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radiomics nomogram

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https://www.readbyqxmd.com/read/30078735/development-and-validation-of-an-mri-based-radiomics-signature-for-the-preoperative-prediction-of-lymph-node-metastasis-in-bladder-cancer
#1
Shaoxu Wu, Junjiong Zheng, Yong Li, Zhuo Wu, Siya Shi, Ming Huang, Hao Yu, Wen Dong, Jian Huang, Tianxin Lin
BACKGROUND: Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature for the individual preoperative prediction of LN metastasis in BCa. METHODS: In total, 103 eligible BCa patients were divided into a training set (n = 69) and a validation set (n = 34)...
August 2, 2018: EBioMedicine
https://www.readbyqxmd.com/read/30035021/radiomics-analysis-allows-for-precise-prediction-of-epilepsy-in-patients-with-low-grade-gliomas
#2
Zhenyu Liu, Yinyan Wang, Xing Liu, Yang Du, Zhenchao Tang, Kai Wang, Jingwei Wei, Di Dong, Yali Zang, Jianping Dai, Tao Jiang, Jie Tian
Purpose: To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy. Methods: This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the interactions between these two kinds of features were analyzed...
2018: NeuroImage: Clinical
https://www.readbyqxmd.com/read/30026116/survival-prediction-in-high-grade-osteosarcoma-using-radiomics-of-diagnostic-computed-tomography
#3
Yan Wu, Lei Xu, Pengfei Yang, Nong Lin, Xin Huang, Weibo Pan, Hengyuan Li, Peng Lin, Binghao Li, Varitsara Bunpetch, Chen Luo, Yangkang Jiang, Disheng Yang, Mi Huang, Tianye Niu, Zhaoming Ye
The poor 5-year survival rate in high-grade osteosarcoma (HOS) has not been increased significantly over the past 30 years. This work aimed to develop a radiomics nomogram for survival prediction at the time of diagnosis in HOS. In this retrospective study, an initial cohort of 102 HOS patients, diagnosed from January 2008 to March 2011, was used as the training cohort. Radiomics features were extracted from the pretreatment diagnostic computed tomography images. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability by using the radiomics signature for each patient...
July 16, 2018: EBioMedicine
https://www.readbyqxmd.com/read/30009172/use-of-a-radiomics-model-to-predict-tumor-invasiveness-of-pulmonary-adenocarcinomas-appearing-as-pulmonary-ground-glass-nodules
#4
Xing Xue, Yong Yang, Qiang Huang, Feng Cui, Yuqing Lian, Siying Zhang, Linpeng Yao, Wei Peng, Xin Li, Peipei Pang, Jianhua Yan, Feng Chen
Background: It is important to distinguish the classification of lung adenocarcinoma. A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT. Materials and Methods: A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs...
2018: BioMed Research International
https://www.readbyqxmd.com/read/29990485/multiparametric-radiomics-improve-prediction-of-lymph-node-metastasis-of-rectal-cancer-compared-with-conventional-radiomics
#5
Li-Da Chen, Jin-Yu Liang, Hui Wu, Zhu Wang, Shu-Rong Li, Wei Li, Xin-Hua Zhang, Jian-Hui Chen, Jin-Ning Ye, Xin Li, Xiao-Yan Xie, Ming-De Lu, Ming Kuang, Jian-Bo Xu, Wei Wang
AIMS: To establish multiparametric radiomics of rectal tumor for the preoperative prediction of lymph node (LN) metastasis. MATERIALS AND METHODS: This prospective study consisted of 115 consecutive patients with rectal carcinoma between April 2015 and April 2017. The multiparametric radiomics scores were extracted from the endorectal ultrasound (ERUS), computed tomography (CT) and shear-wave elastography (SWE) features of the rectal tumor, LN, and peripheral tissues...
