Article
Immunology
Haipeng Tong, Jinju Sun, Jingqin Fang, Mi Zhang, Huan Liu, Renxiang Xia, Weicheng Zhou, Kaijun Liu, Xiao Chen
Summary: The study established a machine learning model to predict tumor immune status in non-small cell lung cancer (NSCLC) using F-18-FDG PET/CT radiomics and clinical characteristics. The PET/CT radiomics model outperformed the CT model in predicting CD8 expression, and the combined radiomics-clinical model performed the best.
FRONTIERS IN IMMUNOLOGY
(2022)
Review
Oncology
Xinyu Ge, Jianxiong Gao, Rong Niu, Yunmei Shi, Xiaoliang Shao, Yuetao Wang, Xiaonan Shao
Summary: This article reviews the progress in applying 18F-FDG PET/CT and radiomics in clinical research on lung adenocarcinoma and how these data are analyzed using traditional statistics, machine learning, and deep learning to predict EGFR mutation status. Satisfactory results have been achieved through traditional statistics, machine learning, and deep learning. Future research should combine these methods to achieve more accurate predictions and provide reliable evidence for the precision treatment of lung adenocarcinoma.
FRONTIERS IN ONCOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yunlin Zheng, Di Zhou, Huan Liu, Ming Wen
Summary: The combined model incorporating radiomics and clinical features demonstrated an excellent ability to distinguish between benign and malignant parotid tumors, providing a noninvasive and efficient method for clinical decision making.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yuki Onozato, Takekazu Iwata, Yasufumi Uematsu, Daiki Shimizu, Takayoshi Yamamoto, Yukiko Matsui, Kazuyuki Ogawa, Junpei Kuyama, Yuichi Sakairi, Eiryo Kawakami, Toshihiko Iizasa, Ichiro Yoshino
Summary: This study developed and validated multiple machine learning models using radiomic features from preoperative [F-18]fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) images to predict the pathological invasiveness of lung cancer.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2023)
Article
Medicine, Research & Experimental
Huanhuan Liu, Hua Ren, Zengbin Wu, He Xu, Shuhai Zhang, Jinning Li, Liang Hou, Runmin Chi, Hui Zheng, Yanhong Chen, Shaofeng Duan, Huimin Li, Zongyu Xie, Dengbin Wang
Summary: The study demonstrated the superiority of radiomics model in diagnosing COVID-19 pneumonia, facilitating more rapid and accurate detection.
JOURNAL OF TRANSLATIONAL MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yan Wang, Fei Xiong, Joseph Leach, Evan Kao, Bing Tian, Chengcheng Zhu, Yue Zhang, Michael Hope, David Saloner, Dimitrios Mitsouras
Summary: This study aimed to determine if three-dimensional radiomic features of contrast-enhanced CT images can improve the prediction of rapid abdominal aortic aneurysm (AAA) growth. By measuring the radiomic features of different regions of the AAA, multiple machine learning models were developed to predict rapid growth. The results showed that the radiomic model focused on the intraluminal thrombus (ILT) and wall had the highest predictive accuracy.
EUROPEAN RADIOLOGY
(2023)
Article
Health Care Sciences & Services
Jong Eun Lee, Luu Ngoc Do, Won Gi Jeong, Hyo Jae Lee, Kum Ju Chae, Yun Hyeon Kim, Ilwoo Park
Summary: This study successfully differentiated primary lung cancer from solitary lung metastasis in colorectal cancer patients using a radiomics approach combined with a machine learning algorithm.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Qiang Yu, Youquan Ning, Anran Wang, Shuang Li, Jinming Gu, Quanjiang Li, Xinwei Chen, Fajin Lv, Xiaodi Zhang, Qiang Yue, Juan Peng
Summary: This study developed deep learning-assisted diagnosis models based on CT images to aid radiologists in differentiating benign and malignant parotid tumors. The results showed that the use of these models can improve the diagnostic performance of radiologists and provide more valuable information for clinical decision-making.
