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https://www.readbyqxmd.com/read/28331828/radiomics-of-pulmonary-nodules-and-lung-cancer
#1
REVIEW
Ryan Wilson, Anand Devaraj
The large number of indeterminate pulmonary nodules encountered incidentally or during CT-based lung screening provides considerable diagnostic and management challenges. Conventional nodule evaluation relies on visually identifiable discriminators such as size and speculation. These visible nodule features are however small in number and subject to considerable interpretation variability. With the development of novel targeted therapies for lung cancer the diagnosis and characterization of early stage lung tumours has never been more important...
February 2017: Translational Lung Cancer Research
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#2
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325002/radiomic-analysis-of-multi-contrast-brain-mri-for-the-prediction-of-survival-in-patients-with-glioblastoma-multiforme
#3
Ahmad Chaddad, Christian Desrosiers, Matthew Toews
Image texture features are effective at characterizing the microstructure of cancerous tissues. This paper proposes predicting the survival times of glioblastoma multiforme (GBM) patients using texture features extracted in multi-contrast brain MRI images. Texture features are derived locally from contrast enhancement, necrosis and edema regions in T1-weighted post-contrast and fluid-attenuated inversion-recovery (FLAIR) MRIs, based on the gray-level co-occurrence matrix representation. A statistical analysis based on the Kaplan-Meier method and log-rank test is used to identify the texture features related with the overall survival of GBM patients...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28295386/an-integrated-segmentation-and-shape-based-classification-scheme-for-distinguishing-adenocarcinomas-from-granulomas-on-lung-ct
#4
Mehdi Alilou, Niha Beig, Mahdi Orooji, Anant Madabhushi, Prabhakar Rajiah, Michael Yang, Robert Gilkeson, Philip Linden, Vamsidhar Velcheti, Sagar Rakshit, Niyoti Reddy, Frank Jacono
PURPOSE: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans...
March 14, 2017: Medical Physics
https://www.readbyqxmd.com/read/28289945/a-novel-representation-of-inter-site-tumour-heterogeneity-from-pre-treatment-computed-tomography-textures-classifies-ovarian-cancers-by-clinical-outcome
#5
Hebert Alberto Vargas, Harini Veeraraghavan, Maura Micco, Stephanie Nougaret, Yulia Lakhman, Andreas A Meier, Ramon Sosa, Robert A Soslow, Douglas A Levine, Britta Weigelt, Carol Aghajanian, Hedvig Hricak, Joseph Deasy, Alexandra Snyder, Evis Sala
PURPOSE: To evaluate the associations between clinical outcomes and radiomics-derived inter-site spatial heterogeneity metrics across multiple metastatic lesions on CT in patients with high-grade serous ovarian cancer (HGSOC). METHODS: IRB-approved retrospective study of 38 HGSOC patients. All sites of suspected HGSOC involvement on preoperative CT were manually segmented. Gray-level correlation matrix-based textures were computed from each tumour site, and grouped into five clusters using a Gaussian Mixture Model...
March 13, 2017: European Radiology
https://www.readbyqxmd.com/read/28280088/radiomics-features-of-multiparametric-mri-as-novel-prognostic-factors-in-advanced-nasopharyngeal-carcinoma
#6
Shuixing Zhang, Bin Zhang, Jie Tian, Di Dong, Dong Sheng Gu, Yu Hao Dong, Lu Zhang, Zhou Yang Lian, Jing Liu, Xiao Ning Luo, Shu Fang Pei, Xiao Kai Mo, Wen Hui Huang, Fu Sheng Ouyang, Bao Liang Guo, Long Liang, Wenbo Chen, Chang H Liang
PURPOSE: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC). EXPERIMENTAL DESIGN: One-hundred and eighteen patients (training cohort: n = 88; validation cohort: n = 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms...
