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Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
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Mushari NA, Soultanidis G, Duff L, Trivieri MG, Fayad ZA, Robson P, Tsoumpas C. Exploring the Utility of Radiomic Feature Extraction to Improve the Diagnostic Accuracy of Cardiac Sarcoidosis Using FDG PET. Front Med (Lausanne) 2022; 9:840261. [PMID: 35295595 PMCID: PMC8920041 DOI: 10.3389/fmed.2022.840261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/01/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThis study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS).MethodsForty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups. From the PET images alone, two different segmentations were drawn. For segmentation A, a region of interest (ROI) was manually delineated for the patients' myocardium hot regions with standardized uptake value (SUV) higher than 2.5 and the controls' normal myocardium region. A second ROI was drawn in the entire left ventricular myocardium for both study groups, segmentation B. The conventional metrics and radiomic features were then extracted for each ROI. Mann-Whitney U-test and a logistic regression classifier were used to compare the individual features of the study groups.ResultsFor segmentation A, the SUVmin had the highest area under the curve (AUC) and greatest accuracy among the conventional metrics. However, for both segmentations, the AUC and accuracy of the TBRmax were relatively high, >0.85. Twenty-two (from segmentation A) and thirty-five (from segmentation B) of 75 radiomic features fulfilled the criteria: P-value < 0.00061 (after Bonferroni correction), AUC >0.5, and accuracy >0.7. Principal Component Analysis (PCA) was conducted, with five components leading to cumulative variance higher than 90%. Ten machine learning classifiers were then tested and trained. Most of them had AUCs and accuracies ≥0.8. For segmentation A, the AUCs and accuracies of all classifiers are >0.9, but k-neighbors and neural network classifiers were the highest (=1). For segmentation B, there are four classifiers with AUCs and accuracies ≥0.8. However, the gaussian process classifier indicated the highest AUC and accuracy (0.9 and 0.8, respectively).ConclusionsRadiomic analysis of the specific PET data was not proven to be necessary for the detection of CS. However, building an automated procedure will help to accelerate the analysis and potentially lead to more reproducible findings across different scanners and imaging centers and consequently improve standardization procedures that are important for clinical trials and development of more robust diagnostic protocols.
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Affiliation(s)
- Nouf A. Mushari
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- *Correspondence: Nouf A. Mushari
| | - Georgios Soultanidis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Lisa Duff
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Institute of Medical and Biological Engineering, University of Leeds, Leeds, United Kingdom
| | - Maria G. Trivieri
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A. Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Philip Robson
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Charalampos Tsoumpas
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
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Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy. Cancers (Basel) 2022; 14:cancers14020309. [PMID: 35053473 PMCID: PMC8773680 DOI: 10.3390/cancers14020309] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/01/2022] [Accepted: 01/05/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma have been known to respond to first-line tyrosine kinase inhibitor (TKI) treatment. However, a subgroup of patients are non-responsive to the treatment, with poor survival outcomes, and those who are initially responsive may still experience resistance. A reliable prognostic tool may provide a valuable direction for tailoring individual treatment strategies in this clinical setting. With this aim, the present study explores the prognostic power of the combination of the systemic inflammation index (portrayed by hematological markers) and tumor glycolytic heterogeneity (characterized by 18F-fluorodeoxyglucose positron emission tomography images). The model integrating these two biomarkers could be used to improve risk stratification, and the subsequent personalized management strategy in patients with advanced EGFR-mutated lung adenocarcinoma. Abstract Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing 18F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell’s C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.
