Search Results - "Shao, Lizhi"

Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study by Shao, Lizhi, Liu, Zhenyu, Feng, Lili, Lou, Xiaoying, Li, Zhenhui, Zhang, Xiao-Yan, Wan, Xiangbo, Zhou, Xuezhi, Sun, Kai, Zhang, Da-Fu, Wu, Lin, Yang, Guanyu, Sun, Ying-Shi, Xu, Ruihua, Fan, Xinjuan, Tian, Jie

“…Background The aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of…”
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MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer by Zhu, Xuehua, Shao, Lizhi, Liu, Zhenyu, Liu, Zenan, He, Jide, Liu, Jiangang, Ping, Hao, Lu, Jian

“…Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment…”
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A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods by Sun, Kai, Liu, Zhenyu, Chen, Guanmao, Zhou, Zhifeng, Zhong, Shuming, Tang, Zhenchao, Wang, Shuo, Zhou, Guifei, Zhou, Xuezhi, Shao, Lizhi, Ye, Xiaoying, Zhang, Yingli, Jia, Yanbin, Pan, Jiyang, Huang, Li, Liu, Xia, Liu, Jiangang, Tian, Jie, Wang, Ying

“…•Radiomics analysis could discriminate MDD patients from healthy controls.•The improvement of multi-modal model was due to the information complementation.•The…”
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Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study by Li, Bao, Li, Fengling, Liu, Zhenyu, Xu, FangPing, Ye, Guolin, Li, Wei, Zhang, Yimin, Zhu, Teng, Shao, Lizhi, Chen, Chi, Sun, Caixia, Qiu, Bensheng, Bu, Hong, Wang, Kun, Tian, Jie

“…Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment…”
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Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images by Sun, Kai, Liu, Zhenyu, Li, Yiming, Wang, Lei, Tang, Zhenchao, Wang, Shuo, Zhou, Xuezhi, Shao, Lizhi, Sun, Caixia, Liu, Xing, Jiang, Tao, Wang, Yinyan, Tian, Jie

“…Purpose: The present study aimed to evaluate the performance of radiomics features in the preoperative prediction of epileptic seizure following surgery in…”
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The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review by Zhang, Shuaitong, Mu, Wei, Dong, Di, Wei, Jingwei, Fang, Mengjie, Shao, Lizhi, Zhou, Yu, He, Bingxi, Zhang, Song, Liu, Zhenyu, Liu, Jianhua, Tian, Jie

“…Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of…”
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From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time by He, Bingxi, Guo, Yu, Zhu, Yongbei, Tong, Lixia, Kong, Boyu, Wang, Kun, Sun, Caixia, Li, Hailin, Huang, Feng, Wu, Liwei, Wang, Meng, Meng, Fanyang, Dou, Le, Sun, Kai, Tong, Tong, Liu, Zhenyu, Wei, Ziqi, Mu, Wei, Wang, Shuo, Tang, Zhenchao, Zhang, Shuaitong, Wei, Jingwei, Shao, Lizhi, Fang, Mengjie, Li, Juntao, Zhu, Shouping, Zhou, Lili, Wang, Shuo, Dong, Di, Zhang, Huimao, Tian, Jie

“…[Display omitted] •The flow of modern medical services is from signal to image to knowledge.•Information distortion and noise are introduced in the process…”
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MP51-05 RADIOMICS MODEL FOR PREDICTING RENAL SINUS INVASION IN RENAL CELL CARCINOMA by Yan, Ye, Zhu, Haibin, Zhou, Li, Ji, Guoying, Liang, Chao, Shao, Lizhi, Jiang, Xiaoming, Zhang, Fan, Zhang, Shudong

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Federated learning with comparative learning-based dynamic parameter updating on glioma whole slide images by Huang, Longjian, Shao, Lizhi, Bao, Meiling, Guo, Changsong, Shao, Zhuhong, Huang, Xiazi, Wang, Mingjing, Jiang, Xiaoming, Hu, Shengzhou

“…The rapid advancements in artificial intelligence have profoundly impacted various societal domains, particularly in healthcare. In computational pathology,…”
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Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study by Zhang, Junhao, Liu, Ruiqing, Wang, Xujian, Zhang, Shiwei, Shao, Lizhi, Liu, Junheng, Zhao, Jiahui, Wang, Quan, Tian, Jie, Lu, Yun

“…Purpose Neoadjuvant chemoradiotherapy has been the standard practice for patients with locally advanced rectal cancer. However, the treatment response varies…”
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Dual-channel end-to-end network with prior knowledge embedding for improving spatial resolution of magnetic particle imaging by Wen, Jiaxuan, An, Yu, Shao, Lizhi, Yin, Lin, Peng, Zhengyao, Liu, Yanjun, Tian, Jie, Du, Yang

