📝 Publications
📓 Journal Paper
Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option
Kai Wang, Michael Dohopolski, Qiongwen Zhang, David Sher, Jing Wang, Medical physics, 2022.
Project |
- Delta-radiomcis features calculated based on Pre- and Post-Therapy PET/CT.
- Multi-modality model for early-stage locoregional recurrence prediction.
- We performed patient-specific prediction uncertainty estimation for learning with rejection option to improve the model reliability (higher performance on high-confidence group).

Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model
Kai Wang, Nicholas George-Jones, Liyuan Chen, Jacob Hunter, Jing Wang, The Laryngoscope, 2022.
Project |
- We presented a deep multi-task (DMT) model to predict vestibular schwannoma enlargement and tumor segmentation mask simultaneously using the initial diagnostic ceT1 MRI.
- The proposed DMT model is of higher learning efficiency and prediction accuracy than single-task prediction model.
A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer
Kai Wang, Zhiguo Zhou, Rongfang Wang, Liyuan Chen, Qiongwen Zhang, David Sher, Jing Wang, Medical physics 47.10 (2020): 5392-5400.
- We built a multi-classifier, multi-objective, and multi-modality (mCOM) model for HNSCC LRR prediction.
- Our study shows fusing multiple classifiers and multiple modalities can improve the robustness of the predictive model. Additionally, the multi-objective model could help to build a predictive model that can be flexibly adapted to different clinical preferences.
- Predicting local persistence/recurrence after radiation therapy for head and neck cancer from PET/CT using a multi-objective, multi-classifier radiomics model, Qiongwen Zhang, Kai Wang, Zhiguo Wang, et al., Frontiers in oncology 12 (2022). (The first two authors contributed to this work equally)
- Preliminary Evaluation of PTV Margins for Online Adaptive Radiotherapy of the Prostatic Fossa, Howard Morgan, Kai Wang, Yulong Yan, et al., Practical Radiation Oncology, 2022.
- Use of deep learning to predict the need for aggressive nutritional supplementation during head and neck radiotherapy, Michael Dohopolski, Kai Wang, Howard Morgan, David Sher, Jing Wang, Radiotherapy and Oncology, 2022, 171, 129-138.
- Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: The additive benefit of CT and intra-treatment cone-beam computed tomography features, Howard E Morgan, Kai Wang, Michael Dohopolski, Xiao Liang, Michael R Folkert, David J Sher, Jing Wang, Quantitative Imaging in Medicine and Surgery, 2021, 11(12), 4781-4796.
- Prediction of vestibular schwannoma enlargement after radiosurgery using tumor shape and MRI texture features, Nicholas A George-Jones, Kai Wang, Jing Wang, Jacob B Hunter, Otology & Neurotology, 2021, 42(3), e348-e354.
- Automated Detection of Vestibular Schwannoma Growth Using a Two‐Dimensional U‐Net Convolutional Neural Network, Nicholas A George-Jones, Kai Wang, Jing Wang, Jacob B Hunter, The Laryngoscope, 2021, 131(2), E619-E624.
- Multifaceted radiomics for distant metastasis prediction in head & neck cancer, Zhiguo Zhou, Kai Wang, Michael Folkert, Hui Liu, Steve Jiang, David Sher, Jing Wang, Physics in Medicine & Biology, 2020, 65(15), 155009.
- A Novel Stationary CT Scheme Based on High-Density X-Ray Sources Device, Yiting Duan, Haitao Cheng, Kai Wang, Xuanqin Mou, IEEE Access, 2020, 8, 112910-112921.
- Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion, Rongfang Wang, Jinkun Guo, Zhiguo Zhou, Kai Wang, Shuiping Gou, Rongbin Xu, David Sher, Jing Wang, Physics in Medicine & Biology, 2022, 67(12), 125004.
- Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer, Liyuan Chen, Michael Dohopolski, Zhiguo Zhou, Kai Wang, Rongfang Wang, David Sher, Jing Wang, International Journal of Radiation Oncology* Biology* Physics, 2021, 110(4), pp.1171-1179.
- Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet), Hua-Chieh Shao, Tian Li, Michael J Dohopolski, Jing Wang, Jing Cai, Jun Tan, Kai Wang, You Zhang, Physics in Medicine & Biology, 2022, 67(13), p.135012.
