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Lee MD, Jain R. Editorial for "MRI-Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy From Epilepsy With Generalized Tonic-Clonic Seizures Alone". J Magn Reson Imaging 2024; 60:289-290. [PMID: 37752725 DOI: 10.1002/jmri.29030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/28/2023] Open
Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York, USA
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Yang Y, Gong J, Yang B, Chen C, Deng X, Chen K, Zhao Y, Cai X, Li J, Zhou J. Post-discharge nutritional management for patients with coronary heart disease and frailty: a qualitative study. BMC Geriatr 2024; 24:268. [PMID: 38504183 PMCID: PMC10949777 DOI: 10.1186/s12877-024-04885-7] [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: 08/14/2023] [Accepted: 03/12/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Frail elderly patients experience physiological function and reserve depletion, leading to imbalances in their internal environment, which increases the risk of coronary heart disease recurrence and malnutrition. However, the majority of these patients, who primarily have a low level of education and lack self-management skills, face difficulties actively dealing with obstacles during the transition period after their discharge from hospitalization. Therefore, it is necessary to understand and discuss in depth the nutrition management experience of discharged elderly patients with coronary heart disease and frailty (ages 65-80 years old) and to analyze the promoting and hindering factors that affect scientific diet behavior during the discharge transition period. METHODS Fifteen elderly patients with coronary heart disease and frailty who had been discharged from the hospital for 6 months were interviewed using a semistructured method. The directed content analysis approach to descriptive research was used to extract topics from the interview content. RESULTS All participants discussed the problems in health nutrition management experience of discharged. Five topics and ten subtopics were extracted, such as ①Weak perceptions and behaviors towards healthy eating (personal habit solidification, negative attitudes towards nutrition management), ②Lack of objective factors for independently adjusting dietary conditions (reliance on subjective feelings, times of appetite change), ③Personal hindrance factors (memory impairment, deficiencies in self-nutrition management), ④Expected external support (assistance care support, ways to obtain nutritional information), ⑤Lack of continuous nutrition management (interruption of professional guidance, avoidance of medical treatment behavior). CONCLUSIONS Nutrition management after discharge places a burden on elderly patients with coronary heart disease and frailty. According to the patients' physical conditions, we should develop a diet support system that is coordinated by individuals, families and society.
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Affiliation(s)
- Yifei Yang
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jing Gong
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Binxu Yang
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Chan Chen
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Xintong Deng
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Kejun Chen
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Yingying Zhao
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Xusihong Cai
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jingjing Li
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jing Zhou
- The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
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Yang M, Meng S, Wu F, Shi F, Xia Y, Feng J, Zhang J, Li C. Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging. Front Med (Lausanne) 2024; 11:1305565. [PMID: 38283620 PMCID: PMC10811129 DOI: 10.3389/fmed.2024.1305565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
Purpose Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). Method This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Result Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.
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Affiliation(s)
- Mingguang Yang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Shan Meng
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Jinan, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Jinrui Zhang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
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Yao L, Cheng N, Chen AQ, Wang X, Gao M, Kong QX, Kong Y. Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy. J Magn Reson Imaging 2023. [PMID: 38014782 DOI: 10.1002/jmri.29157] [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: 09/05/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Lei Yao
- Clinical Medical College, Jining Medical University, Jining, China
| | - Nan Cheng
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - An-Qiang Chen
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xun Wang
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Ming Gao
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Qing-Xia Kong
- Department of Neurology, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yu Kong
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
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Chen J, Wang X, Lv H, Zhang W, Tian Y, Song L, Wang Z. Development and external validation of a clinical-radiomics nomogram for preoperative prediction of LVSI status in patients with endometrial carcinoma. J Cancer Res Clin Oncol 2023; 149:13943-13953. [PMID: 37542548 DOI: 10.1007/s00432-023-05044-y] [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: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE To develop and validate a model that incorporates radiomics based on MRI scans and clinical characteristics to predict lymphovascular invasion (LVSI) in endometrial cancer (EC) patients. METHODS There were 332 patients with EC enrolled retrospectively in this multicenter study. Radiomics score (Radscore) were computed using the valuable radiomics features. The independent predictors of LVSI were identified by univariate logistic analysis. Multivariate logistic regression was used to develop a clinical-radiomics predictive model. Based on the model, a nomogram was developed and validated internally and externally. The nomogram was evaluated with discrimination, calibration, decision curve analysis (DCA), and clinical impact curves (CIC). RESULTS Three predictive models were constructed based on clinicopathological features, radiomic factors and a combination of them, and that the clinic-radiomic model performed best among the three models. Four independent factors comprised the clinical-radiomics model: dynamic contrast enhancement rate of late arterial phase (DCE2), deep myometrium invasion (DMI), lymph node metastasis (LNM), and Radscore. Clinical-radiomics model performance was 0.901 (95% CI 0.84-0.96) in the training cohort, 0.80 (95% CI 0.68-0.92) in the internal validation cohort, and 0.81 (95% CI 0.73-0.9) in the external validation cohort for identifying patients with LVSI, respectively. The model is used to develop a nomogram for clinical use. CONCLUSIONS The MRI-based radiomics nomogram could serve as a noninvasive tool to predict LVSI in EC patients.
