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Xu H, Kim D, Zhao YY, Kim C, Song G, Hu Q, Kang H, Yoon J. Remote Control of Energy Transformation-Based Cancer Imaging and Therapy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2402806. [PMID: 38552256 DOI: 10.1002/adma.202402806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/24/2024] [Indexed: 04/06/2024]
Abstract
Cancer treatment requires precise tumor-specific targeting at specific sites that allows for high-resolution diagnostic imaging and long-term patient-tailorable cancer therapy; while, minimizing side effects largely arising from non-targetability. This can be realized by harnessing exogenous remote stimuli, such as tissue-penetrative ultrasound, magnetic field, light, and radiation, that enable local activation for cancer imaging and therapy in deep tumors. A myriad of nanomedicines can be efficiently activated when the energy of such remote stimuli can be transformed into another type of energy. This review discusses the remote control of energy transformation for targetable, efficient, and long-term cancer imaging and therapy. Such ultrasonic, magnetic, photonic, radiative, and radioactive energy can be transformed into mechanical, thermal, chemical, and radiative energy to enable a variety of cancer imaging and treatment modalities. The current review article describes multimodal energy transformation where a serial cascade or multiple types of energy transformation occur. This review includes not only mechanical, chemical, hyperthermia, and radiation therapy but also emerging thermoelectric, pyroelectric, and piezoelectric therapies for cancer treatment. It also illustrates ultrasound, magnetic resonance, fluorescence, computed tomography, photoluminescence, and photoacoustic imaging-guided cancer therapies. It highlights afterglow imaging that can eliminate autofluorescence for sustained signal emission after the excitation.
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Affiliation(s)
- Hai Xu
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Dahee Kim
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Yuan-Yuan Zhao
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Chowon Kim
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Guosheng Song
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China
| | - Qiongzheng Hu
- Qilu University of Technology (Shandong Academy of Sciences), Shandong Analysis and Test Center, Jinan, 250014, China
| | - Heemin Kang
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
- College of Medicine, Korea University, Seoul, 02841, Republic of Korea
| | - Juyoung Yoon
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul, 03760, Republic of Korea
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Kingsmore SF, Nofsinger R, Ellsworth K. Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review. NPJ Genom Med 2024; 9:17. [PMID: 38413639 PMCID: PMC10899612 DOI: 10.1038/s41525-024-00404-0] [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: 10/16/2023] [Accepted: 02/15/2024] [Indexed: 02/29/2024] Open
Abstract
Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in <5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases.
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Affiliation(s)
- Stephen F Kingsmore
- Rady Children's Institute for Genomic Medicine, Rady Children's Hospital, San Diego, CA, USA.
| | - Russell Nofsinger
- Rady Children's Institute for Genomic Medicine, Rady Children's Hospital, San Diego, CA, USA
| | - Kasia Ellsworth
- Rady Children's Institute for Genomic Medicine, Rady Children's Hospital, San Diego, CA, USA
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Manzarbeitia-Arroba B, Hodolic M, Pichler R, Osipova O, Soriano-Castrejón ÁM, García-Vicente AM. 18F-Fluoroethyl-L Tyrosine Positron Emission Tomography Radiomics in the Differentiation of Treatment-Related Changes from Disease Progression in Patients with Glioblastoma. Cancers (Basel) 2023; 16:195. [PMID: 38201621 PMCID: PMC10778283 DOI: 10.3390/cancers16010195] [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: 10/27/2023] [Revised: 12/10/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
The follow-up of glioma patients after therapeutic intervention remains a challenging topic, as therapy-related changes can emulate true progression in contrast-enhanced magnetic resonance imaging. 18F-fluoroethyl-tyrosine (18F-FET) is a radiopharmaceutical that accumulates in glioma cells due to an increased expression of L-amino acid transporters and, contrary to gadolinium, does not depend on blood-brain barrier disruption to reach tumoral cells. It has demonstrated a high diagnostic value in the differentiation of tumoral viability and pseudoprogression or any other therapy-related changes, especially when combining traditional visual analysis with modern radiomics. In this review, we aim to cover the potential role of 18F-FET positron emission tomography in everyday clinical practice when applied to the follow-up of patients after the first therapeutical intervention, early response evaluation, and the differential diagnosis between therapy-related changes and progression.
