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Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges. Int J Radiat Oncol Biol Phys 2018; 102:1117-1142. [PMID: 30064704 DOI: 10.1016/j.ijrobp.2018.05.022] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/27/2018] [Accepted: 05/02/2018] [Indexed: 02/06/2023]
Abstract
Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving 18F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.
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Parkinson C, Foley K, Whybra P, Hills R, Roberts A, Marshall C, Staffurth J, Spezi E. Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods. EJNMMI Res 2018; 8:29. [PMID: 29644499 PMCID: PMC5895559 DOI: 10.1186/s13550-018-0379-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Accepted: 03/23/2018] [Indexed: 12/25/2022] Open
Abstract
Background Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Results Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Conclusion Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used. Electronic supplementary material The online version of this article (10.1186/s13550-018-0379-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Craig Parkinson
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK
| | - Kieran Foley
- Division of Cancer and Genetics, School of Medicine, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK.
| | - Philip Whybra
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK
| | - Robert Hills
- Clinical Trials Unit, Cardiff University, Cardiff, CF10 3AT, UK
| | - Ashley Roberts
- Clinical Radiology, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW, UK
| | - Chris Marshall
- Wales Research and Diagnostic PET Imaging Centre, Cardiff University, School of Medicine, Ground Floor, C Block, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK
| | - John Staffurth
- Division of Cancer and Genetics, School of Medicine, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK.,Velindre Cancer Centre, Velindre Rd, Cardiff, CF14 2TL, UK
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK.,Velindre Cancer Centre, Velindre Rd, Cardiff, CF14 2TL, UK
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Attiyeh MA, Chakraborty J, Doussot A, Langdon-Embry L, Mainarich S, Gönen M, Balachandran VP, D'Angelica MI, DeMatteo RP, Jarnagin WR, Kingham TP, Allen PJ, Simpson AL, Do RK. Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis. Ann Surg Oncol 2018; 25:1034-1042. [PMID: 29380093 DOI: 10.1245/s10434-017-6323-3] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Indexed: 12/15/2022]
Abstract
BACKGROUND Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. METHODS A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. RESULTS A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. CONCLUSION We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.
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Affiliation(s)
- Marc A Attiyeh
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jayasree Chakraborty
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandre Doussot
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Liana Langdon-Embry
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shiana Mainarich
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Vinod P Balachandran
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael I D'Angelica
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronald P DeMatteo
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William R Jarnagin
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - T Peter Kingham
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter J Allen
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Richard K Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Moore TA, Li A, Young EWK. Integrating Population Heterogeneity Indices with Microfluidic Cell-Based Assays. SLAS DISCOVERY 2017; 23:459-473. [PMID: 29048950 DOI: 10.1177/2472555217738533] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Recent advances in cell-based assays have involved the integration of single-cell analyses and microfluidics technology to facilitate both high-content and high-throughput applications. These technical advances have yielded large datasets with single-cell resolution, and have given rise to the study of cell population dynamics, but statistical analyses of these populations and their properties have received much less attention, particularly for cells cultured in microfluidic systems. The objective of this study was to perform statistical analyses using Pittsburgh Heterogeneity Indices (PHIs) to understand the heterogeneity and evolution of cell population demographics on datasets generated from a microfluidic single-cell-resolution cell-based assay. We applied PHIs to cell population data obtained from studies involving drug response and soluble factor signaling of multiple myeloma cancer cells, and investigated effects of reducing population size in the microfluidic assay on both the PHIs and traditional population-averaged readouts. Results showed that PHIs are useful for examining changing population distributions within a microfluidic setting. Furthermore, PHIs provided data in support of finding the minimum population size for a microfluidic assay without altering the heterogeneity indices of the cell population. This work will be useful for novel assay development, and for advancing the integration of microfluidics, cell-based assays, and heterogeneity analyses.
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Affiliation(s)
- Thomas A Moore
- 1 Department of Mechanical & Industrial Engineering, Institute of Biomaterials & Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Alexander Li
- 2 Division of Engineering Science, University of Toronto, Toronto, ON, Canada
| | - Edmond W K Young
- 1 Department of Mechanical & Industrial Engineering, Institute of Biomaterials & Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Tian J, Zhang S. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 2017; 403:21-27. [PMID: 28610955 DOI: 10.1016/j.canlet.2017.06.004] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 05/31/2017] [Accepted: 06/03/2017] [Indexed: 02/08/2023]
Abstract
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice.
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Affiliation(s)
- Bin Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China
| | - Xin He
- Department of Mathematics, City University of Hong Kong, PR China
| | - Fusheng Ouyang
- Department of Radiology, The First People's Hospital of Shunde, Foshan, Guangdong, PR China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Chinese Academy of Science, Beijing, PR China
| | - Yuhao Dong
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; Shantou University Medical College, Guangdong, PR China
| | - Lu Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China
| | - Xiaokai Mo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; Shantou University Medical College, Guangdong, PR China
| | - Wenhui Huang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, PR China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Chinese Academy of Science, Beijing, PR China.
| | - Shuixing Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China.
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Zhang B, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Zhang S. Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget 2017; 8:72457-72465. [PMID: 29069802 PMCID: PMC5641145 DOI: 10.18632/oncotarget.19799] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 06/28/2017] [Indexed: 01/03/2023] Open
Abstract
We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression. The selected features were used to construct radiomics-based models and the predictive performance of which were assessed with respect to the area under the curve (AUC). As a result, eight features significantly associated with the progression of advanced NPC were identified. In the training cohort, a radiomic model based on combined CET1-w and T2-w images (AUC: 0.886, 95%CI: 0.815-0.956) demonstrated better prognostic performance than models based on CET1-w (AUC: 0.793, 95%CI: 0.698-0.889) or T2-w images alone (AUC: 0.813, 95%CI: 0.721-0.904). These results were confirmed in the validation cohort. Accordingly, MRI-based radiomic biomarkers present high accuracy in the pre-treatment prediction of progression in advanced NPC.
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Affiliation(s)
- Bin Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.,Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China
| | - Fusheng Ouyang
- Department of Radiology, The First People's Hospital of Shunde, Foshan, P.R. China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, P.R. China
| | - Yuhao Dong
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Lu Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Xiaokai Mo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Wenhui Huang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Shuixing Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.,Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China
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