EUROPEAN RADIOLOGY
(2023)
Article
Oncology
Giacomo Avesani, Huong Elena Tran, Giulio Cammarata, Francesca Botta, Sara Raimondi, Luca Russo, Salvatore Persiani, Matteo Bonatti, Tiziana Tagliaferri, Miriam Dolciami, Veronica Celli, Luca Boldrini, Jacopo Lenkowicz, Paola Pricolo, Federica Tomao, Stefania Maria Rita Rizzo, Nicoletta Colombo, Lucia Manganaro, Anna Fagotti, Giovanni Scambia, Benedetta Gui, Riccardo Manfredi
Summary: This study attempted to build radiomic models for early relapse and BRCA mutation in ovarian cancer using a multicentric database. However, the results showed that a good predicting model could not be found, highlighting the need for standardization and multicentric validations.
Article
Oncology
Ji-wen Huo, Tian-you Luo, Le Diao, Fa-jin Lv, Wei-dao Chen, Rui-ze Yu, Qi Li
Summary: This study shows that combined models incorporating radiomics signatures, clinical, and CT morphological features can help predict EGFR-mutation subtypes in lung adenocarcinoma, contributing to individualized treatment for patients.
FRONTIERS IN ONCOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Michail E. Klontzas, Dimitrios Leventis, Konstantinos Spanakis, Apostolos H. Karantanas, Elena F. Kranioti
Summary: This study aimed to evaluate the use of post-mortem CT (PMCT) radiomics for the differentiation between early and late post-mortem interval (PMI). The results showed that radiomics analysis on PMCT can differentiate early from late PMI, providing a novel image-based method with significant implications in forensic casework.
EUROPEAN RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yilin Tang, Liang Jin, Wenbin Ji, Zhuangxuan Ma, Dechun Li, Wei Hong, Ming Li
Summary: The study aims to develop a combined model, based on clinical and radiomic features, for classifying the age of rib fractures. The results show that the combined model has higher AUCs than the radiomic model in the training set, internal test set, and external test set, indicating good performance.
INSIGHTS INTO IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yong Han, Yuan Ma, Zhiyuan Wu, Feng Zhang, Deqiang Zheng, Xiangtong Liu, Lixin Tao, Zhigang Liang, Zhi Yang, Xia Li, Jian Huang, Xiuhua Guo
Summary: This study evaluated the capability of PET/CT images for differentiating histologic subtypes of NSCLC and identified the optimal model from radiomics-based machine learning/deep learning algorithms. Linear discriminant analysis and support vector machine coupled with feature selection method achieved optimal performance for the classification of ADC and SCC. The VGG16 deep learning algorithm outperformed conventional machine learning methods in combination with radiomics for the differential diagnosis of NSCLC subtypes.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2021)
Article
Neurosciences
Stefan P. Haider, Adnan I. Qureshi, Abhi Jain, Hishan Tharmaseelan, Elisa R. Berson, Tal Zeevi, David J. Werring, Moritz Gross, Adrian Mak, Ajay Malhotra, Lauren H. Sansing, Guido J. Falcone, Kevin N. Sheth, Seyedmehdi Payabvash
Summary: Radiomic features of admission non-contrast head CT can predict impending hematoma expansion in patients with intracerebral hemorrhage. Combining radiomic with clinical predictors yields the highest predictive value.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ya-Ting Jan, Pei-Shan Tsai, Wen-Hui Huang, Ling-Ying Chou, Shih-Chieh Huang, Jing-Zhe Wang, Pei-Hsuan Lu, Dao-Chen Lin, Chun-Sheng Yen, Ju-Ping Teng, Greta S. P. Mok, Cheng-Ting Shih, Tung-Hsin Wu
Summary: This study developed an AI model based on CT images to distinguish benign from malignant ovarian tumors using radiomics and deep learning features. The model achieved high accuracy, specificity, and sensitivity in the testing set and outperformed junior radiologists. This model is of significant importance in guiding gynecologists to provide better therapeutic strategies.
INSIGHTS INTO IMAGING
(2023)
Article
Clinical Neurology
Ru-Jing Ren, Qiang Huang, Gang Xu, Kai Gu, Eric B. Dammer, Chun-Fang Wang, Xin-Yi Xie, Wen Chen, Zhen-Yi Shao, Sheng-Di Chen, Gang Wang
Summary: The study found that patients with Alzheimer's disease in the Chinese population had a lower risk of developing certain cancers, including lung cancer and prostate and testicular cancer, but a positive association with a higher incident rate of lymphoma was observed.