“…Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth…”
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Unsupervised domain adaptation based on feature and edge alignment for femur X-ray image segmentation by Jiang, Xiaoming, Yang, Yongxin, Su, Tong, Xiao, Kai, Lu, LiDan, Wang, Wei, Guo, Changsong, Shao, Lizhi, Wang, Mingjing, Jiang, Dong

“…The gold standard for diagnosing osteoporosis is bone mineral density (BMD) measurement by dual-energy X-ray absorptiometry (DXA). However, various factors…”
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PD57-07 RADIOMIC SIGNATURE FROM BIPARAMETRIC MR IMAGES TO PREDICT BIOCHEMICAL RECURRENCE AFTER RADICAL PROSTATECTOMY by Yan, Ye, Shao, Lizhi, Xia, Haizhui, He, Wei, Zhang, Yuting, Liu, Zhenyu, Tian, Jie, Lu, Jian

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Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study by Feng, Lili, Liu, Zhenyu, Li, Chaofeng, Li, Zhenhui, Lou, Xiaoying, Shao, Lizhi, Wang, Yunlong, Huang, Yan, Chen, Haiyang, Pang, Xiaolin, Liu, Shuai, He, Fang, Zheng, Jian, Meng, Xiaochun, Xie, Peiyi, Yang, Guanyu, Ding, Yi, Wei, Mingbiao, Yun, Jingping, Hung, Mien-Chie, Zhou, Weihua, Wahl, Daniel R, Lan, Ping, Tian, Jie, Wan, Xiangbo

“…Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed…”
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Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning by Sun, Caixia, Luo, Tao, Liu, Zhenyu, Ge, Jia, Shao, Lizhi, Liu, Xiangyu, Li, Bao, Zhang, Song, Qiu, Qi, Wei, Wei, Wang, Shuo, Bian, Xiu-Wu, Tian, Jie

“…Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and…”
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Patient-level grading prediction of prostate cancer from mp-MRI via GMINet by Shao, Lizhi, Liu, Zhenyu, Liu, Jiangang, Yan, Ye, Sun, Kai, Liu, Xiangyu, Lu, Jian, Tian, Jie

“…Magnetic resonance imaging (MRI) is considered the best imaging modality for non-invasive observation of prostate cancer. However, the existing quantitative…”
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Improving initial nodal staging of T3 rectal cancer using quantitative image features by Zhou, Xuezhi, Liu, Zhenyu, Zhang, Dafu, Wu, Lin, Sun, Kai, Shao, Lizhi, Huang, Liyu, Li, Zhenhui, Tian, Jie

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Impact of response patterns for patients with advanced acral melanoma treated with anti-programmed death-1 monotherapy by Zhou, Li, Shao, Lizhi, Gao, Shunyu, Cui, Chuanliang, Chi, Zhihong, Sheng, Xinan, Tang, Bixia, Mao, Lili, Lian, Bin, Yan, Xieqiao, Wang, Xuan, Bai, Xue, Li, Siming, Guo, Jun, Si, Lu

“…Acral melanoma (AM) is less responsive to immunotherapy than nonacral cutaneous melanoma. Variable responses are seen during immunotherapy, including…”
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Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists by Shao, Lizhi, Liu, Zhenyu, Yan, Ye, Liu, Jiangang, Ye, Xiongjun, Xia, Haizhui, Zhu, Xuehua, Zhang, Yuting, Zhang, Zhiying, Chen, Huiying, He, Wei, Liu, Cheng, Lu, Min, Huang, Yi, Sun, Kai, Zhou, Xuezhi, Yang, Guanyu, Lu, Jian, Tian, Jie

“…The grade groups (GGs) of Gleason scores (Gs) is the most critical indicator in the clinical diagnosis and treatment system of prostate cancer. End-to-end…”
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Deep Learning with Quantitative Features of Magnetic Resonance Images to Predict Biochemical Recurrence of Radical Prostatectomy: A Multi-Center Study by Yan, Ye, Shao, Lizhi, Liu, Zhenyu, He, Wei, Yang, Guanyu, Liu, Jiangang, Xia, Haizhui, Zhang, Yuting, Chen, Huiying, Liu, Cheng, Lu, Min, Ma, Lulin, Sun, Kai, Zhou, Xuezhi, Ye, Xiongjun, Wang, Lei, Tian, Jie, Lu, Jian

“…Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether…”
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