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Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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Araujo-Filho JAB, Mayoral M, Horvat N, Santini F, Gibbs P, Ginsberg MS. Radiogenomics in personalized management of lung cancer patients: Where are we? Clin Imaging 2022; 84:54-60. [DOI: 10.1016/j.clinimag.2022.01.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 01/03/2022] [Accepted: 01/24/2022] [Indexed: 11/03/2022]
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Park S, Lee C, Ku BM, Kim M, Park WY, Kim NKD, Ahn MJ. Paired analysis of tumor mutation burden calculated by targeted deep sequencing panel and whole exome sequencing in non-small cell lung cancer. BMB Rep 2021. [PMID: 34154699 PMCID: PMC8328823 DOI: 10.5483/bmbrep.2021.54.7.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Owing to rapid advancements in NGS (next generation sequen-cing), genomic alteration is now considered an essential pre-dictive biomarkers that impact the treatment decision in many cases of cancer. Among the various predictive biomarkers, tumor mutation burden (TMB) was identified by NGS and was con-sidered to be useful in predicting a clinical response in cancer cases treated by immunotherapy. In this study, we directly com-pared the lab-developed-test (LDT) results by target sequencing panel, K-MASTER panel v3.0 and whole-exome sequencing (WES) to evaluate the concordance of TMB. As an initial step, the reference materials (n = 3) with known TMB status were used as an exploratory test. To validate and evaluate TMB, we used one hundred samples that were acquired from surgically resected tissues of non-small cell lung cancer (NSCLC) patients. The TMB of each sample was tested by using both LDT and WES methods, which extracted the DNA from samples at the same time. In addition, we evaluated the impact of capture re-gion, which might lead to different values of TMB; the evalu-ation of capture region was based on the size of NGS and target sequencing panels. In this pilot study, TMB was evalu-ated by LDT and WES by using duplicated reference samples; the results of TMB showed high concordance rate (R2 = 0.887). This was also reflected in clinical samples (n = 100), which showed R2 of 0.71. The difference between the coding sequence ratio (3.49%) and the ratio of mutations (4.8%) indicated that the LDT panel identified a relatively higher number of mutations. It was feasible to calculate TMB with LDT panel, which can be useful in clinical practice. Furthermore, a customized approach must be developed for calculating TMB, which differs according to cancer types and specific clinical settings.
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Affiliation(s)
- Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Chung Lee
- Geninus Inc., Seoul 05836, 3Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Bo Mi Ku
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Minjae Kim
- Geninus Inc., Seoul 05836, 3Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Woong-Yang Park
- Geninus Inc., Seoul 05836, 3Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Nayoung K. D. Kim
- Geninus Inc., Seoul 05836, 3Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
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Lee SH, Cho HH, Kwon J, Lee HY, Park H. Are radiomics features universally applicable to different organs? Cancer Imaging 2021; 21:31. [PMID: 33827699 PMCID: PMC8028225 DOI: 10.1186/s40644-021-00400-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 03/26/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments. METHODS Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated. RESULTS Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified. CONCLUSION Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties.
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Affiliation(s)
- Seung-Hak Lee
- Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea
- Core Research & Development Center, Korea University Ansan Hospital, Ansan, 15355, South Korea
| | - Hwan-Ho Cho
- Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea
| | - Junmo Kwon
- Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, South Korea.
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea.
- School of Electronic and Electrical Engineering, Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, 16419, South Korea.
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