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Sun M, Qu H, Xia H, Chen Y, Gao X, Wang Z, Gao R, Qi T. Implications of a Ultrasomics Signature for Predicting Malignancy in Thyroid Nodules with Hashimoto's Thyroiditis. Acad Radiol 2024:S1076-6332(24)00299-X. [PMID: 38796400 DOI: 10.1016/j.acra.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/28/2024]
Abstract
RATIONALE AND OBJECTIVES It remains a challenge to determine the nature of thyroid nodules (TNs) with Hashimoto's thyroiditis (HT). We aim to investigate the multiregional ultrasomics signatures obtained from B-mode ultrasound (B-US) and contrast-enhanced ultrasound (CEUS) images for predicting malignancy in TNs of patients with HT. MATERIALS AND METHODS B-US and CEUS images of 193 nodules (110 malignant and 83 benign nodules) from 110 patients were retrospectively collected in the single-center study, extracting ultrasomics signatures from the intratumoral (In) and peritumoral (Peri) regions of the thyroid. In-B-US, Peri-B-US, In-CEUS, and Peri-CEUS ultrasomics models and a stacking regression model were constructed, and the diagnostic performance of the models was evaluated by comparing the area under the receiver operating characteristic curve (ROC). RESULTS The In-B-US, Peri-B-US, In-CEUS, Peri-CEUS, and stacking regression model in the training and testing datasets which attained AUC (95% CI) of 0.872(0.812, 0.932), 0.815(0.747, 0.882), 0.739(0.659, 0.819), 0.890(0.836, 0.943), 0.997(0.992, 1.000) and 0.799(0.650, 0.948), 0.851(0.727, 0.974), 0.622(0.440, 0.805), 0.742(0.573, 0.911), 0.867(0.741, 0.992); sensitivity of 82.8%, 89.7%, 71.3%, 74.7%, 96.6% and 69.6%, 78.3%, 43.5%, 78.3%, 91.3%; specificity of 80.6%, 58.2%, 67.2%, 91.0%, 98.5% and 93.8%, 87.5%, 93.3%, 75.0%, 81.2%, respectively. The stacking regression model based on ultrasomics signatures showed favorable calibration and discriminative capabilities. Compared to the stacking regression model, the difference in AUC between the In-B-US and Peri-B-US models was not statistically significant (P > 0.05). However, the difference in AUC between the In-CEUS and Peri-CEUS models was significant (P < 0.05). CONCLUSION The application of an ultrasomics approach can effectively predict the benign or malignant nature of TNs accompanied by HT. The diagnostic performance of the ultrasomics model was improved by combining the dual-region and dual-mode of thyroid.
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Affiliation(s)
- Mingzhi Sun
- Graduate school of Dalian Medical University, Da Lian 116000, China; Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225012, China
| | - Hang Qu
- Department of Radiology, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225012, China
| | - Han Xia
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225012, China
| | - Yu Chen
- College of Information Engineering, Yangzhou University, Yangzhou 225127, China
| | - Xiaokang Gao
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225012, China
| | - Zheng Wang
- Department of Pathology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225012, China
| | - Rui Gao
- College of Information Engineering, Yangzhou University, Yangzhou 225127, China
| | - Tingyue Qi
- Department of Ultrasound, Medical Imaging Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225012, China.
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Zhang S, Liu R, Wang Y, Zhang Y, Li M, Wang Y, Wang S, Ma N, Ren J. Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00154-5. [PMID: 38555183 DOI: 10.1016/j.acra.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024]
Abstract
PURPOSE Ultrasound is the imaging modality of choice for preoperative diagnosis of lymph node metastasis (LNM) in thyroid cancer (TC), yet its efficacy remains suboptimal. As radiomics gains traction in tumor diagnosis, its integration with ultrasound for LNM differentiation in TC has emerged, but its diagnostic merit is debated. This study assesses the accuracy of ultrasound-integrated radiomics in preoperatively diagnosing LNM in TC. METHODS Literatures were searched in PubMed, Embase, Cochrane, and Web of Science until July 11, 2023. Quality of the studies was assessed by the radiomics quality score (RQS). A meta-analysis was executed using a bivariate mixed effects model, with a subgroup analysis based on modeling variables (clinical features, radiomics features, or their combination). RESULTS Among 27 articles (16,410 TC patients, 6356 with LNM), the average RQS was 16.5 (SD:5.47). Sensitivity of the models based on clinical features, radiomics features, and radiomics features plus clinical features were 0.64, 0.76 and 0.69. Specificities were 0.77, 0.78 and 0.82. SROC values were 0.76, 0.84 and 0.81. CONCLUSION Ultrasound-based radiomics effectively evaluates LNM in TC preoperatively. Adding clinical features does not notably enhance the model's performance. Some radiomics studies showed high bias, possibly due to the absence of standard application guidelines.
