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226 results on '"Yan, Senxiang"'

5. Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study

Author

Guo, Dazhou, Ge, Jia, Ye, Xianghua, Yan, Senxiang, Xin, Yi, Song, Yuchen, Huang, Bing-shen, Hung, Tsung-Min, Zhu, Zhuotun, Peng, Ling, Ren, Yanping, Liu, Rui, Zhang, Gong, Mao, Mengyuan, Chen, Xiaohua, Lu, Zhongjie, Li, Wenxiang, Chen, Yuzhen, Huang, Lingyun, Xiao, Jing, Harrison, Adam P., Lu, Le, Lin, Chien-Yu, Jin, Dakai, and Ho, Tsung-Ying

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive labor-cost of this task, most institutions choose a substantially simplified protocol by delineating a smaller subset of OARs and neglecting the dose distributions associated with other OARs. In this work we propose a novel, automated and highly effective stratified OAR segmentation (SOARS) system using deep learning to precisely delineate a comprehensive set of 42 H&N OARs. SOARS stratifies 42 OARs into anchor, mid-level, and small & hard subcategories, with specifically derived neural network architectures for each category by neural architecture search (NAS) principles. We built SOARS models using 176 training patients in an internal institution and independently evaluated on 1327 external patients across six different institutions. It consistently outperformed other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative error reduction in other metrics). More importantly, extensive multi-user studies evidently demonstrated that 98% of the SOARS predictions need only very minor or no revisions for direct clinical acceptance (saving 90% radiation oncologists workload), and their segmentation and dosimetric accuracy are within or smaller than the inter-user variation. These findings confirmed the strong clinical applicability of SOARS for the OAR delineation process in H&N cancer radiotherapy workflows, with improved efficiency, comprehensiveness, and quality.

Published
2021

6. Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT

Author

Ye, Xianghua, Guo, Dazhou, Tseng, Chen-kan, Ge, Jia, Hung, Tsung-Min, Pai, Ping-Ching, Ren, Yanping, Zheng, Lu, Zhu, Xinli, Peng, Ling, Chen, Ying, Chen, Xiaohua, Chou, Chen-Yu, Chen, Danni, Yu, Jiaze, Chen, Yuzhen, Jiao, Feiran, Xin, Yi, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Lu, Le, Yan, Senxiang, Jin, Dakai, and Ho, Tsung-Ying

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To validate the clinical applicability of a deep learning (DL) multi-modality esophageal GTV contouring model, developed at 1 institution whereas tested at multiple ones. Methods and Materials: We collected 606 esophageal cancer patients from four institutions. 252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT. A two-streamed DL model for GTV segmentation was developed using pCT and PETCT scans of a 148 patient institution-1 subset. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PETCT combined. For independent evaluation, the rest 104 institution-1 patients behaved as unseen internal testing, and 354 institutions 2-4 patients were used for external testing. We evaluated manual revision degrees by human experts to assess the contour-editing effort. The performance of the deep model was compared against 4 radiation oncologists in a multiuser study with 20 random external patients. Contouring accuracy and time were recorded for the pre-and post-DL assisted delineation process. Results: Our model achieved high segmentation accuracy in internal testing (mean Dice score: 0.81 using pCT and 0.83 using pCT+PET) and generalized well to external evaluation (mean DSC: 0.80). Expert assessment showed that the predicted contours of 88% patients need only minor or no revision. In multi-user evaluation, with the assistance of a deep model, inter-observer variation and required contouring time were reduced by 37.6% and 48.0%, respectively. Conclusions: Deep learning predicted GTV contours were in close agreement with the ground truth and could be adopted clinically with mostly minor or no changes., Comment: 36 pages, 10 figures

Published
2021

7. DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search

Author

Guo, Dazhou, Ye, Xianghua, Ge, Jia, Di, Xing, Lu, Le, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Liu, Zhongjie, Peng, Ling, Yan, Senxiang, and Jin, Dakai

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists and prohibitive laboring costs motivated the automated approach. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. However, without voxel-level supervision, the performance is severely limited. LNS is highly context-dependent - LNS boundaries are constrained by anatomical organs - we formulate it as a deep spatial and contextual parsing problem via encoded anatomical organs. This permits the deep network to better learn from both CT appearance and organ context. We develop a stratified referencing organ segmentation protocol that divides the organs into anchor and non-anchor categories and uses the former's predictions to guide the later segmentation. We further develop an auto-search module to identify the key organs that opt for the optimal LNS parsing performance. Extensive four-fold cross-validation experiments on a dataset of 98 esophageal cancer patients (with the most comprehensive set of 12 LNSs + 22 organs in thoracic region to date) are conducted. Our LNS parsing model produces significant performance improvements, with an average Dice score of 81.1% +/- 6.1%, which is 5.0% and 19.2% higher over the pure CT-based deep model and the previous representative approach, respectively.