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Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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Yoon HJ, Cho K, Kim WG, Jeong YJ, Jeong JE, Kang DY. Heterogeneity by global and textural feature analysis in F-18 FP-CIT brain PET images for diagnosis of Parkinson's disease. Medicine (Baltimore) 2021; 100:e26961. [PMID: 34477126 PMCID: PMC8415938 DOI: 10.1097/md.0000000000026961] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 07/30/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The quantification of heterogeneity for the striatum and whole brain with F-18 FP-CIT PET images will be useful for diagnosis. The index obtained from texture analysis on PET images is related to pathological change that the neuronal loss of the nigrostriatal tract is heterogeneous according to the disease state. The aim of this study is to evaluate various heterogeneity indices of F-18 FP-CIT PET images in the diagnosis of Parkinson's disease (PD) patients and to access the diagnostic accuracy of the indices using machine learning (ML). METHODS This retrospective study included F-18 FP-CIT PET images of 31 PD and 31 age-matched health controls (HC). The volume of interest was delineated according to iso-contour lines around standardized uptake value (SUV) 3.0 g/ml for each region of the striatum by PMod 3.603. One hundred eight heterogeneity indices were calculated using CGITA to find indices from which the PD and HC were classified using statistical significance. PD group was classified by constructing a 2-dimensional or 3-dimensional phase space quantifier using these heterogeneity indices. We used 71 heterogeneity indices to classify PD from HC using ML for dimensional reduction. RESULTS The heterogeneity indices for classifying PD from HC were size-zone variability, contrast, inverse difference-moment, and homogeneity in the order of low P value. Three-dimensional quantifiers composed of normalized-contrast, code-similarity, and contrast were more clearly classified than 2-dimensional ones. After 71-dimensional reduction using PCA, classification was possible by logistic regression with 91.3% accuracy. The 2 groups were classified with an accuracy of 85.5% using the support vector machine and 88.4% using the random forest. The classification accuracy using the eXtreme Gradient Boosting was 95.7%, and feature importance was highest in order of SUV bias-corrected kurtosis, size-zone-variability, intensity-variability, and high-intensity-zone-variability. CONCLUSION It was confirmed that PD patients is more clearly classified than the conventional 2-dimensional quantifier by introducing a 3-dimensional phase space quantifier. We observed that ML can be used to classify the 2 groups in an easy and explanatory manner. For the discrimination of the disease, 24 heterogeneity indices were found to be statistically useful, and the major cut-off values of 3 heterogeneity indices were size-zone variability (1906.44), intensity variability (129.21), and high intensity zone emphasis (800.29).
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Affiliation(s)
- Hyun Jin Yoon
- Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
- Institute of Convergence Bio-Health, Dong-A University, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
| | - Kook Cho
- College of General Education, Dong-A University, Busan, Korea
| | - Woong Gon Kim
- Economic Survey, Gyeongin Regional Statistics Office, Gwacheon, Korea
| | - Young-Jin Jeong
- Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
- Institute of Convergence Bio-Health, Dong-A University, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
| | - Ji-Eun Jeong
- Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
- Institute of Convergence Bio-Health, Dong-A University, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
| | - Do-Young Kang
- Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
- Institute of Convergence Bio-Health, Dong-A University, 26 Daesingongwon-ro, Seo-gu, Busan, Korea
- Department of Translational Biomedical Sciences, Dong-A University College of Medicine, Busan, Korea
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Jang J, El‐Rewaidy H, Ngo LH, Mancio J, Csecs I, Rodriguez J, Pierce P, Goddu B, Neisius U, Manning W, Nezafat R. Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac
MR
Imaging. J Magn Reson Imaging 2021; 54:787-794. [DOI: 10.1002/jmri.27581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 02/04/2021] [Accepted: 02/13/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
- Jihye Jang
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Hossam El‐Rewaidy
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Long H. Ngo
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
- Department of Biostatistics Harvard T.H. Chan School of Public Health Boston Massachusetts USA
| | - Jennifer Mancio
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Ibolya Csecs
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Jennifer Rodriguez
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Ulf Neisius
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Warren Manning
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
- Radiology Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division) Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts USA
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020; 11:91. [PMID: 32785796 PMCID: PMC7423816 DOI: 10.1186/s13244-020-00887-2] [Citation(s) in RCA: 561] [Impact Index Per Article: 140.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
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Affiliation(s)
- Janita E van Timmeren
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Davide Cester
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
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Ha S, Choi H, Paeng JC, Cheon GJ. Radiomics in Oncological PET/CT: a Methodological Overview. Nucl Med Mol Imaging 2019; 53:14-29. [PMID: 30828395 DOI: 10.1007/s13139-019-00571-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 11/27/2018] [Accepted: 01/02/2019] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a medical imaging analysis approach based on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of these steps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of small sample size data should be considered. Standardization of methodology and harmonization of image quality are one of the most important challenges with radiomics methodology. Even though there are current issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.