ALZHEIMERS & DEMENTIA
(2022)
Article
Engineering, Electrical & Electronic
Haishuai Wang, Guangyu Tao, Jiali Ma, Shangru Jia, Lianhua Chi, Hong Yang, Ziping Zhao, Jianhua Tao
Summary: Studying the spread and predicting the epidemic trend of COVID-19 is crucial for global control measures. However, current epidemiological and machine learning models have limitations in accurate prediction. In this study, we propose the T-SIRGAN model, which integrates epidemiological theories and deep learning models to accurately predict the growth trend of COVID-19. Extensive experiments demonstrate the superiority of our method.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2022)
Article
Geriatrics & Gerontology
Qiang Huang, Yong-Fang Zhang, Lin-Jie Li, Eric B. Dammer, Yong-Bo Hu, Xin-Yi Xie, Ran Tang, Jian-Ping Li, Jin-Tao Wang, Xiang-Qian Che, Gang Wang, Ru-Jing Ren
Summary: Neuronal ceroid lipofuscinosis (NCL) is a group of inherited neurodegenerative diseases characterized by the presence of lipofuscin deposits in the lysosomal lumen. In this study, a novel C128Y mutation in the DNAJC5 gene was identified in a young Chinese female with adult-onset NCL, leading to abnormal palmitoylation and lipofuscin deposits.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Oncology
Guangyu Tao, Li Zhu, Qunhui Chen, Lekang Yin, Yamin Li, Jiancheng Yang, Bingbing Ni, Zheng Zhang, Chi Wan Koo, Pradnya D. Patil, Yinan Chen, Hong Yu, Yi Xu, Xiaodan Ye
Summary: This study developed a CT-based visual forecasting system that can accurately predict the imagery of lung nodules in a given future. The system showed promising results in differentiating growth and non-growth nodules and classifying high- and low-risk nodules. Further investigation and validation are needed to fully assess its potential as a prognostication tool.
TRANSLATIONAL LUNG CANCER RESEARCH
(2022)
Article
Oncology
Guangyu Tao, Dejun Shi, Lingming Yu, Chunji Chen, Zheng Zhang, Chang Min Park, Edyta Szurowska, Yinan Chen, Rui Wang, Hong Yu
Summary: This study utilized longitudinal data of lung nodules and applied sequential modeling using long short-term memory (LSTM) networks to accurately predict nodule invasiveness. The results showed that the LSTM-based classifier outperformed the logistic regression (LR) baseline classifier in predicting invasiveness. This research approach warrants further investigation for improving the management of lung nodules.
TRANSLATIONAL LUNG CANCER RESEARCH
(2022)
Article
Clinical Neurology
Jin-Tao Wang, Gang Xu, Ru-Jing Ren, Ying Wang, Ran Tang, Qiang Huang, Jian-Ping Li, Murad Al-Nusaif, Wei-Dong Le, Gang Wang
Summary: This study investigates the impact of health insurance and resources on the burden of Alzheimer's disease and related dementias (ADRD). The findings suggest that ADRD has an increasing burden on patients globally, both physically and economically. Moreover, health resources are negatively associated with certain burden factors, such as disability-adjusted life years (DALY) and death, but positively associated with years of life lived with disability (YLD).
ALZHEIMERS & DEMENTIA
(2023)
Article
Medicine, Research & Experimental
Wei Nie, Guangyu Tao, Zhenghai Lu, Jie Qian, Yaqiong Ge, Shuyuan Wang, Xueyan Zhang, Hua Zhong, Hong Yu
Summary: This study aimed to develop and validate a radiomic signature for predicting overall survival and candidacy for adjuvant chemotherapy in stage I lung adenocarcinoma patients. The results demonstrated that the radiomic signature was associated with overall survival and could predict the benefit of adjuvant chemotherapy, especially in stage IB patients.
JOURNAL OF TRANSLATIONAL MEDICINE
(2022)
Article
Biochemistry & Molecular Biology
Xin-Yi Xie, Qian-Hua Zhao, Qiang Huang, Eric Dammer, Sheng-di Chen, Ru-Jing Ren, Gang Wang, Alzheimer's Dis Neuroimaging Initiat
Summary: Compared with early-onset familial Alzheimer's disease, the heritability of most familial late-onset Alzheimer's disease remains unclear. In this study, targeted sequencing of selected candidate genes was conducted for Chinese Han individuals with familial late-onset Alzheimer's disease, revealing the most frequent variants in CR1, TREM2, and ACE. Additionally, novel pathogenic mutations in ACE were found to be associated with familial late-onset Alzheimer's disease.