July 7, 2018: Life Sciences
https://www.readbyqxmd.com/read/29948074/a-clinical-radiomics-nomogram-for-the-preoperative-prediction-of-lung-metastasis-in-colorectal-cancer-patients-with-indeterminate-pulmonary-nodules
#6
TingDan Hu, ShengPing Wang, Lv Huang, JiaZhou Wang, DeBing Shi, Yuan Li, Tong Tong, Weijun Peng
OBJECTIVES: To develop and validate a clinical-radiomics nomogram for preoperative prediction of lung metastasis for colorectal cancer (CRC) patients with indeterminate pulmonary nodules (IPN). METHODS: 194 CRC patients with lung nodules were enrolled in this study (136 in the training cohort and 58 in the validation cohort). To evaluate the probability of lung metastasis, we developed three models, the clinical model with significant clinical risk factors, the radiomics model with radiomics features constructed by the least absolute shrinkage and selection operator algorithm, and the clinical-radiomics model with significant variables selected by the stepwise logistic regression...
June 12, 2018: European Radiology
https://www.readbyqxmd.com/read/29922924/radiomics-nomogram-outperforms-size-criteria-in-discriminating-lymph-node-metastasis-in-resectable-esophageal-squamous-cell-carcinoma
#7
Xianzheng Tan, Zelan Ma, Lifen Yan, Weitao Ye, Zaiyi Liu, Changhong Liang
OBJECTIVES: To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients. METHODS: Data of 230 consecutive patients were retrospectively analyzed (154 in the training set and 76 in the test set). A total of 1576 radiomics features were extracted from arterial-phase CT images of the whole primary tumor. LASSO logistic regression was performed to choose the key features and construct a radiomics signature...
June 19, 2018: European Radiology
https://www.readbyqxmd.com/read/29914892/radiomics-signature-on-magnetic-resonance-imaging-association-with-disease-free-survival-in-patients-with-invasive-breast-cancer
#8
Hyunjin Park, Yaeji Lim, Eun Sook Ko, Hwan-Ho Cho, Jeong Eon Lee, Boo-Kyung Han, Eun Young Ko, Ji Soo Choi, Ko Woon Park
PURPOSE: To develop a radiomics signature based on preoperative magnetic resonance imaging (MRI) to estimate disease-free survival (DFS) in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings. EXPERIMENTAL DESIGN: We identified 294 patients with invasive breast cancer who underwent preoperative MRI. Patients were randomly divided into training (n = 194) and validation (n = 100) sets...
June 18, 2018: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/29780627/a-new-approach-to-predict-lymph-node-metastasis-in-solid-lung-adenocarcinoma-a-radiomics-nomogram
#9
Xinguan Yang, Xiaohuan Pan, Hui Liu, Dashan Gao, Jianxing He, Wenhua Liang, Yubao Guan
Background: Lymph node metastasis (LNM) of lung cancer is an important factor related to survival and recurrence. The association between radiomics features of lung cancer and LNM remains unclear. We developed and validated a radiomics nomogram to predict LNM in solid lung adenocarcinoma. Methods: A total of 159 eligible patients with solid lung adenocarcinoma were divided into training (n=106) and validation cohorts (n=53). Radiomics features were extracted from venous-phase CT images...
April 2018: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/29770763/a-radiomics-nomogram-for-preoperative-prediction-of-microvascular-invasion-risk-in-hepatitis-b-virus-related-hepatocellular-carcinoma
#10
Jie Peng, Jing Zhang, Qifan Zhang, Yikai Xu, Jie Zhou, Li Liu
PURPOSE: We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODS: A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature...
May 2018: Diagnostic and Interventional Radiology: Official Journal of the Turkish Society of Radiology
https://www.readbyqxmd.com/read/29727831/building-ct-radiomics-based-nomogram-for-preoperative-esophageal-cancer-patients-lymph-node-metastasis-prediction
#11
Chen Shen, Zhenyu Liu, Zhaoqi Wang, Jia Guo, Hongkai Zhang, Yingshu Wang, Jianjun Qin, Hailiang Li, Mengjie Fang, Zhenchao Tang, Yin Li, Jinrong Qu, Jie Tian
PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort...
