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Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients.

B. Ma, Y. Li, H. Chu, W. Tang, L. la O. Arévalo, J. Guo, P. van Ooijen, S. Both, J. Langendijk, L. van Dijk, and N. Sijtsema. HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 240-254. Springer, (2022)

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Rutger van Dijk

Thomas C. van Dijk

Lisanne Hartmann

Esther Dijk

Atze Dijkstra

Other publications of authors with the same name

Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology.A. Wentzel, G. Canahuate, L. van Dijk, A. Mohamed, C. Fuller, and G. Marai. IEEE VIS (Short Papers), page 281-285. IEEE, (2020)Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.H. Chu, L. la O. Arévalo, W. Tang, B. Ma, Y. Li, A. Biase, S. Both, J. Langendijk, P. van Ooijen, N. Sijtsema and 1 other author(s). HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 114-120. Springer, (2022)Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data.M. Naser, K. Wahid, A. Mohamed, M. Abdelaal, R. He, C. Dede, L. van Dijk, and C. Fuller. HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 287-299. Springer, (2021)Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.A. Biase, L. Ziegfeld, N. Sijtsema, R. Steenbakkers, R. Wijsman, L. van Dijk, J. Langendijk, F. Cnossen, and P. van Ooijen. Comput. Biol. Medicine, (2024)Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.A. Biase, B. Ma, J. Guo, L. van Dijk, J. Langendijk, S. Both, P. van Ooijen, and N. Sijtsema. Comput. Methods Programs Biomed., (2024)Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients.B. Ma, Y. Li, H. Chu, W. Tang, L. la O. Arévalo, J. Guo, P. van Ooijen, S. Both, J. Langendijk, L. van Dijk and 1 other author(s). HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 240-254. Springer, (2022)Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma.K. Wahid, R. He, C. Dede, A. Mohamed, M. Abdelaal, L. van Dijk, C. Fuller, and M. Naser. HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 300-307. Springer, (2021)Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes.Y. Wang, G. Canahuate, L. van Dijk, A. Mohamed, C. Fuller, X. Zhang, and G. Marai. IDEAS, page 273-279. ACM, (2021)Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images.A. Biase, N. Sijtsema, L. van Dijk, J. Langendijk, and P. van Ooijen. CoRR, (2022)TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer.B. Ma, J. Guo, L. van Dijk, P. van Ooijen, S. Both, and N. Sijtsema. MIDL, volume 227 of Proceedings of Machine Learning Research, page 1640-1654. PMLR, (2023)

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