1201
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Jiang Y, Li T, Liang X, Hu Y, Huang L, Liao Z, Zhao L, Han Z, Zhu S, Wang M, Xu Y, Qi X, Liu H, Yang Y, Yu J, Liu W, Cai S, Li G. Association of Adjuvant Chemotherapy With Survival in Patients With Stage II or III Gastric Cancer. JAMA Surg 2017; 152:e171087. [PMID: 28538950 DOI: 10.1001/jamasurg.2017.1087] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Importance The current staging system of gastric cancer is not adequate for defining a prognosis and predicting the patients most likely to benefit from chemotherapy. Objective To construct a survival prediction model based on specific tumor and patient characteristics that enables individualized predictions of the net survival benefit of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. Design, Setting, and Participants In this multicenter retrospective analysis, a survival prediction model was constructed using data from a training cohort of 746 patients with stage II or stage III gastric cancer who satisfied the study's inclusion criteria and underwent surgery between January 1, 2004, and December 31, 2012, at Nanfang Hospital in Guangzhou, China. Patient and tumor characteristics were included as covariates, and their association with overall survival and disease-free survival with and without adjuvant chemotherapy was assessed. The model was internally validated for discrimination and calibration using bootstrap resampling. To externally validate the model, data were included from a validation cohort of 973 patients with stage II or stage III gastric cancer who met the inclusion criteria and underwent surgery at First Affiliated Hospital in Guangzhou, China, and at West China Hospital of Sichuan Hospital in Chendu, China, between January 1, 2000, and June 30, 2009. Data were analyzed from July 10, 2016, to September 1, 2016. Main Outcomes and Measures Concordance index and decision curve analysis for each measure associated with postoperative overall survival and disease-free survival. Results Of the 1719 patients analyzed, 1183 (68.8%) were men and 536 (31.2%) were women and the median (interquartile range) age was 57 (49-66) years. Age, location, differentiation, carcinoembryonic antigen, cancer antigen 19-9, depth of invasion, lymph node metastasis, and adjuvant chemotherapy were significantly associated with overall survival and disease-free survival, with P < .05. The survival prediction model demonstrated good calibration and discrimination, with relatively high bootstrap-corrected concordance indexes in the training and validation cohorts. In the validation cohort, the concordance index for overall survival was 0.693 (95% CI, 0.671-0.715) and for disease-free survival was 0.704 (95% CI, 0.681-0.728). Two nomograms and a calculating tool were built on the basis of specific input variables to estimate an individual's net survival gain attributable to adjuvant chemotherapy. Conclusions and Relevance The survival prediction model can be used to make individualized predictions of the expected survival benefit from the addition of adjuvant chemotherapy for patients with stage II or stage III gastric cancer.
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Affiliation(s)
- Yuming Jiang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China2Guangdong Key Laboratory of Liver Disease Research, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tuanjie Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China2Guangdong Key Laboratory of Liver Disease Research, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoling Liang
- Department of Pharmacy, Nanfang Hospital, Southern Medical University, Guangzhou, China4School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, China
| | - Yanfeng Hu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lei Huang
- German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Zhenchen Liao
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Liying Zhao
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhen Han
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shuguang Zhu
- Guangdong Key Laboratory of Liver Disease Research, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China7Department of Hepatic Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Menglan Wang
- Guangdong Key Laboratory of Liver Disease Research, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China8Department of Infectious Disease, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yangwei Xu
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiaolong Qi
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hao Liu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yang Yang
- Guangdong Key Laboratory of Liver Disease Research, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China7Department of Hepatic Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wei Liu
- Guangdong Key Laboratory of Liver Disease Research, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China7Department of Hepatic Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shirong Cai
- Department of Gastrointestinal Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guoxin Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
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1202
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Xu X, Liu Y, Zhang X, Tian Q, Wu Y, Zhang G, Meng J, Yang Z, Lu H. Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps. Abdom Radiol (NY) 2017; 42:1896-1905. [PMID: 28217825 DOI: 10.1007/s00261-017-1079-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE To determine radiomic features which are capable of reflecting muscular invasiveness of bladder cancer (BC) and propose a non-invasive strategy for the differentiation of muscular invasiveness preoperatively. METHODS Sixty-eight patients with clinicopathologically confirmed BC were included in this retrospective study. A total of 118 cancerous volumes of interest (VOI) were segmented from patients' T2 weighted MR images (T2WI), including 34 non-muscle invasive bladder carcinomas (NMIBCs, stage <T2) and 84 muscle invasive ones (MIBCs, stage ≥T2). The radiomic features quantifying tumor signal intensity and textures were extracted from each VOI and its high-order derivative maps to characterize heterogeneity of tumor tissues. Statistical analysis was used to build radiomic signatures with significant inter-group differences of NMIBCs and MIBCs. The synthetic minority oversampling technique (SMOTE) and a support vector machine (SVM)-based feature selection and classification strategy were proposed to first rebalance the imbalanced sample size and then further select the most predictive and compact signature subset to verify its differentiation capability. RESULTS From each tumor VOI, a total of 63 radiomic features were derived and 30 of them showed significant inter-group differences (P ≤ 0.01). By using the SVM-based feature selection algorithm with rebalanced samples, an optimal subset including 13 radiomic signatures was determined. The area under receiver operating characteristic curve and Youden index were improved to 0.8610 and 0.7192, respectively. CONCLUSION 3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscular invasiveness in patients with bladder cancer.