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Affiliation(s)
| | - Marina Hodolic
- Nuclear Medicine Department, Faculty of Medicine and Dentistry, Palacky University, 779 00 Olomouc, Czech Republic;
| | - Robert Pichler
- Institute of Nuclear Medicine Kepler University Hospital—Neuromed Campus, 4020 Linz, Austria; (R.P.); (O.O.)
| | - Olga Osipova
- Institute of Nuclear Medicine Kepler University Hospital—Neuromed Campus, 4020 Linz, Austria; (R.P.); (O.O.)
| | | | - Ana María García-Vicente
- Nuclear Medicine Department, University Hospital of Toledo, 45007 Toledo, Spain; (B.M.-A.); (Á.M.S.-C.)
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Ma X, Zhao Q. Application of artificial intelligence in oncology. Semin Cancer Biol 2023; 97:68-69. [PMID: 37977345 DOI: 10.1016/j.semcancer.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Affiliation(s)
- Xuelei Ma
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Qi Zhao
- Institute of Translational Medicine, Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau Special Administrative region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macau Special Administrative region of China.
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Ge M, Zhu Y, Wei M, Piao H, He M. Improving the efficacy of anti-EGFR drugs in GBM: Where we are going? Biochim Biophys Acta Rev Cancer 2023; 1878:188996. [PMID: 37805108 DOI: 10.1016/j.bbcan.2023.188996] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/11/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023]
Abstract
The therapies targeting mutations of driver genes in cancer have advanced into clinical trials for a variety of tumors. In glioblastoma (GBM), epidermal growth factor receptor (EGFR) is the most commonly mutated oncogene, and targeting EGFR has been widely investigated as a promising direction. However, the results of EGFR pathway inhibitors have not been satisfactory. Limited blood-brain barrier (BBB) permeability, drug resistance, and pathway compensation mechanisms contribute to the failure of anti-EGFR therapies. This review summarizes recent research advances in EGFR-targeted therapy for GBM and provides insight into the reasons for the unsatisfactory results of EGFR-targeted therapy. By combining the results of preclinical studies with those of clinical trials, we discuss that improved drug penetration across the BBB, the use of multi-target combinations, and the development of peptidomimetic drugs under the premise of precision medicine may be promising strategies to overcome drug resistance in GBM.
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Affiliation(s)
- Manxi Ge
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China; Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Shenyang, China
| | - Yan Zhu
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China; Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Shenyang, China
| | - Minjie Wei
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China; Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Shenyang, China; Liaoning Medical Diagnosis and Treatment Center, Shenyang, China.
| | - Haozhe Piao
- Department of Neurosurgery, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China.
| | - Miao He
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China; Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Shenyang, China.
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Park YJ, Park YS, Kim ST, Hyun SH. A Machine Learning Approach Using [ 18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol 2023; 25:897-910. [PMID: 37395887 DOI: 10.1007/s11307-023-01832-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs). PROCEDURES A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation. RESULTS We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001). CONCLUSIONS Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.
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Affiliation(s)
- Yong-Jin Park
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Nuclear Medicine, Ajou University Medical Center, Ajou University School of Medicine, 164, Worldcup-ro, Yeongtong-gu, Suwon, 16499, South Korea
| | - Young Suk Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
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Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol 2023; 95:75-87. [PMID: 37499847 DOI: 10.1016/j.semcancer.2023.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/29/2023]
Abstract
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
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Affiliation(s)
- Yichen Meng
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Yue Yang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Miao Hu
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Zheng Zhang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
| | - Xuhui Zhou
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
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