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Affiliation(s)
- Sijie Zhang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China
| | - Ruijuan Liu
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China
| | - Yiyang Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yuewei Zhang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Mengpu Li
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yang Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Siyu Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Na Ma
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Junhong Ren
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China; Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
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Biffoni M, Grani G, Melcarne R, Geronzi V, Consorti F, Ruggieri GD, Galvano A, Razlighi MH, Iannuzzi E, Engel TD, Pace D, Di Gioia CRT, Boniardi M, Durante C, Giacomelli L. Drawing as a Way of Knowing: How a Mapping Model Assists Preoperative Evaluation of Patients with Thyroid Carcinoma. J Clin Med 2024; 13:1389. [PMID: 38592234 PMCID: PMC10931768 DOI: 10.3390/jcm13051389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Effective pre-surgical planning is crucial for achieving successful outcomes in endocrine surgery: it is essential to provide patients with a personalized plan to minimize operative and postoperative risks. Methods: Preoperative lymph node (LN) mapping is a structured high-resolution ultrasonography examination performed in the presence of two endocrinologists and the operating surgeon before intervention to produce a reliable "anatomical guide". Our aim was to propose a preoperative complete model that is non-invasive, avoids overdiagnosis of thyroid microcarcinomas, and reduces medical expenses. Results: The use of 'preoperative echography mapping' has been shown to be successful, particularly in patients with suspected or confirmed neoplastic malignancy. Regarding prognosis, positive outcomes have been observed both post-surgery and in terms of recurrence rates. We collected data on parameters such as biological sex, age, BMI, and results from cytologic tests performed with needle aspiration, and examined whether these parameters predict tumor malignancy or aggressiveness, calculated using a multivariate analysis (MVA). Conclusions: A standard multidisciplinary approach for evaluating neck lymph nodes pre-operation has proven to be an improved diagnostic and preoperative tool.
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Affiliation(s)
- Marco Biffoni
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Giorgio Grani
- Department of Translational and Precision Medicine, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (G.G.); (C.D.)
| | - Rossella Melcarne
- Department of Translational and Precision Medicine, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (G.G.); (C.D.)
| | - Valerio Geronzi
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Fabrizio Consorti
- Department of General Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy;
| | - Giuseppe De Ruggieri
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Alessia Galvano
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Maryam Hosseinpour Razlighi
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Eva Iannuzzi
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Tal Deborah Engel
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
| | - Daniela Pace
- Department of Endocrinology, Valmontone Hospital, 00038 Valmontone, Italy;
| | - Cira Rosaria Tiziana Di Gioia
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy;
| | - Marco Boniardi
- Endocrine Surgery Unit, Niguarda Hospital, 20162 Milan, Italy;
| | - Cosimo Durante
- Department of Translational and Precision Medicine, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (G.G.); (C.D.)
| | - Laura Giacomelli
- Department of General and Specialist Surgery, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy; (M.B.); (V.G.); (G.D.R.); (A.G.); (M.H.R.); (E.I.); (T.D.E.); (L.G.)