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Affiliation(s)
- Xiaopan Xu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Yang Liu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Xi Zhang
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Qiang Tian
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China
| | - Yuxia Wu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Guopeng Zhang
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Jiang Meng
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Zengyue Yang
- Department of Urology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
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1203
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Abstract
The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.
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1204
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CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY) 2017; 42:1695-1704. [PMID: 28180924 DOI: 10.1007/s00261-017-1072-0] [Citation(s) in RCA: 151] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
PURPOSE To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC). METHODS A total of 215 HCC patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model. Preoperative clinical factors associated with early recurrence were evaluated. A radiomics signature, a clinical model, and a combined model were built, and the area under the curve (AUC) of operating characteristics (ROC) was used to explore their performance to discriminate early recurrence. RESULTS Twenty-one radiomics features were chosen from 300 candidate features to build a radiomics signature that was significantly associated with early recurrence (P < 0.001), and they presented good performance in the discrimination of early recurrence alone with an AUC of 0.817 (95% CI: 0.758-0.866), sensitivity of 0.794, and specificity of 0.699. The AUCs of the clinical and combined models were 0.781 (95% CI: 0.719-0.834) and 0.836 (95% CI: 0.779-0.883), respectively, with the sensitivity being 0.784 and 0.824, and the specificity being 0.619 and 0.708, respectively. Adding a radiomics signature into conventional clinical variables can significantly improve the accuracy of the preoperative model in predicting early recurrence (P = 0.01). CONCLUSIONS The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.
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1205
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Verma V, Simone CB, Krishnan S, Lin SH, Yang J, Hahn SM. The Rise of Radiomics and Implications for Oncologic Management. J Natl Cancer Inst 2017; 109:3573848. [PMID: 28423406 DOI: 10.1093/jnci/djx055] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 03/07/2017] [Indexed: 12/28/2022] Open
Abstract
Clinical medicine, particularly oncology, is progressing toward personalized care. Whereas the terms genomics, proteomics, transcriptomics, and metabolomics have dominated personalized medicine for the past couple decades, the concept of radiomics was first described in 2012. This nascent concept has major implications for personalized cancer care and involves extracting hundreds of standardized and quantifiable imaging characteristics from diagnostic computed tomography/magnetic resonance imaging images. The central hypothesis of radiomics is that these libraries of quantitative individual voxel-based variables are more sensitively associated with various clinical endpoints compared with the more qualitative radiologic, histopathologic, and clinical data more commonly utilized today. Because radiomics offers immense potential but has not reached a mainstream oncologic audience, the authors discuss herein the role of radiomics in cancer care in the future.
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Affiliation(s)
- Vivek Verma
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Charles B Simone
- Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, MD, USA
| | - Sunil Krishnan
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Steven H Lin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stephen M Hahn
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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1206
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Zhang Q, Xiao Y, Suo J, Shi J, Yu J, Guo Y, Wang Y, Zheng H. Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography. ULTRASOUND IN MEDICINE & BIOLOGY 2017; 43:1058-1069. [PMID: 28233619 DOI: 10.1016/j.ultrasmedbio.2016.12.016] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 12/09/2016] [Accepted: 12/24/2016] [Indexed: 06/06/2023]
Abstract
A radiomics approach to sonoelastography, called "sonoelastomics," is proposed for classification of benign and malignant breast tumors. From sonoelastograms of breast tumors, a high-throughput 364-dimensional feature set was calculated consisting of shape features, intensity statistics, gray-level co-occurrence matrix texture features and contourlet texture features, which quantified the shape, hardness and hardness heterogeneity of a tumor. The high-throughput features were then selected for feature reduction using hierarchical clustering and three-feature selection metrics. For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, an accuracy of 88.0%, a sensitivity of 85.7% and a specificity of 89.3% in a validation set via the leave-one-out cross-validation, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features. The sonoelastomic features are valuable in breast tumor differentiation.
