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radiomics nomogram

Gu-Wei Ji, Yu-Dong Zhang, Hui Zhang, Fei-Peng Zhu, Ke Wang, Yong-Xiang Xia, Yao-Dong Zhang, Wang-Jie Jiang, Xiang-Cheng Li, Xue-Hao Wang
Purpose To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value for disease-specific and recurrence-free survival. Materials and Methods For this retrospective study, a radiomics model was developed on the basis of a primary cohort of 177 patients with BTC who underwent resection and LN dissection between June 2010 and December 2016. Radiomic features were extracted from portal venous CT scans. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method...
October 16, 2018: Radiology
Qiuyu Wang, Qingneng Li, Rui Mi, Hai Ye, Heye Zhang, Baodong Chen, Ye Li, Guodong Huang, Jun Xia
BACKGROUND: Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients. PURPOSE: To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading. STUDY TYPE: Retrospective. POPULATION: This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas. FIELD STRENGTH/SEQUENCE: 1...
September 8, 2018: Journal of Magnetic Resonance Imaging: JMRI
Jianxing Niu, Shuaitong Zhang, Shunchang Ma, Jinfu Diao, Wenjianlong Zhou, Jie Tian, Yali Zang, Wang Jia
OBJECTIVES: To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging. METHODS: A total of 194 patients with Knosp grade two and three PAs (training set: n = 97; test set: n = 97) were enrolled in this retrospective study. From CE-T1 and T2 MR images, 2553 quantitative imaging features were extracted. To select the most informative features, least absolute shrinkage and selection operator (LASSO) was performed...
September 25, 2018: European Radiology
Wenjie Liang, Lei Xu, Pengfei Yang, Lele Zhang, Dalong Wan, Qiang Huang, Tianye Niu, Feng Chen
Introduction: The emerging field of "radiomics" has considerable potential in disease diagnosis, pathologic grading, prognosis evaluation, and prediction of treatment response. We aimed to develop a novel radiomics nomogram based on radiomics features and clinical characteristics that could preoperatively predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy. Methods: A predictive model was developed from a training cohort comprising 139 ICC patients diagnosed between January 2010 and June 2014...
2018: Frontiers in Oncology
Yuming Jiang, Chuanli Chen, Jingjing Xie, Wei Wang, Xuefan Zha, Wenbing Lv, Hao Chen, Yanfeng Hu, Tuanjie Li, Jiang Yu, Zhiwei Zhou, Yikai Xu, Guoxin Li
To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation...
September 14, 2018: EBioMedicine
Bin Hu, Ke Xu, Zheng Zhang, Ruimei Chai, Shu Li, Lina Zhang
Objective: To develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI). Methods: Eighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study...
August 2018: Chinese Journal of Cancer Research, Chung-kuo Yen Cheng Yen Chiu
Yanfen Cui, Xiaotang Yang, Zhongqiang Shi, Zhao Yang, Xiaosong Du, Zhikai Zhao, Xintao Cheng
OBJECTIVES: To develop and validate a radiomics predictive model based on pre-treatment multiparameter magnetic resonance imaging (MRI) features and clinical features to predict a pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHODS: One hundred and eighty-six consecutive patients with LARC (training dataset, n = 131; validation dataset, n = 55) were enrolled in our retrospective study...
August 20, 2018: European Radiology
Tao Chen, Zhenyuan Ning, Lili Xu, Xingyu Feng, Shuai Han, Holger R Roth, Wei Xiong, Xixi Zhao, Yanfeng Hu, Hao Liu, Jiang Yu, Yu Zhang, Yong Li, Yikai Xu, Kensaku Mori, Guoxin Li
OBJECTIVE: To develop and evaluate a radiomics nomogram for differentiating the malignant risk of gastrointestinal stromal tumours (GISTs). METHODS: A total of 222 patients (primary cohort: n = 130, our centre; external validation cohort: n = 92, two other centres) with pathologically diagnosed GISTs were enrolled. A Relief algorithm was used to select the feature subset with the best distinguishing characteristics and to establish a radiomics model with a support vector machine (SVM) classifier for malignant risk differentiation...
August 16, 2018: European Radiology
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 2018: EBioMedicine
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
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...
August 2018: EBioMedicine
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
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...
September 1, 2018: Life Sciences
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
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
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 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. A radiomics signature (Rad-score) was generated using an elastic net in the training set, and the cutoff point of the radiomics signature to divide the patients into high- and low-risk groups was determined using receiver-operating characteristic curve analysis...
October 1, 2018: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
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
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
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
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...
September 2018: European Radiology
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