March 9, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/28268557/prediction-of-malignant-and-benign-of-lung-tumor-using-a-quantitative-radiomic-method
#7
Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268556/association-between-tumor-heterogeneity-and-progression-free-survival-in-non-small-cell-lung-cancer-patients-with-egfr-mutations-undergoing-tyrosine-kinase-inhibitors-therapy
#8
Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian
For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28261818/on-the-impact-of-smoothing-and-noise-on-robustness-of-ct-and-cbct-radiomics-features-for-patients-with-head-and-neck-cancers
#9
Hassan Bagher-Ebadian, Farzan Siddiqui, Liu Chang, Benjamin Movsas, Indrin J Chetty
PURPOSE: We investigated the characteristics of radiomics features extracted from planning CT (pCT) and cone beam CT (CBCT) image datasets acquired for 18 oropharyngeal cancer patients treated with fractionated radiation therapy. Images were subjected to smoothing, sharpening, and noise to evaluate changes in features relative to baseline datasets. METHODS: Textural features were extracted from tumor volumes, contoured on pCT and CBCT images, according to the following 8 different classes: Intensity Based Histogram Features (IBHF), Gray Level Run Length (GLRL), Law's Textural information (LAWS), Discrete Orthonormal Stockwell Transform (DOST), Local Binary Pattern (LBP), Two-Dimensional Wavelet Transform (2DWT), Two Dimensional Gabor Filter (2DGF), and Gray Level Co-Occurrence Matrix (GLCM)...
March 6, 2017: Medical Physics
https://www.readbyqxmd.com/read/28256615/associations-between-tumor-vascularity-vascular-endothelial-growth-factor-expression-and-pet-mri-radiomic-signatures-in-primary-clear-cell-renal-cell-carcinoma-proof-of-concept-study
#10
Qingbo Yin, Sheng-Che Hung, Li Wang, Weili Lin, Julia R Fielding, W Kimryn Rathmell, Amir H Khandani, Michael E Woods, Matthew I Milowsky, Samira A Brooks, Eric M Wallen, Dinggang Shen
Studies have shown that tumor angiogenesis is an essential process for tumor growth, proliferation and metastasis. Also, tumor angiogenesis is an important prognostic factor of clear cell renal cell carcinoma (ccRCC), as well as a factor in guiding treatment with antiangiogenic agents. Here, we attempted to find the associations between tumor angiogenesis and radiomic imaging features from PET/MRI. Specifically, sparse canonical correlation analysis was conducted on 3 feature datasets (i.e., radiomic imaging features, tumor microvascular density (MVD), and vascular endothelial growth factor (VEGF) expression) from 9 patients with primary ccRCC...
March 3, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28231582/radiomics-in-head-and-neck-cancer-extracting-valuable-information-from-data-beyond-recognition
#11
Kathrin Scheckenbach, Lena Colter, Martin Wagenmann
In oncology, biomarkers that describe the characteristics of a malignancy on different levels (clinical, histological, molecular) and the patient's outcome and treatment response are increasingly integrated into the clinical routine. Extensive screening tools, "omics," offer incredible opportunities and vast amounts of data. During the last years, the field of "omics" gained a new promising partner, the "radiomics." Based on radiological imaging, multiple features can be extracted and linked to clinical, genomic, and histopathological data from other sources...