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Affiliation(s)
- Qi Zhang
- Institute of Biomedical Engineering, Shanghai University, Shanghai, China.
| | - Yang Xiao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jingfeng Suo
- Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Jun Shi
- Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Yi Guo
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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1207
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Ma Z, Fang M, Huang Y, He L, Chen X, Liang C, Huang X, Cheng Z, Dong D, Liang C, Xie J, Tian J, Liu Z. CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol 2017. [PMID: 28629560 DOI: 10.1016/j.ejrad.2017.04.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL). MATERIALS AND METHODS 40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test. RESULTS The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422). CONCLUSION Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL.
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Affiliation(s)
- Zelan Ma
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| | - Mengjie Fang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China.
| | - Yanqi Huang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| | - Lan He
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China.
| | - Cuishan Liang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Southern Medical University, Guangzhou, Guangdong, 510515, China.
| | - Xiaomei Huang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Southern Medical University, Guangzhou, Guangdong, 510515, China.
| | - Zixuan Cheng
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
| | - Di Dong
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China.
| | - Changhong Liang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| | - Jiajun Xie
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China.
| | - Jie Tian
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
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1208
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Lee G, Lee HY, Ko ES, Jeong WK. Radiomics and imaging genomics in precision medicine. PRECISION AND FUTURE MEDICINE 2017. [DOI: 10.23838/pfm.2017.00101] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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1209
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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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1210
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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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1211
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Larue RTHM, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 2016; 90:20160665. [PMID: 27936886 PMCID: PMC5685111 DOI: 10.1259/bjr.20160665] [Citation(s) in RCA: 234] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.
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Affiliation(s)
- Ruben T H M Larue
- 1 Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Gilles Defraene
- 2 Department of Oncology, Experimental Radiation Oncology, University of Leuven, Leuven, Belgium
| | - Dirk De Ruysscher
- 1 Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Philippe Lambin
- 1 Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Wouter van Elmpt
- 1 Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
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1212
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van Timmeren JE, Leijenaar RTH, van Elmpt W, Wang J, Zhang Z, Dekker A, Lambin P. Test-Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific? ACTA ACUST UNITED AC 2016; 2:361-365. [PMID: 30042967 PMCID: PMC6037932 DOI: 10.18383/j.tom.2016.00208] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Radiomics is an objective method for extracting quantitative information from medical images. However, in radiomics, standardization, overfitting, and generalization are major challenges to be overcome. Test–retest experiments can be used to select robust radiomic features that have minimal variation. Currently, it is unknown whether they should be identified for each disease (disease specific) or are only imaging device-specific (computed tomography [CT]-specific). Here, we performed a test–retest analysis on CT scans of 40 patients with rectal cancer in a clinical setting. Correlation between radiomic features was assessed using the concordance correlation coefficient (CCC). In total, only 9/542 features have a CCC > 0.85. Furthermore, results were compared with the test–retest results on CT scans of 27 patients with lung cancer with a 15-minute interval. Results show that 446/542 features have a higher CCC for the test–retest analysis of the data set of patients with lung cancer than for patients with rectal cancer. The importance of controlling factors such as scanners, imaging protocol, reconstruction methods, and time points in a radiomics analysis is shown. Moreover, the results imply that test–retest analyses should be performed before each radiomics study. More research is required to independently evaluate the effect of each factor.