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Ren JY, Lv WZ, Wang L, Zhang W, Ma YY, Huang YZ, Peng YX, Lin JJ, Cui XW. Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules. Cancer Imaging 2024; 24:17. [PMID: 38263209 PMCID: PMC10807093 DOI: 10.1186/s40644-024-00661-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/10/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS, TR) 4 and 5 thyroid nodules (TNs) demonstrate much more complicated and overlapping risk characteristics than TR1-3 and have a rather wide range of malignancy possibilities (> 5%), which may cause overdiagnosis or misdiagnosis. This study was designed to establish and validate a dual-modal ultrasound (US) radiomics nomogram integrating B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) imaging to improve differential diagnostic accuracy and reduce unnecessary fine needle aspiration biopsy (FNAB) rates in TR 4-5 TNs. METHODS A retrospective dataset of 312 pathologically confirmed TR4-5 TNs from 269 patients was collected for our study. Data were randomly divided into a training dataset of 219 TNs and a validation dataset of 93 TNs. Radiomics characteristics were derived from the BMUS and CEUS images. After feature reduction, the BMUS and CEUS radiomics scores (Rad-score) were built. A multivariate logistic regression analysis was conducted incorporating both Rad-scores and clinical/US data, and a radiomics nomogram was subsequently developed. The performance of the radiomics nomogram was evaluated using calibration, discrimination, and clinical usefulness, and the unnecessary FNAB rate was also calculated. RESULTS BMUS Rad-score, CEUS Rad-score, age, shape, margin, and enhancement direction were significant independent predictors associated with malignant TR4-5 TNs. The radiomics nomogram involving the six variables exhibited excellent calibration and discrimination in the training and validation cohorts, with an AUC of 0.873 (95% CI, 0.821-0.925) and 0.851 (95% CI, 0.764-0.938), respectively. The marked improvements in the net reclassification index and integrated discriminatory improvement suggested that the BMUS and CEUS Rad-scores could be valuable indicators for distinguishing benign from malignant TR4-5 TNs. Decision curve analysis demonstrated that our developed radiomics nomogram was an instrumental tool for clinical decision-making. Using the radiomics nomogram, the unnecessary FNAB rate decreased from 35.3 to 14.5% in the training cohort and from 41.5 to 17.7% in the validation cohorts compared with ACR TI-RADS. CONCLUSION The dual-modal US radiomics nomogram revealed superior discrimination accuracy and considerably decreased unnecessary FNAB rates in benign and malignant TR4-5 TNs. It could guide further examination or treatment options.
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Affiliation(s)
- Jia-Yu Ren
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, China
| | - Liang Wang
- Center of Computer, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying-Ying Ma
- Department of Medical Ultrasound, The First People's Hospital of Qinzhou, Qinzhou, China
| | - Yong-Zhen Huang
- Department of Medical Ultrasound, The First People's Hospital of Qinzhou, Qinzhou, China
| | - Yue-Xiang Peng
- Department of Medical Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Jian-Jun Lin
- Department of Medical Ultrasound, The First People's Hospital of Qinzhou, Qinzhou, China.
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Fan F, Li F, Wang Y, Dai Z, Lin Y, Liao L, Wang B, Sun H. Integration of ultrasound-based radiomics with clinical features for predicting cervical lymph node metastasis in postoperative patients with differentiated thyroid carcinoma. Endocrine 2023:10.1007/s12020-023-03644-9. [PMID: 38129723 DOI: 10.1007/s12020-023-03644-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE The primary objective was to establish a radiomics model utilizing longitudinal +cross-sectional ultrasound (US) images of lymph nodes (LNs) to predict cervical lymph node metastasis (CLNM) following differentiated thyroid carcinoma (DTC) surgery. METHODS A retrospective collection of 211 LNs from 211 postoperative DTC patients who underwent neck US with suspicious LN fine needle aspiration cytopathology findings at our institution was conducted between June 2021 and April 2023. Conventional US and clinicopathological information of patients were gathered. Based on the pathological results, patients were categorized into CLNM and non-CLNM groups. The database was randomly divided into a training cohort (n = 147) and a test cohort (n = 64) at a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was applied to screen the most relevant radiomic features from the longitudinal + cross-sectional US images, and a radiomics model was constructed. Univariate and multivariate analyses were used to assess US and clinicopathological significance features. Subsequently, a combined model for predicting CLNM was constructed by integrating radiomics, conventional US, and clinicopathological features and presented as a nomogram. RESULTS The area under the curves (AUCs) of the longitudinal + cross-sectional radiomics models were 0.846 and 0.801 in the training and test sets, respectively, outperforming the single longitudinal and cross-sectional models (p < 0.05). In the testing cohort, the AUC of the combined model in predicting CLNM was 0.901, surpassing that of the single US model (AUC, 0.731) and radiomics model (AUC, 0.801). CONCLUSIONS The US-based radiomics model exhibits the potential to accurately predict CLNM following DTC surgery, thereby enhancing diagnostic accuracy.
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Affiliation(s)
- Fengjing Fan
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Fei Li
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Yixuan Wang
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Yuyang Lin
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Lin Liao
- Department of Endocrinology and Metabology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Bei Wang
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China.
| | - Hongjun Sun
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
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Tasci E, Jagasia S, Zhuge Y, Sproull M, Cooley Zgela T, Mackey M, Camphausen K, Krauze AV. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers (Basel) 2023; 15:2672. [PMID: 37345009 DOI: 10.3390/cancers15102672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/03/2023] [Accepted: 05/06/2023] [Indexed: 06/23/2023] Open
Abstract
Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.
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Affiliation(s)
- Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Sarisha Jagasia
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Ying Zhuge
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Theresa Cooley Zgela
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Megan Mackey
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Andra Valentina Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
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