Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics
Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Shanghai Medical College, Fudan University（复旦大学上海医学院）；Fudan University（复旦大学）；Shanghai Engineering Research Center of Molecular Imaging Probes（上海分子影像探针工程技术研究中心）；Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University（复旦大学核物理与离子束应用教育部重点实验室）；the University of Sydney（悉尼大学）；Shanghai Proton and Heavy Ion Center（上海市质子重离子医院）
Bingxin Gu, Mingyuan Meng, Lei Bi, Jinman Kim, David Dagan Feng, Shaoli Song
Objective: Deep Learning-based Radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year Progression-Free Survival (PFS) in Nasopharyngeal Carcinoma (NPC) using pretreatment PET/CT. Methods: A total of 257 patients (170/87 in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. To compare conventional radiomics and DLR, 1456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of 6 feature selection methods and 9 classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature. Results: Our multi-modality DLR model using both PET and CT achieved higher prognostic performance than the optimal conventional radiomics method. Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET or only CT. For risk group stratification, the conventional radiomics signature and DLR signature enabled significant differences between the high- and low-risk patient groups in both internal and external cohorts, while the clinical signature failed in the external cohort. Conclusion: Our study identified potential prognostic tools for survival prediction in advanced NPC, suggesting that DLR could provide complementary values to the current TNM staging.
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation
The First People’s Hospital of Foshan, China（佛山市第一人民医院）；The Second Affiliated Hospital of Anhui Medical University, China（安徽医科大学第二附属医院）；Zhongshan Hospital, Fudan University, Shanghai, China（复旦大学中山医院）；ELTE University, Budapest, Hungary（ELTE大学）；Foundation Robotics Labs, Munich, Germany（Foundation Robotics Labs）
School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息学院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）
Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. Accurate and automatic segmentation of organs at risk (OAR) of computed tomography (CT) images is clinically significant. In recent years, deep learning models represented by U-Net have been widely applied in medical image segmentation tasks, which can help doctors with reduction of workload and get accurate results more quickly. In OAR segmentation of NPC, the sizes of OAR are variable, especially, some of them are small. Traditional deep neural networks underperform during segmentation due to the lack use of global and multi-size i…
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Smart Ageing, The Hong Kong Polytechnic University（香港理工大学智慧老龄研究学院）；Nantong University（南通大学）
The prediction of adaptive radiation therapy (ART) prior to radiation therapy (RT) for nasopharyngeal carcinoma (NPC) patients is important to reduce toxicity and prolong the survival of patients. Currently, due to the complex tumor micro-environment, a single type of high-resolution image can provide only limited information. Meanwhile, the traditional softmax-based loss is insufficient for quantifying the discriminative power of a model. To overcome these challenges, we propose a supervised multi-view contrastive learning method with an additive margin (MMCon). For each patient, four medical…
Shanghai Jiao Tong University（上海交通大学）；Shandong Cancer Hospital Affiliated to Shandong University（山东大学附属山东省肿瘤医院）；Deepwise AI Lab（深睿医疗人工智能实验室）
Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to captu…
Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Cancer Hospital & Institute（四川省肿瘤医院（研究所））；Shanghai AI Lab（上海人工智能实验室）；Canon Medical Systems (China) Co. Ltd.（佳能医疗系统（中国）有限公司）；Stockholm University（斯德哥尔摩大学）；KTH（瑞典皇家理工学院）；Northwestern Polytechnical University（西北工业大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
University of Sydney（悉尼大学）；Fudan University（复旦大学）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）
Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also in…
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The University of Hong Kong（香港大学）；The First Affiliated Hospital, Zhejiang University（浙江大学第一附属医院）；Hupan Lab（华盼实验室）
Harbin Institute of Technology（哈尔滨工业大学）；CVTE Research（CVTE研究院）；Southern University of Science and Technology（南方科技大学）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）
Radiation encephalopathy (REP) is the most common complication for nasopharyngeal carcinoma (NPC) radiotherapy. It is highly desirable to assist clinicians in optimizing the NPC radiotherapy regimen to reduce radiotherapy-induced temporal lobe injury (RTLI) according to the probability of REP onset. To the best of our knowledge, it is the first exploration of predicting radiotherapy-induced REP by jointly exploiting image and non-image data in NPC radiotherapy regimen. We cast REP prediction as a survival analysis task and evaluate the predictive accuracy in terms of the concordance index (CI)…
Nasopharyngeal Carcinoma (NPC) is endemic cancer in the south-east Asia. With the advent of intensity-modulated radiotherapy excellent locoregional control are being achieved. Consequently, this had led to pretreatment clinical staging classification to be less prognostic of outcomes such as recurrence after treatment. Alternative pretreatment strategies for prognosis of NPC after treatment are needed to provide better risk stratification for NPC. In this study we proposed a deep convolution neural network model based on contrast-enhanced T1 (T1C) and T2 weighted (T2) MRI scan to predict 3-yea…
SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电子工程学院）；Department of Radiation Oncology, Sichuan Cancer Center, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（四川省肿瘤医院放射肿瘤科）；Shanghai AI Lab, Shanghai, China（上海人工智能实验室）；The Affiliated Hospital of Qingdao University, Qingdao, China（青岛大学附属医院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China（香港中文大学计算机科学与工程系）；Faculty of Information Technology, Monash University, Melbourne, Australia（墨尔本大学信息科技学院）；Department of Computing and Department of Brain Sciences, Imperial College London, United Kingdom（伦敦帝国理工学院计算机系和脑科学系）；School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China（西北工业大学计算机科学与工程学院）；UMIT Tirol - Private University for Health Sciences and Health Technology, Austria（蒂罗尔大学（私人健康科学与健康技术大学））；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；National Heart and Lung Institute, Imperial College London, London, United Kingdom（伦敦帝国理工学院国家心脏和肺研究所）；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院霍克斯研究所）；Shanghai MediWorks Precision Instruments Co., Ltd., China（上海 MediWorks 精密仪器有限公司）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（上海交通大学自动化与智能感知学院）；Department of Systems Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China（香港科技大学（广州）系统中心）
CmFNet: Cross-modal Fusion Network for Weakly-supervised Segmentation of Medical Images
School of Physics, Peking University（北京大学物理学院）；School of Computer Science, Peking University（北京大学计算机学院）；Department of Radiotherapy, Peking University Cancer Hospital（北京大学肿瘤医院放疗科）；Department of Radiation Oncology, Peking University Third Hospital（北京大学第三医院放疗科）