📑 Conference Proceeding
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers
Kai Wang, Yunxiang Li, Michael Dohopolski, Tao Peng, Weiguo Lu, You Zhang, Jing Wang (The first two authors contributed to this work equally, Oral 🎤)
- Ranked No.3 in the recurrece-free survival (RFS) prediction task of the head and neck tumor segmentation and outcome prediction challenge (HECKTOR 2022).
- nnUNet for GTVp and GTVn segmentation on a multi-institution dataset.
- Extract radiomics feature from the AI-based auto-segmentated ROI for radiomics-based HNC RFS prediction.
- Ensemble multi-modality model for more robust prediction.
- Gas Bubble Motion Artifact Reduction through Simultaneous Motion Estimation and Image Reconstruction, Kai Wang, Hua-Chieh Shao, You Zhang, Chunjoo Park, Steve Jiang, Jing Wang, CT Meeting 2021
- Enabling High-Resolution (~2 mm or better) Brain Imaging with a Standard Clinical Whole-Body PET: A Simulation Study, Kai Wang, Jing Wang, Yiping Shao, NSS MIC 2021
- Compton scatter tomography with photon-counting detector: a preliminary study, Kai Wang, Haitao Cheng, Xi Chen, Xuanqin Mou, SPIE Medical Imaging 2018
- Image Restoration for Field Emission X-ray Source Array Based Radiographic Imaging, Kai Wang, Xi Chen, Haitao Cheng, Qinrong Qian, Xuanqin Mou, Fully 3D 2017
- A Stationary CT Scheme Based on Field Emission Flat-panel X-ray Source Array, Haitao Cheng, Kai Wang, Xuanqin Mou, Fully 3D 2017
- Multi-Modality and Multi-View 2D CNN to Predict Locoregional Recurrence in Head & Neck Cancer, Jinkun Guo, Rongfang Wang, Zhiguo Zhou, Kai Wang, Rongbin Xu, Jing Wang, IJCNN 2021
📰 Conference Abstract
- Time Dependence of Coverage of the Prostatic Fossa: Implications for Daily Adaptive Radiotherapy, Kai Wang, Howard Morgan, Yulong Yan, et al., ASTRO 2022 (ePoster)
- Predicting radiotherapy induced anatomic change for head neck cancer patients using vision transformer, Kai Wang, Aixa Andrade, Michael Dohopolski, Jing Wang, AAPM 2022 (ePoster)
- Delta radiomic features predict failure and survival outcomes for surgically resected pancreatic cancer patients treated with neoadjuvant therapy, Kai Wang, Ahmed Elamir, John Karalis, et al., AAPM 2022 (Oral 🎤)
- Attention guided network for vestibular schwannoma growth prediction, Kai Wang, Liyuan Chen, Nicholas George-Jone, Jacob Hunter, Jing Wang, SWAAPM 2021 (Oral 🎤)
- Combining radiomics and convolutional neural network to predict tumor growth of vestibular schwannoma, Kai Wang, Liyuan Chen, Nicholas George-Jone, Jacob Hunter, Jing Wang, AAPM 2020 (Blue Ribbon ePoster)
- Head Neck Squamous Cell Cancer Locoregional Recurrence Prediction Using Delta-radiomics Feature, Kai Wang, Zhiguo Zhou, Jing Wang, SWAAPM 2019 (Oral 🎤)
- A Relational Autoencoder for Retrieving Similar Patients in Radiotherapy Treatment Planning. AAPM Annual Meeting, Kai Wang, Xuejun Gu, Mingli Chen, Weiguo Lu, AAPM 2019 (Oral 🎤)
- Using Radiomics to Improve the Diagnostic Accuracy of Indeterminate Residual Primary Disease on Restaging PET/CT Imaging Following Radiation Therapy for Head and Neck Cancers, Michael Dohopolski, Kai Wang, Howard Morgan, David Sher, Jing Wang, ASTRO 2022
- Using Prediction Uncertainty to Identify Reliable Predictions for a Deep Learning Model that Predicts the Need for Early Feeding Tube Placement, Michael Dohopolski, Kai Wang, Howard Morgan, David Sher, Jing Wang, ASTRO 2022
- Reducing PTV Margins with Daily Adaptive Radiotherapy to the Prostatic Fossa, Howard Morgan, Kai Wang, Yulong Yan, et al., AAPM 2022