Published
2021

11. Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study

Author

Ye, Xianghua, Guo, Dazhou, Ge, Jia, Yan, Senxiang, Xin, Yi, Song, Yuchen, Yan, Yongheng, Huang, Bing-shen, Hung, Tsung-Min, Zhu, Zhuotun, Peng, Ling, Ren, Yanping, Liu, Rui, Zhang, Gong, Mao, Mengyuan, Chen, Xiaohua, Lu, Zhongjie, Li, Wenxiang, Chen, Yuzhen, Huang, Lingyun, Xiao, Jing, Harrison, Adam P., Lu, Le, Lin, Chien-Yu, Jin, Dakai, and Ho, Tsung-Ying

Published
2022
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13. Radiotherapy‐Induced Astrocyte Senescence Promotes an Immunosuppressive Microenvironment in Glioblastoma to Facilitate Tumor Regrowth

Author

Ji, Jianxiong, primary, Ding, Kaikai, additional, Cheng, Bo, additional, Zhang, Xin, additional, Luo, Tao, additional, Huang, Bin, additional, Yu, Hao, additional, Chen, Yike, additional, Xu, Xiaohui, additional, Lin, Haopu, additional, Zhou, Jiayin, additional, Wang, Tingtin, additional, Jin, Mengmeng, additional, Liu, Aixia, additional, Yan, Danfang, additional, Liu, Fuyi, additional, Wang, Chun, additional, Chen, Jingsen, additional, Yan, Feng, additional, Wang, Lin, additional, Zhang, Jianmin, additional, Yan, Senxiang, additional, Wang, Jian, additional, Li, Xingang, additional, and Chen, Gao, additional

Published
2024
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31. A novel hydrogel orthotopic injection model in moderately hypofractionated radiation therapy for prostate cancer: Adaptive degradation and durable imaging

Author

Yu, Hao, primary, Wang, Cheng, additional, Wu, Lingyun, additional, Zhou, Ziyang, additional, Wang, Yiqi, additional, Li, Wenxiang, additional, Yuan, Huili, additional, Lu, Zeyi, additional, Yan, Danfang, additional, Chen, Si, additional, Wang, Xu, additional, and Yan, Senxiang, additional

Published
2023
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34. Suicide Among the First and Second Primary Cancer Patients

Author

Jiang, Yanting, primary, Wang, YiQi, additional, Cheng, XiaoFei, additional, Zhou, ZiYang, additional, Wang, JiLi, additional, Yu, HaoGang, additional, Yao, GuoRong, additional, Lu, ZhongJie, additional, Chen, Xin, additional, Zhao, Feng, additional, and Yan, SenXiang, additional

Published
2023
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42. Nimotuzumab plus concurrent chemo-radiotherapy versus chemo-radiotherapy in unresectable locally advanced esophageal squamous cell carcinoma (ESCC): Interim analysis from a prospective, randomized-controlled, double-blinded, multicenter, and phase III clinical trial (NXCEL1311 Study).

Author

Meng, Xue, primary, Zheng, Anping, additional, Wang, Jun, additional, Wang, Jianhua, additional, Li, Guang, additional, Zhu, Jun, additional, Ma, Hu, additional, Zhu, Xiaodong, additional, Shi, Anhui, additional, Dai, Chunhua, additional, Yan, Senxiang, additional, Wang, Buhai, additional, Qu, Zhongyu, additional, Han, Chun, additional, Sun, Xindong, additional, Ye, Ming, additional, Fan, Ruitai, additional, Huerxidan, Ni yazi, additional, Wang, Xiaohong, additional, and Yu, Jinming, additional

Published
2022
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44. Prognostic Analysis and Comparison of the 2014 and 2018 International Federation of Gynecology and Obstetrics Staging System on Overall Survival in Patients with Stage IIB-IVA Cervix Carcinoma

Author

Song,Tao, Xu,Hong'en, Shi,Lei, Yan,Senxiang, Song,Tao, Xu,Hong'en, Shi,Lei, and Yan,Senxiang