2017: ORL; Journal for Oto-rhino-laryngology and its related Specialties
https://www.readbyqxmd.com/read/28227398/radiomic-analysis-of-multi-contrast-brain-mri-for-the-prediction-of-survival-in-patients-with-glioblastoma-multiforme
#12
Ahmad Chaddad, Christian Desrosiers, Matthew Toews, Ahmad Chaddad, Christian Desrosiers, Matthew Toews, Christian Desrosiers, Ahmad Chaddad, Matthew Toews
Image texture features are effective at characterizing the microstructure of cancerous tissues. This paper proposes predicting the survival times of glioblastoma multiforme (GBM) patients using texture features extracted in multi-contrast brain MRI images. Texture features are derived locally from contrast enhancement, necrosis and edema regions in T1-weighted post-contrast and fluid-attenuated inversion-recovery (FLAIR) MRIs, based on the gray-level co-occurrence matrix representation. A statistical analysis based on the Kaplan-Meier method and log-rank test is used to identify the texture features related with the overall survival of GBM patients...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226734/prediction-of-malignant-and-benign-of-lung-tumor-using-a-quantitative-radiomic-method
#13
Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian, Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian, Min Xu, Jiangdian Song, Di Dong, Jie Tian, Jun Wang, Yali Zang, Xia Liu
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226733/association-between-tumor-heterogeneity-and-progression-free-survival-in-non-small-cell-lung-cancer-patients-with-egfr-mutations-undergoing-tyrosine-kinase-inhibitors-therapy
#14
Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian, Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian, Zaiyi Liu, Yanqi Huang, Yali Zang, Jiangdian Song, Jie Tian, Di Dong
For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28217825/preoperative-prediction-of-muscular-invasiveness-of-bladder-cancer-with-radiomic-features-on-conventional-mri-and-its-high-order-derivative-maps
#15
Xiaopan Xu, Yang Liu, Xi Zhang, Qiang Tian, Yuxia Wu, Guopeng Zhang, Jiang Meng, Zengyue Yang, Hongbing Lu
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)...
February 20, 2017: Abdominal Radiology
https://www.readbyqxmd.com/read/28212290/defining-clinical-response-criteria-and-early-response-criteria-for-precision-oncology-current-state-of-the-art-and-future-perspectives
#16
Vivek Subbiah, Hubert H Chuang, Dhiraj Gambhire, Kalevi Kairemo
In this era of precision oncology, there has been an exponential growth in the armamentarium of genomically targeted therapies and immunotherapies. Evaluating early responses to precision therapy is essential for "go" versus "no go" decisions for these molecularly targeted drugs and agents that arm the immune system. Many different response assessment criteria exist for use in solid tumors and lymphomas. We reviewed the literature using the Medline/PubMed database for keywords "response assessment" and various known response assessment criteria published up to 2016...
February 15, 2017: Diagnostics
https://www.readbyqxmd.com/read/28211505/corrigendum-defining-a-radiomic-response-phenotype-a-pilot-study-using-targeted-therapy-in-nsclc
#17
Hugo J W L Aerts, Patrick Grossmann, Yongqiang Tan, Geoffrey R Oxnard, Naiyer Rizvi, Lawrence H Schwartz, Binsheng Zhao
No abstract text is available yet for this article.
February 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28205306/multi-site-quality-and-variability-analysis-of-3d-fdg-pet-segmentations-based-on-phantom-and-clinical-image-data
#18
MULTICENTER STUDY
Reinhard R Beichel, Brian J Smith, Christian Bauer, Ethan J Ulrich, Payam Ahmadvand, Mikalai M Budzevich, Robert J Gillies, Dmitry Goldgof, Milan Grkovski, Ghassan Hamarneh, Qiao Huang, Paul E Kinahan, Charles M Laymon, James M Mountz, John P Muzi, Mark Muzi, Sadek Nehmeh, Matthew J Oborski, Yongqiang Tan, Binsheng Zhao, John J Sunderland, John M Buatti
PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics...
February 2017: Medical Physics
https://www.readbyqxmd.com/read/28199039/radiomics-assessment-of-bladder-cancer-grade-using-texture-features-from-diffusion-weighted-imaging
#19
Xi Zhang, Xiaopan Xu, Qiang Tian, Baojuan Li, Yuxia Wu, Zengyue Yang, Zhengrong Liang, Yang Liu, Guangbin Cui, Hongbing Lu
PURPOSE: To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features. MATERIALS AND METHODS: In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3...
February 15, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28180924/ct-based-radiomics-signature-a-potential-biomarker-for-preoperative-prediction-of-early-recurrence-in-hepatocellular-carcinoma
#20
Ying Zhou, Lan He, Yanqi Huang, Shuting Chen, Penqi Wu, Weitao Ye, Zaiyi Liu, Changhong Liang
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...
February 8, 2017: Abdominal Radiology
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