotili
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
| | - Silvia Penco
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
| | - Marta Montesano
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
| | | | - Valeria Dominelli
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
| | - Chiara Trentin
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
| | - Maria Pizzamiglio
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
| | - Enrico Cassano
- Radiology Department, Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (S.P.); (M.M.); (V.D.); (C.T.); (M.P.); (E.C.)
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Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis 2020; 14:e0008960. [PMID: 33362244 PMCID: PMC7757819 DOI: 10.1371/journal.pntd.0008960] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/08/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. METHODOLOGY/PRINCIPAL FINDINGS Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. CONCLUSIONS/SIGNIFICANCE We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.
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Cucchiara F, Del Re M, Valleggi S, Romei C, Petrini I, Lucchesi M, Crucitta S, Rofi E, De Liperi A, Chella A, Russo A, Danesi R. Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer. Front Oncol 2020; 10:593831. [PMID: 33489892 PMCID: PMC7819134 DOI: 10.3389/fonc.2020.593831] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022] Open
Abstract
Background EGFR-positive Non-small Cell Lung Cancer (NSCLC) is a dynamic entity and tumor progression and resistance to tyrosine kinase inhibitors (TKIs) arise from the accumulation, over time and across different disease sites, of subclonal genetic mutations. For instance, the occurrence of EGFR T790M is associated with resistance to gefitinib, erlotinib, and afatinib, while EGFR C797S causes osimertinib to lose activity. Sensitive technologies as radiomics and liquid biopsy have great potential to monitor tumor heterogeneity since they are both minimally invasive, easy to perform, and can be repeated over patient’s follow-up, enabling the extraction of valuable information. Yet, to date, there are no reported cases associating liquid biopsy and radiomics during treatment. Case presentation In this case series, seven patients with metastatic EGFR-positive NSCLC have been monitored during target therapy. Plasma-derived cell free DNA (cfDNA) was analyzed by a digital droplet PCR (ddPCR), while radiomic analyses were performed using the validated LifeX® software on computed tomography (CT)-images. The dynamics of EGFR mutations in cfDNA was compared with that of radiomic features. Then, for each EGFR mutation, a radiomic signature was defines as the sum of the most predictive features, weighted by their corresponding regression coefficients for the least absolute shrinkage and selection operator (LASSO) model. The receiver operating characteristic (ROC) curves were computed to estimate their diagnostic performance. The signatures achieved promising performance on predicting the presence of EGFR mutations (R2 = 0.447, p <0.001 EGFR activating mutations R2 = 0.301, p = 0.003 for T790M; and R2 = 0.354, p = 0.001 for activating plus resistance mutations), confirmed by ROC analysis. Conclusion To our knowledge, these are the first cases to highlight a potentially promising strategy to detect clonal heterogeneity and ultimately identify patients at risk of progression during treatment. Together, radiomics and liquid biopsy could detect the appearance of new mutations and therefore suggest new therapeutic management.
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Affiliation(s)
- Federico Cucchiara
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Marzia Del Re
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Simona Valleggi
- Pneumology Unit, Cardiovascular and Thoracic Department, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Chiara Romei
- Radiology Unit 2, Department of Diagnostics and Imaging, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Iacopo Petrini
- Pneumology Unit, Cardiovascular and Thoracic Department, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.,Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Pneumology Unit, Cardiovascular and Thoracic Department, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Stefania Crucitta
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eleonora Rofi
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Annalisa De Liperi
- Radiology Unit 2, Department of Diagnostics and Imaging, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Antonio Chella
- Pneumology Unit, Cardiovascular and Thoracic Department, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Antonio Russo
- Section of Medical Oncology, Department of Surgical, Oncological and Stomatological Sciences, University of Palermo, Palermo, Italy
| | - Romano Danesi
- Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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29
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Pesapane F, Downey K, Rotili A, Cassano E, Koh DM. Imaging diagnosis of metastatic breast cancer. Insights Imaging 2020; 11:79. [PMID: 32548731 PMCID: PMC7297923 DOI: 10.1186/s13244-020-00885-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 05/22/2020] [Indexed: 12/11/2022] Open
Abstract
Numerous imaging modalities may be used for the staging of women with advanced breast cancer. Although bone scintigraphy and multiplanar-CT are the most frequently used tests, others including PET, MRI and hybrid scans are also utilised, with no specific recommendations of which test should be preferentially used. We review the evidence behind the imaging modalities that characterise metastases in breast cancer and to update the evidence on comparative imaging accuracy.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO - European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milano, MI, Italy.
| | - Kate Downey
- Department of Breast Radiology, Royal Marsden Hospital, Downs Road, Sutton, SM2 5PT, UK
| | - Anna Rotili
- Breast Imaging Division, IEO - European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milano, MI, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO - European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milano, MI, Italy
| | - Dow-Mu Koh
- Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK.,Department of Radiology, Royal Marsden Hospital, Downs Road, Sutton, SM2 5PT, UK
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