June 2018: Translational Oncology
https://www.readbyqxmd.com/read/29572634/non-invasive-radiomics-approach-potentially-predicts-non-functioning-pituitary-adenomas-subtypes-before-surgery
#12
Shuaitong Zhang, Guidong Song, Yali Zang, Jian Jia, Chao Wang, Chuzhong Li, Jie Tian, Di Dong, Yazhuo Zhang
PURPOSE: To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach. METHODS: We enrolled 112 patients (training set: n = 75; test set: n = 37) with complete T1-weighted magnetic resonance imaging (MRI) and contrast-enhanced T1-weighted MRI (CE-T1). A total of 1482 quantitative imaging features were extracted from T1 and CE-T1 images. Support vector machine trained a predictive model that was validated using a receiver operating characteristics (ROC) analysis on an independent test set...
March 23, 2018: European Radiology
https://www.readbyqxmd.com/read/29545718/individualized-prediction-of-perineural-invasion-in-colorectal-cancer-development-and-validation-of-a-radiomics-prediction-model
#13
Yanqi Huang, Lan He, Di Dong, Caiyun Yang, Cuishan Liang, Xin Chen, Zelan Ma, Xiaomei Huang, Su Yao, Changhong Liang, Jie Tian, Zaiyi Liu
Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). Methods: After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]...
February 2018: Chinese Journal of Cancer Research, Chung-kuo Yen Cheng Yen Chiu
https://www.readbyqxmd.com/read/29216508/quantitative-biomarkers-for-prediction-of-epidermal-growth-factor-receptor-mutation-in-non-small-cell-lung-cancer
#14
Liwen Zhang, Bojiang Chen, Xia Liu, Jiangdian Song, Mengjie Fang, Chaoen Hu, Di Dong, Weimin Li, Jie Tian
OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression...
February 2018: Translational Oncology
https://www.readbyqxmd.com/read/28874414/a-radiomics-nomogram-for-the-preoperative-prediction-of-lymph-node-metastasis-in-bladder-cancer
#15
Shaoxu Wu, Junjiong Zheng, Yong Li, Hao Yu, Siya Shi, Weibin Xie, Hao Liu, Yangfan Su, Jian Huang, Tianxin Lin
Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in bladder cancer. Experimental Design: A total of 118 eligible bladder cancer patients were divided into a training set ( n = 80) and a validation set ( n = 38). Radiomics features were extracted from arterial-phase CT images of each patient. A radiomics signature was then constructed with the least absolute shrinkage and selection operator algorithm in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model...
November 15, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/28871110/a-deep-learning-based-radiomics-model-for-prediction-of-survival-in-glioblastoma-multiforme
#16
Jiangwei Lao, Yinsheng Chen, Zhi-Cheng Li, Qihua Li, Ji Zhang, Jing Liu, Guangtao Zhai
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images...
September 4, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28280088/radiomics-features-of-multiparametric-mri-as-novel-prognostic-factors-in-advanced-nasopharyngeal-carcinoma
#17
Bin Zhang, Jie Tian, Di Dong, Dongsheng Gu, Yuhao Dong, Lu Zhang, Zhouyang Lian, Jing Liu, Xiaoning Luo, Shufang Pei, Xiaokai Mo, Wenhui Huang, Fusheng Ouyang, Baoliang Guo, Long Liang, Wenbo Chen, Changhong Liang, Shuixing Zhang
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...
August 1, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/27347764/radiomics-signature-a-potential-biomarker-for-the-prediction-of-disease-free-survival-in-early-stage-i-or-ii-non-small-cell-lung-cancer
#18
Yanqi Huang, Zaiyi Liu, Lan He, Xin Chen, Dan Pan, Zelan Ma, Cuishan Liang, Jie Tian, Changhong Liang
Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC...
December 2016: Radiology
https://www.readbyqxmd.com/read/27138577/development-and-validation-of-a-radiomics-nomogram-for-preoperative-prediction-of-lymph-node-metastasis-in-colorectal-cancer
#19
Yan-Qi Huang, Chang-Hong Liang, Lan He, Jie Tian, Cui-Shan Liang, Xin Chen, Ze-Lan Ma, Zai-Yi Liu
PURPOSE: To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). PATIENTS AND METHODS: The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building...
June 20, 2016: Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology
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