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radiomics nomogram

Xiaoping Yi, Xiao Guan, Youming Zhang, Longfei Liu, Xueying Long, Hongling Yin, Zhongjie Wang, Xuejun Li, Weihua Liao, Bihong T Chen, Chishing Zee
Objectives: This study aims to define a radiomic signature for pre-operative differentiation between subclinical pheochromocytoma (sPHEO) and lipid-poor adrenal adenoma (LPA) in adrenal incidentaloma. The goal was to apply a predictive, preventive, and personalized medical approach to the management of adrenal tumors. Patients and methods: This retrospective study consisted of 265 consecutive patients (training cohort, 212 (LPA, 145; sPHEO, 67); validation cohort, 53 (LPA, 36; sPHEO, 17))...
December 2018: EPMA Journal
Chun-Qiang Lu, Yuan-Cheng Wang, Xiang-Pan Meng, Hai-Tong Zhao, Chu-Hui Zeng, Weiwei Xu, Ya-Ting Gao, Shenghong Ju
OBJECTIVES: To identify CT markers for screening of early type 2 diabetes and assessment of the risk of incident diabetes using a radiomics method. METHODS: The medical records of 26,947 inpatients were reviewed. A total of 690 patients were selected and allocated to a primary cohort, a validation cohort, and a prediction cohort and used to build prediction models for diabetes. Three radiomics signatures were constructed using CT image features extracted from three regions of interest, i...
December 6, 2018: European Radiology
Lifeng Yang, Jingbo Yang, Xiaobo Zhou, Liyu Huang, Weiling Zhao, Tao Wang, Jian Zhuang, Jie Tian
OBJECTIVES: The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC). METHODS: One training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient's 2D and 3D CT images...
December 6, 2018: European Radiology
Chen-Lu Liu, Fan Zhang, Qing Cai, Yu-Ying Shen, Shuang-Qing Chen
PURPOSE: To establish a predictive model for surgical resection of invasive pulmonary adenocarcinoma (IPA) presenting as ground-glass nodules (GGNs) based on a radiomics nomogram. METHODS: The CT images of 239 patients with GGNs were collected, of which 160 cases were included in the training set to construct the predictive model and 79 cases were included in the validation set to verify the established predictive model. The least absolute shrinkage and selection operator algorithm was used to select the radiomic features and construct the radiomics tagging...
November 19, 2018: Journal of the American College of Radiology: JACR
Bo-Hao Zheng, Long-Zi Liu, Zhi-Zhi Zhang, Jie-Yi Shi, Liang-Qing Dong, Ling-Yu Tian, Zhen-Bin Ding, Yuan Ji, Sheng-Xiang Rao, Jian Zhou, Jia Fan, Xiao-Ying Wang, Qiang Gao
BACKGROUND: Radiomics is an emerging field in oncological research. In this study, we aimed at developing a radiomics score (rad-score) to estimate postoperative recurrence and survival in patients with solitary hepatocellular carcinoma (HCC). METHODS: A total of 319 solitary HCC patients (training cohort: n = 212; validation cohort: n = 107) were enrolled. Radiomics features were extracted from the artery phase of preoperatively acquired computed tomography (CT) in all patients...
November 21, 2018: BMC Cancer
Xiaoxiao Ma, Liwen Zhang, Dehui Huang, Jinhao Lyu, Mengjie Fang, Jianxing Hu, Yali Zang, Dekang Zhang, Hang Shao, Lin Ma, Jie Tian, Di Dong, Xin Lou
BACKGROUND: Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. PURPOSE: To investigate radiomic signatures as potential imaging biomarkers for distinguishing NMOSD from MS, and to develop and validate a diagnostic radiomic-signature-based nomogram for individualized disease discrimination...
November 8, 2018: Journal of Magnetic Resonance Imaging: JMRI
Wenjie Liang, Pengfei Yang, Rui Huang, Lei Xu, Jiawei Wang, Weihai Liu, Lele Zhang, Dalong Wan, Qiang Huang, Yao Lu, Yu Kuang, Tianye Niu
PURPOSE: To develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate Grade1 and Grade2/3 tumors in patients with pancreatic neuroendocrine tumors (pNETs). EXPERIMENTAL DESIGN: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n=51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction...
November 5, 2018: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
Xing Liu, Yiming Li, Zenghui Qian, Zhiyan Sun, Kaibin Xu, Kai Wang, Shuai Liu, Xing Fan, Shaowu Li, Zhong Zhang, Tao Jiang, Yinyan Wang
OBJECTIVE: The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. METHODS: In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images...
2018: NeuroImage: Clinical
Zenghui Qian, Yiming Li, Zhiyan Sun, Xing Fan, Kaibin Xu, Kai Wang, Shaowu Li, Zhong Zhang, Tao Jiang, Xing Liu, Yinyan Wang
OBJECTIVE: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. METHODS: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature...
October 22, 2018: Aging
Zhicong Li, Hailin Li, Shiyu Wang, Di Dong, Fangfang Yin, An Chen, Siwen Wang, Guangming Zhao, Mengjie Fang, Jie Tian, Sufang Wu, Han Wang
BACKGROUND: Lymph-vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. PURPOSE: To develop and validate an axial T1 contrast-enhanced (CE) MR-based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. STUDY TYPE: Retrospective...
October 26, 2018: Journal of Magnetic Resonance Imaging: JMRI
Ding-Yun Feng, Yu-Qi Zhou, Yan-Fang Xing, Chuang-Feng Li, Qing Lv, Jie Dong, Jie Qin, Yue-Fei Guo, Nan Jiang, Chencui Huang, Hai-Tao Hu, Xing-Hua Guo, Jie Chen, Liang-Hong Yin, Tian-Tuo Zhang, Xing Li
Purpose: The effect of glucocorticoid(s) on connective tissue disease (CTD)-related interstitial lung disease (ILD) is controversial. This multicenter study aimed to identify glucocorticoid-sensitive patients using a radiomics approach. Methods: A total of 416 CTD-ILD patients who began glucocorticoid treatment at the discretion of the attending physician, with or without cyclophosphamide, were included in this study. High doses were defined as pulsed intravenous methylprednisolone, an initial dose of 1 mg/kg/day of prednisolone or 0...
2018: Therapeutics and Clinical Risk Management
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...
October 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
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