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Affiliation(s)
- Seunggyun Ha
- 1Radiation Medicine Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- 2Department of Nuclear Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Hongyoon Choi
- 2Department of Nuclear Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jin Chul Paeng
- 2Department of Nuclear Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Gi Jeong Cheon
- 1Radiation Medicine Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- 2Department of Nuclear Medicine, Seoul National University Hospital, Seoul, South Korea
- 3Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
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Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 2018. [DOI: 10.1007/s40336-018-0299-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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11
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A systematic review of the prognostic value of texture analysis in 18F-FDG PET in lung cancer. Ann Nucl Med 2018; 32:602-610. [DOI: 10.1007/s12149-018-1281-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]
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Traverso A, Wee L, Dekker A, Gillies R. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int J Radiat Oncol Biol Phys 2018; 102:1143-1158. [PMID: 30170872 PMCID: PMC6690209 DOI: 10.1016/j.ijrobp.2018.05.053] [Citation(s) in RCA: 487] [Impact Index Per Article: 81.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 05/15/2018] [Accepted: 05/20/2018] [Indexed: 12/15/2022]
Abstract
Purpose: An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or “radiomic features.” Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature. Methods and Materials: The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features. Results: Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible. Conclusions: Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features. The repeatability and reproducibility of radiomic features are sensitive at various degrees to image quality and to software used to extract radiomic features. Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. No consensus was found regarding the most repeatable and reproducible features with respect to different settings.
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Affiliation(s)
- Alberto Traverso
- Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.
| | - Leonard Wee
- Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology, MAASTRO Clinic, Maastricht, The Netherlands; School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands
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Luo Y, McShan DL, Matuszak MM, Ray D, Lawrence TS, Jolly S, Kong FM, Ten Haken RK, Naqa IE. A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys 2018; 45:10.1002/mp.13029. [PMID: 29862533 PMCID: PMC6279602 DOI: 10.1002/mp.13029] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 05/28/2018] [Accepted: 05/28/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning. METHODS A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance. RESULTS Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively. CONCLUSIONS MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Daniel L. McShan
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Martha M. Matuszak
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Dipankar Ray
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Theodore S. Lawrence
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Shruti Jolly
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Feng-Ming Kong
- Department of Radiation Oncology, Indiana University, Indianapolis, Indiana, 46202 United States
| | - Randall K. Ten Haken
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Issam El Naqa
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
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14
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Phillips I, Ajaz M, Ezhil V, Prakash V, Alobaidli S, McQuaid SJ, South C, Scuffham J, Nisbet A, Evans P. Clinical applications of textural analysis in non-small cell lung cancer. Br J Radiol 2017; 91:20170267. [PMID: 28869399 DOI: 10.1259/bjr.20170267] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.
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Affiliation(s)
- Iain Phillips
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Mazhar Ajaz
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK.,2 Surrey Clinical Research Centre, University of Surrey, Guildford, UK
| | - Veni Ezhil
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Vineet Prakash
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Sheaka Alobaidli
- 3 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
| | | | | | - James Scuffham
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Andrew Nisbet
- 1 Royal Surrey County Hospital, University of Surrey, Guildford, UK
| | - Philip Evans
- 3 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
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15
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Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A. The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol 2017; 27:4498-4509. [PMID: 28567548 DOI: 10.1007/s00330-017-4859-z] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 04/02/2017] [Accepted: 04/19/2017] [Indexed: 12/11/2022]
Abstract
OBJECTIVES The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. METHODS Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). RESULTS Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. CONCLUSIONS Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. KEY POINTS • PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake. • Radiomic features robustness is an important issue over different image reconstruction settings. • Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent. • Robust radiomic features can be considered as good candidates for tumour quantification.