Abstract

Tao Song,1,2 Hong’en Xu,2 Lei Shi,2 Senxiang Yan1 1Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, People’s Republic of China; 2Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, People’s Republic of ChinaCorrespondence: Senxiang Yan, Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79, Qingchun Road, Shangcheng District, Hangzhou, 310003, People’s Republic of China, Tel +86 571-87236403, Email yansenxiang@zju.edu.cnPurpose: This study aimed to assess the prognostic factors of overall survival (OS) in patients with stage IIB–IVA cervix carcinoma (CC) who underwent external beam radiation therapy (EBRT) and brachytherapy (BRT) and to compare the prognostic accuracy of the 2014 and 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system using the Surveillance, Epidemiology, and End Results (SEER) database.Methods: Patients with a histopathological diagnosis of CC between 2004 and 2016 were included. The primary endpoint was OS. The prognostic significance for OS was analyzed by the Cox regression model. Prognostic accuracy in evaluating 3- and 5-year OS in different staging systems was evaluated using time-dependent receiver operating characteristic (tdROC) curves.Results: A total of 2585 patients with stage IIB–IVA CC, staged according to the 2014 FIGO staging system, were included in the study. The 3- and 5-year OS rates were 63.9% and 56.6%, respectively, with a median OS of 98 months. Independent variables, such as older age at diagnosis, histological grades apart from well or moderately differentiated, large tumor size, advanced tumor stages classified according to the 2014 and 2018 FIGO staging systems and treatment without chemotherapy or unknown were associat

Published
2022

45. The Natural History of Parapharyngeal Solitary Fibrous Tumor/Hemangiopericytoma: A Case Report.

Author

Xu, Mengyou, Chen, Meiqin, Hua, Hongzhong, Yin, Jie, and Yan, Senxiang

Subjects
DISEASE progression, MESENCHYME tumors, SYMPTOMS, NECK, SKULL base
Abstract

Solitary fibrous tumor/hemangiopericytoma (SFT/HPC) is extremely rare, and most of them are immediately treated for radical resection. However, the information concerning its natural history remains unclear. In this report, we presented a patient with parapharyngeal SFT/HPC, who was not immediately treated with surgical resection at first diagnosis. After approximately 3 years, the tumor volume doubling time (TVDT) and specific growth rate (SGR) could be measured through 3 serial magnetic resonance imagings. The TVDTs in the early and late pretreatment stages were 350 and 180 days, respectively, while the SGRs were 0.002 and 0.003, respectively. The growth rate of this disease entity is generally slow and may accelerate in the disease process. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Multi-Institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume Using Planning CT and FDG-PET/CT

Author

Ye, Xianghua, Guo, Dazhou, Tseng, Chen-Kan, Ge, Jia, Hung, Tsung-Min, Pai, Ping-Ching, Ren, Yanping, Zheng, Lu, Zhu, Xinli, Peng, Ling, Chen, Ying, Chen, Xiaohua, Chou, Chen-Yu, Chen, Danni, Yu, Jiaze, Chen, Yuzhen, Jiao, Feiran, Xin, Yi, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Lu, Le, Yan, Senxiang, Jin, Dakai, and Ho, Tsung-Ying

Subjects
FOS: Computer and information sciences, Cancer Research, delineation, Computer Vision and Pattern Recognition (cs.CV), segmentation, Computer Science - Computer Vision and Pattern Recognition, deep learning, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Oncology, esophageal cancer, PET/CT (18)F-FDG, radiotherapy, RC254-282, Original Research
Abstract

Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To validate the clinical applicability of a deep learning (DL) multi-modality esophageal GTV contouring model, developed at 1 institution whereas tested at multiple ones. Methods and Materials: We collected 606 esophageal cancer patients from four institutions. 252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT. A two-streamed DL model for GTV segmentation was developed using pCT and PETCT scans of a 148 patient institution-1 subset. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PETCT combined. For independent evaluation, the rest 104 institution-1 patients behaved as unseen internal testing, and 354 institutions 2-4 patients were used for external testing. We evaluated manual revision degrees by human experts to assess the contour-editing effort. The performance of the deep model was compared against 4 radiation oncologists in a multiuser study with 20 random external patients. Contouring accuracy and time were recorded for the pre-and post-DL assisted delineation process. Results: Our model achieved high segmentation accuracy in internal testing (mean Dice score: 0.81 using pCT and 0.83 using pCT+PET) and generalized well to external evaluation (mean DSC: 0.80). Expert assessment showed that the predicted contours of 88% patients need only minor or no revision. In multi-user evaluation, with the assistance of a deep model, inter-observer variation and required contouring time were reduced by 37.6% and 48.0%, respectively. Conclusions: Deep learning predicted GTV contours were in close agreement with the ground truth and could be adopted clinically with mostly minor or no changes., 36 pages, 10 figures

Published
2022
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