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Affiliation(s)
- Jingya Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | | | - Haoyi Lv
- University of Science and Technology of China, Hefei, Anhui, China
| | - Wei Zhang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing, Jiangsu Province, China
| | - Ying Tian
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | - Lina Song
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | - Zhongqiu Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China.
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Yuan N, Lv ZH, Tao TY, Qian D. Influencing Factors and Nomogram for the Development of Epilepsy in Advanced Lung Cancer Patients With Brain Metastases. Biol Res Nurs 2023; 25:606-614. [PMID: 37138370 DOI: 10.1177/10998004231173425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
BACKGROUND Epilepsy is a prevalent comorbidity in patients with brain metastases (BM) and could result in sudden and accidental damage, as well as increased disease burden due to its rapid onset. Foreseeing the potential for the development of epilepsy may permit timely and efficient measures. This study aimed to analyze the influencing factors of epilepsy in advanced lung cancer (ALC) patients with BM and construct a nomogram model to predict the likelihood of developing epilepsy. METHODS Socio-demographic and clinical data of ALC patients with BM were retrospectively collected from the First Affiliated Hospital of Zhejiang University School of Medicine between September 2019 and June 2021. Univariate and multivariate logistic regression analyses were applied to determine the influencing factors for epilepsy in ALC patients with BM. Based on the results of the logistic regression analysis, a nomogram was built to represent the contribution of each influencing factor in predicting the probability of epilepsy development in ALC patients with BM. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were utilized to evaluate the goodness of fit and prediction performance of the model. RESULTS The incidence of epilepsy among 138 ALC patients with BM was 29.7%. On the multivariate analysis, having a higher number of supratentorial lesions (odds ratio [OR] = 1.727; p = 0.022), hemorrhagic foci (OR = 4.922; p = .021), and a high-grade of peritumoral edema (OR = 2.524; p < .001) were independent risk factors for developing epilepsy, while undergoing gamma knife radiosurgery (OR = .327; p = .019) was an independent protective factor. The p-value of the Hosmer-Lemeshow test was .535 and the area under the ROC curve (AUC) was .852 (95% CI: .807-.897), suggesting the model had a good fit and exhibited strong predictive accuracy. CONCLUSION The nomogram was constructed that can predict the probability of epilepsy development for ALC patients with BM, which is helpful for healthcare professionals to identify high-risk groups early and allows for individualized interventions.
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Affiliation(s)
- Niu Yuan
- Department of Nursing, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Respiratory Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhang-Hong Lv
- Department of Nursing, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Respiratory Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ting-Yu Tao
- Department of Nursing, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Respiratory Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Dan Qian
- Department of Nursing, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Respiratory Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Li X, Zhang C, Li T, Lin X, Wu D, Yang G, Cao D. Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies. Sci Rep 2023; 13:15586. [PMID: 37730961 PMCID: PMC10511693 DOI: 10.1038/s41598-023-42916-2] [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: 03/10/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023] Open
Abstract
Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.
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Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Tingting Li
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, 361021, Fujian, China
| | - Xiuqiang Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China.
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Shanghai, 200062, China.
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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Huang L, Yang Z, Zeng Z, Ren H, Jiang M, Hu Y, Xu Y, Zhang H, Ma K, Long L. MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma. Front Neurol 2023; 14:1135978. [PMID: 37006478 PMCID: PMC10060957 DOI: 10.3389/fneur.2023.1135978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/09/2023] [Indexed: 03/18/2023] Open
Abstract
ObjectiveThis study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThis retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics–clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model.ResultsSix texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306–0.9939] and 0.904 (95% CI, 0.8431–0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841–1.0000) and 0.891 (95% CI, 0.7903–0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect.ConclusionThe radiomics–clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.
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Affiliation(s)
- Lixuan Huang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zongxiang Yang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zisan Zeng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hao Ren
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Muliang Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yao Hu
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yifan Xu
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthineers Ltd., Wuhan, China
| | - Kun Ma
- CT Imaging Research Center, GE Healthcare China, Guangzhou, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
- *Correspondence: Liling Long
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Feng L, Yang X, Lu X, Kan Y, Wang C, Sun D, Zhang H, Wang W, Yang J. 18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma. Insights Imaging 2022; 13:144. [PMID: 36057694 PMCID: PMC9440965 DOI: 10.1186/s13244-022-01283-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/07/2022] [Indexed: 11/10/2022] Open
Abstract
Objective To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma. Methods A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). Results Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility. Conclusion The 18F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01283-8.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xia Lu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Ying Kan
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Chao Wang
- Sinounion Medical Technology (Beijing) Co., Ltd., Beijing, 100192, China
| | - Dehui Sun
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
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