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Affiliation(s)
- Janna E van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, China; and.,Department of Oncology, Shanghai Medical College, Fudan University, China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, China; and.,Department of Oncology, Shanghai Medical College, Fudan University, China
| | - André Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
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1213
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Fu J, Wu L, Jiang M, Li D, Jiang T, Hong Z, Wang F, Li S. Clinical Nomogram for Predicting Survival Outcomes in Early Mucinous Breast Cancer. PLoS One 2016; 11:e0164921. [PMID: 27760180 PMCID: PMC5070827 DOI: 10.1371/journal.pone.0164921] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 10/03/2016] [Indexed: 01/08/2023] Open
Abstract
Background The features related to the prognosis of patients with mucinous breast cancer (MBC) remain controversial. We aimed to explore the prognostic factors of MBC and develop a nomogram for predicting survival outcomes. Methods The Surveillance, Epidemiology, and End Results (SEER) database was searched to identify 139611 women with resectable breast cancer from 1990 to 2007. Survival curves were generated using Kaplan-Meier methods. The 5-year and 10-year cancer-specific survival (CSS) rates were calculated using the Life-Table method. Based on Cox models, a nomogram was constructed to predict the probabilities of CSS for an individual patient. The competing risk regression model was used to analyse the specific survival of patients with MBC. Results There were 136569 (97.82%) infiltrative ductal cancer (IDC) patients and 3042 (2.18%) MBC patients. Patients with MBC had less lymph node involvement, a higher frequency of well-differentiated lesions, and more estrogen receptor (ER)-positive tumors. Patients with MBC had significantly higher 5 and10-year CSS rates (98.23 and 96.03%, respectively) than patients with IDC (91.44 and 85.48%, respectively). Univariate and multivariate analyses showed that MBC was an independent factor for better prognosis. As for patients with MBC, the event of death caused by another disease exceeded the event of death caused by breast cancer. A competing risk regression model further showed that lymph node involvement, poorly differentiated grade and advanced T-classification were independent factors of poor prognosis in patients with MBC. The Nomogram can accurately predict CSS with a high C-index (0.816). Risk scores developed from the nomogram can more accurately predict the prognosis of patients with MBC (C-index = 0.789) than the traditional TNM system (C-index = 0.704, P< 0.001). Conclusions Patients with MBC have a better prognosis than patients with IDC. Nomograms could help clinicians make more informed decisions in clinical practice. The competing risk regression model, as a more rational model, is recommended for use in the survival analysis of patients with MBC in the future.
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MESH Headings
- Adenocarcinoma, Mucinous/epidemiology
- Adenocarcinoma, Mucinous/metabolism
- Adenocarcinoma, Mucinous/mortality
- Aged
- Analysis of Variance
- Breast Neoplasms/epidemiology
- Breast Neoplasms/metabolism
- Breast Neoplasms/mortality
- Carcinoma, Ductal, Breast/epidemiology
- Carcinoma, Ductal, Breast/metabolism
- Carcinoma, Ductal, Breast/mortality
- Female
- Humans
- Lymphatic Metastasis
- Middle Aged
- Neoplasm Grading
- Prognosis
- Receptors, Estrogen/metabolism
- Risk Factors
- SEER Program
- Survival Analysis
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Affiliation(s)
- Jianfei Fu
- Department of Oncology, Zhejiang University Jinhua hospital, Jinhua, Zhejiang Province, China
- * E-mail: ,
| | - Lunpo Wu
- Department of Gastroenterology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
- Institute of Gastroenterology, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Mengjie Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of ZheJiang Chinese Medical University, Zhejiang Provincial Hospital of TCM, Hangzhou, Zhejiang Province, China
| | - Dan Li
- Department of Medical Oncology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, Chinese National Ministry of Education; Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China), Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Ting Jiang
- Department of Nuclear Medicine, Zhejiang University Jinhua hospital, Jinhua, Zhejiang Province, China
| | - Zhongwu Hong
- Department of Oncology, Zhejiang University Jinhua hospital, Jinhua, Zhejiang Province, China
| | - Fan Wang
- Department of Oncology, Zhejiang University Jinhua hospital, Jinhua, Zhejiang Province, China
| | - Shuguang Li
- Department of Oncology, Zhejiang University Jinhua hospital, Jinhua, Zhejiang Province, China
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1214
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Chamberlain MC. Neuro-oncology: a selected review of ASCO 2016 abstracts. CNS Oncol 2016; 5:193-8. [PMID: 27616612 PMCID: PMC6040048 DOI: 10.2217/cns-2016-0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 08/18/2016] [Indexed: 11/21/2022] Open
Abstract
ASCO 2016, 29 May-2 June 2016, Chicago, IL, USA The largest annual clinical oncology conference the American Society of Clinical Oncology is held in the USA and gives researchers and other key opinion leaders the opportunity to present new cancer clinical trials and research data. The CNS tumors section of the American Society of Clinical Oncology 2016 covered various aspects of neuro-oncology including metastatic CNS diseases and primary brain tumors, presented via posters, oral talks and over 100 abstracts. This brief review selectively highlights presentations from this meeting in an organizational manner that reflects clinically relevant aspects of a large and multifaceted meeting.
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Affiliation(s)
- Marc C Chamberlain
- Department of Neurology/Division of Neuro-Oncology, University of Washington, Fred Hutchinson Cancer Research Center, Seattle Cancer Care Alliance, 825 Eastlake Avenue E, POB 19023, MS G4940, Seattle, WA 98109-1023, USA
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