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Affiliation(s)
- Isaac Shiri
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Junction of Shahid Hemmat and Shahid Chamran Expressways, Tehran, Iran
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, Baltimore, MD, 21287, USA.,Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Pardis Ghaffarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Junction of Shahid Hemmat and Shahid Chamran Expressways, Tehran, Iran.
| | - Ahmad Bitarafan-Rajabi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Junction of Shahid Hemmat and Shahid Chamran Expressways, Tehran, Iran. .,Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Avenue, Niyayesh Blvd, Tehran, Iran.
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16
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Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep 2017; 7:358. [PMID: 28336974 PMCID: PMC5428425 DOI: 10.1038/s41598-017-00426-y] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 02/23/2017] [Indexed: 12/21/2022] Open
Abstract
Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting. The present paper provides a comprehensive review of literature describing the state-of-the-art of FDG-PET/CT texture analysis in NSCLC, suggesting a proposal for harmonization of methodology.
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Affiliation(s)
- M Sollini
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy.
| | - L Cozzi
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
- Radiotherapy and Radiosurgery Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - L Antunovic
- Nuclear Medicine Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - A Chiti
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
- Nuclear Medicine Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - M Kirienko
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
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17
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Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol 2017; 90:20160642. [PMID: 27885836 PMCID: PMC5685100 DOI: 10.1259/bjr.20160642] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 10/27/2016] [Accepted: 11/16/2016] [Indexed: 12/29/2022] Open
Abstract
The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.
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Affiliation(s)
- Elisa Scalco
- Institute of Molecular Bioimaging and Physiology (IBFM), Italian
National Research Council (CNR), Milan, Italy
| | - Giovanna Rizzo
- Institute of Molecular Bioimaging and Physiology (IBFM), Italian
National Research Council (CNR), Milan, Italy
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18
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Huynh E, Coroller TP, Narayan V, Agrawal V, Romano J, Franco I, Parmar C, Hou Y, Mak RH, Aerts HJWL. Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT. PLoS One 2017; 12:e0169172. [PMID: 28046060 PMCID: PMC5207741 DOI: 10.1371/journal.pone.0169172] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2016] [Accepted: 12/13/2016] [Indexed: 02/06/2023] Open
Abstract
Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638-0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643-0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601-0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.
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Affiliation(s)
- Elizabeth Huynh
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
- * E-mail:
| | - Thibaud P. Coroller
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Vivek Narayan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Vishesh Agrawal
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - John Romano
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Idalid Franco
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Ying Hou
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Raymond H. Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
| | - Hugo J. W. L. Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
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19
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Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 2017; 44:151-165. [PMID: 27271051 PMCID: PMC5283691 DOI: 10.1007/s00259-016-3427-0] [Citation(s) in RCA: 320] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/18/2016] [Indexed: 02/07/2023]
Abstract
After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.
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Affiliation(s)
- Mathieu Hatt
- INSERM, UMR 1101, LaTIM, University of Brest IBSAM, Brest, France.
| | - Florent Tixier
- Nuclear Medicine, University Hospital, Poitiers, France
- Medical school, EE DACTIM, University of Poitiers, Poitiers, France
| | - Larry Pierce
- Imaging Research Laboratory, University of Washington, Seattle, WA, USA
| | - Paul E Kinahan
- Imaging Research Laboratory, University of Washington, Seattle, WA, USA
| | - Catherine Cheze Le Rest
- Nuclear Medicine, University Hospital, Poitiers, France
- Medical school, EE DACTIM, University of Poitiers, Poitiers, France
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