- Delta Radiomic Signature from Neoadjuvant Pancreas Cancer Radiation Planning Volume is Superior to Tumor Proper Volume to Predict Surgical Failures, Ahmed Elamir, Kai Wang, John Karalis, et al., AAPM 2022
- Comprehensive Evaluation of a Real-Time 3D MR Imaging Technique Using a Deformation-Driven Deep Convolutional Neural Network (KS-RegNet), Hua-Chieh Shao1, Tian Li, Michael Dohopolski, Jing Wang, Jing Cai, Jun Tan, Kai Wang and You Zhang, AAPM 2022
- Lymph Node Segmentation via Deep Feature Boosting Network in Head and Neck CT Images, Tao Peng, Kai Wang, Hua-Chieh Shao, Michael Dohopolski, You Zhang, Jing Wang,AAPM 2022
- Predicting Feeding Tube Placement in Head and Neck Cancer Patients Receiving Radiation Therapy with Machine Learning, Michael Dohopolski, Kai Wang, Howard Morgan, Liyuan Chen, David Sher, Jing Wang,ASTRO 2021
- Explainable Boosting Machine Model with a Parallel Ensemble Design Predicts Local Failure for Head and Neck Cancer with Clinical, Howard Morgan, Kai Wang, Michael Dohopolski, et al,ASTRO 2021
- Interpretable Machine Learning Model Supported by Parallel Ensemble Learning to Predict Local Recurrence for Patients with Cervical Cancer, Allen Yen, Howard Morgan, Kai Wang, Kevin Albuquerque, Jing Wang, ASTRO 2021
- A Bilateral Neural Network for Loco-Regional Recurrence Prediction in Head and Neck Squamous Cell Cancer Liyuan Chen, Kai Wang, Chenyang Shen, David Sher, Jing Wang,AAPM 2021
- Segmentation Guided Classification Scheme for Lymph Node Malignancy Prediction in Head and Neck Cancer Liyuan Chen, Michael Dohopolski, Kai Wang, Zhiguo Zhou, David Sher, Jing Wang,AAPM 2021
- An automated method for determining vestibular schwannoma size and growth, Nicholas George-Jone, Kai Wang, Jacob Hunter, Jing Wang, Journal of Neurological Surgery Part B: Skull Base, 2020
- Predicting Treatment Outcome After Immunotherapy Based on Delta-Radiomic Model in Metastatic Melanoma, Xi Chen, Zhiguo Zhou, Kai Wang, Zhiguo Zhou, AAPM 2020
- Deciphering Metabolic Features to Target Neuroblastoma Using Machine Learning, Rongfang Wang, Yuanyuan Zhang, Panayotis Pachnis, Hieu Vu, Kai Wang, Ralph DeBerardinis, Jing Wang, AAPM 2020
- Multifaceted Radiomics: towards more reliable radiomics for predicting distant metastasis in head & neck cancer, Zhiguo Zhou, Kai Wang, Hui Liu, David Sher, Jing Wang, AAPM 2019
📰 Patent
- Projection Image Restoration Method for X-ray Source Array Based Radiographic Imaging System, Xuanqin Mou, Kai Wang, Haitao Cheng, Application No. 201611170284.4. China. (Issued)
- Computed Tomography Imaging Methods Based on Array X-ray Source Array and Flat Panel Detector, Xuanqin Mou, Haitao Cheng, Kai Wang, Application No. 201611170283.X, China. (Issued)
- A polygon stationary computed tomography system and imaging methods, Xuanqin Mou, Qinrong Qian, Haitao Cheng, Kai Wang, Application No. 201810167211.2, China. (Issued)
✒️ In Preparation
- Uncertainty estimations methods for a deep learning model to aid in clinical decision-making–a clinician’s perspective, Michael Dohopolski, Kai Wang , Biling Wang, Ti Bai, Dan Nguyen, David Sher, Steve Jiang, Jing Wang, 📃
- Development and Validation of Delta-radiomics based Predictors of Resectability and Prognosis in Pancreatic Ductal Adenocarcinoma after Neoadjuvant, Kai Wang, Ahmed Elamir, John Karalis, et al.
- Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma Chemo-radiotherapy using Planning CT based Radiomics Model, Shanshan Tang, Kai Wang, David Hein, Nina N. Sanford, Jing Wang.