Article
Medicine, General & Internal
Xiehe Kong, Zhao Ma, Ran Tang, Xuejun Wang, Kai Wei, Guang Yang, Yanting Yang, Yue Zhao, Dan Zhang, Chen Xie, Gang Wang, Xiaopeng Ma
Summary: This study aims to confirm the efficacy of acupuncture as an adjunctive treatment for mild AD and explore the relationship between clinical efficacy and shifts in gut microbiota.
FRONTIERS IN MEDICINE
(2023)
Article
Neurosciences
Xiang-Qian Che, Guo-Zhen Lin, Xiao-Hong Liu, Gang Wang, Qian-Hua Zhao, Ru-Jing Ren
Summary: This study identified a novel SIGMAR1 gene variant independent of C9orf72 gene status, which is associated with semantic dementia. Further MR imaging showed that this variant is related to pathological progression and is predicted to affect normal splicing.
JOURNAL OF ALZHEIMERS DISEASE
(2023)
Editorial Material
Clinical Neurology
Yue Huang, Qiao-Xin Li, Ling-Xiao Cao, Gang Wang, Daniel Kam Yin Chan, Conceicao Bettencourt, Adrienne E. Milward
FRONTIERS IN NEUROLOGY
(2023)
Article
Psychiatry
Xinyi Xie, Qingxiang Song, Chengxiang Dai, Shishuang Cui, Ran Tang, Suke Li, Jing Chang, Ping Li, Jintao Wang, Jianping Li, Chao Gao, Hongzhuan Chen, Shengdi Chen, Rujing Ren, Xiaoling Gao, Gang Wang
Summary: This study conducted a phase I/II clinical trial with three dosage groups to explore the safety and efficacy of allogenic human adipose MSCs-Exos in patients with mild to moderate AD. The results showed that intranasal administration of ahaMSCs-Exos was safe and well tolerated, and a specific dosage was recommended for further clinical trials.
GENERAL PSYCHIATRY
(2023)
Review
Health Care Sciences & Services
Ling-Xiao Cao, Gang Wang, Qi-Hao Guo, Wei Zhang, Thomas Bak, Yue Huang
Summary: Addenbrooke's cognitive examination (ACE) is an effective cognitive screening tool that shows better performance than other tests in detecting mild cognitive impairment in various neurological disorders. The Chinese version of ACE has been translated into multiple languages and can facilitate early identification and management of cognitive impairment in China.
Review
Genetics & Heredity
Haishuai Wang, Shangru Jia, Zhao Li, Yucong Duan, Guangyu Tao, Ziping Zhao
Summary: This paper analyzes the role of Artificial Intelligence in the prevention and treatment of COVID-19, discusses future development directions, and provides experimental evidence for the effectiveness of the models, which will contribute to controlling the spread of the pandemic.
FRONTIERS IN GENETICS
(2022)
Review
Psychiatry
Rujing Ren, Jinlei Qi, Shaohui Lin, Xinya Liu, Peng Yin, Zhihui Wang, Ran Tang, Jintao Wang, Qiang Huang, Jianping Li, Xinyi Xie, Yongbo Hu, Shishuang Cui, Yuan Zhu, Xiaoping Yu, Pengfei Wang, Yikang Zhu, Yiran Wang, Yanyan Huang, Yisong Hu, Ying Wang, Chunbo Li, Maigeng Zhou, Gang Wang
Summary: China's aging population and the increasing prevalence of neurodegenerative disorders, particularly Alzheimer's disease and related dementias, have led to an urgent need for healthcare resources to support the affected individuals and their families. This report provides an overview of the epidemiological trends, economic burden, clinical diagnosis and treatment status, and available public health resources for these diseases in China. It also highlights the public health impact of Alzheimer's disease and related dementias, including prevalence, mortality, costs, and the overall effect on caregivers and society. Furthermore, it offers technical guidance and support for prevention and treatment, and emphasizes the importance of international exchange and cooperation in addressing this issue.
GENERAL PSYCHIATRY
(2022)